DEV Community: MechCloud

Terraform vs MechCloud: Closing the Infrastructure-as-Code Verbosity Gap for Azure

Shailendra Singh — Sun, 22 Mar 2026 15:04:04 +0000

Provisioning infrastructure should be about the intent, not the ceremony. Yet, in the current landscape of Infrastructure as Code (IaC), we often find ourselves spending more time configuring the tools than describing the actual resources we need.

When you want a simple Microsoft Azure Public IP, the goal is straightforward. But the path to getting there varies wildly depending on your choice of tool. If we look at the difference between traditional stateful tools like Terraform and modern stateless platforms like MechCloud, a significant "verbosity gap" emerges.

Let’s break down why this matters for your daily engineering workflow and why "less" often means "more" when it comes to infrastructure reliability.

The Terraform Experience: The Tax of Setup

In the first template, we see a standard Terraform configuration for a Public IP. Terraform is a powerful industry giant, but that power comes with a mandatory "boilerplate tax."

To stand up a single Standard Public IP, you have to manage:

The Terraform Block: Defining required providers and version constraints.
Provider Configuration: Explicitly telling the tool to use azurerm and often passing feature flags.
Data Source Lookups: Before you can even define the IP, you usually have to write a data block to fetch an existing Resource Group so you can "wire" the location and name properties correctly.
The Resource Block: Finally, you describe the IP itself, but even here, you are manually mapping outputs from the data source to the resource inputs.

By the time you reach line 26, you’ve written a significant amount of "plumbing." This isn't just a syntax preference; it's cognitive load. Every line of boilerplate is a line you have to maintain, a line that can have a typo, and a line that someone else has to read during a PR review.

Terraform Boilerplate

terraform {
  required_providers {
    azurerm = {
      source  = "hashicorp/azurerm"
      version = "~> 3.0"
    }
  }
}

provider "azurerm" {
  features {}
}

# Data source to reference existing resource group rg1
data "azurerm_resource_group" "rg" {
  name = "rg1"
}

resource "azurerm_public_ip" "lb_public_ip" {
  name                = "lb-public-ip"
  resource_group_name = data.azurerm_resource_group.rg.name
  location            = data.azurerm_resource_group.rg.location

  allocation_method   = "Static"
  sku                 = "Standard"
}

The MechCloud Experience: Essential State Only

Now, look at the second template. This is the MechCloud approach. MechCloud is designed as a stateless IaC platform, and that architectural choice changes the authoring experience fundamentally.

In the MechCloud template, the "noise" is stripped away. There are no provider blocks, no version constraints to manually sync, and no complex data lookups just to find a location.

Why it’s shorter:

Contextual Awareness: Through the defaults section, you define the environment context (like the Resource Group) once. The resources underneath inherit that context automatically.
No Plumbing: You don't "wire" the location from a data source. The platform understands the destination context.
Pure Description: You are describing the desired state of the resource, not the logic required to fetch dependencies.

MechCloud Essential State

defaults:
  resource_group: rg1

resources:
  - type: "Microsoft.Network/publicIPAddresses"
    name: "lb-public-ip"
    api_version: "2025-05-01"
    sku:
      # Default value is 'Basic'
      name: "Standard"

Why This Matters for Day-to-Day Engineering

The difference in line count might seem trivial for a single IP address, but infrastructure is rarely just one resource. When scaled across hundreds of resources and multiple environments, the verbosity gap compounds.

1. Faster Authoring and Refactoring

When you don't have to write 15 lines of setup for every 5 lines of resource code, you move faster. Refactoring becomes easier because you aren't untangling a web of data source references and provider configurations.

2. Easier Readability and Reviews

During a code review, you want to see what is changing in your infrastructure. In a verbose Terraform file, the actual change can be buried under boilerplate. In MechCloud, the template is a 1:1 representation of the infrastructure intent. What you see is exactly what you get.

3. Lower Maintenance Overhead

Every time a provider updates or a version constraint changes, stateful tools require manual intervention or configuration updates. Because MechCloud handles the "how" of the deployment behind the scenes, you only focus on the "what."

4. Reduced Cognitive Load

As engineers, we only have so much "contextual bandwidth." If 60% of your IaC file is dedicated to tool-specific logic (state management, provider initialization, variable passing), you have less mental energy to focus on the actual architecture and security of your cloud environment.

Conclusion: Focus on the Resource, Not the Tool

Both Terraform and MechCloud get the job done. They both provision the IP, and they both provide a path to automation.

However, the trade-off is clear: How much ceremony are you willing to tolerate?

If you prefer a system where the syntax remains centered on the resource and its properties—without the overhead of state files and boilerplate—the move toward a stateless, essential-state-focused platform is the logical next step. You can see this simplified approach in practice by authoring desired states in Azure, which highlights how the syntax stays focused on the resource itself.

Infrastructure should be simple. MechCloud makes it so by removing the plumbing and letting you focus on the architecture.

🚀 Taming Your AWS Bills: A Practical Guide to Finding and Eliminating Cloud Wastage

Shailendra Singh — Mon, 16 Mar 2026 03:49:36 +0000

If you are a software developer, DevOps engineer, or a dedicated Cloud Architect, you already know that the modern public cloud is a magnificent but highly dangerous double edged sword. On one hand, you are granted infinite global scalability and the almost magical power to provision immensely complex infrastructure with just a few lines of declarative code or a handful of clicks in a web browser. On the other hand, you are constantly haunted by the dreaded, highly unpredictable end of the month cloud bill. We have all been in that exact, uncomfortable scenario where a developer spins up a massive testing environment for a quick Proof of Concept. The testing finishes successfully, the expensive EC2 instances are terminated to save money, but the attached Elastic IPs and Elastic Block Store volumes are accidentally left behind in the digital void.

Fast forward three to six months, and your organization is suddenly hemorrhaging hundreds or even thousands of dollars for completely invisible resources that are doing absolutely nothing but collecting digital dust. In the cloud native infrastructure world, we refer to these forgotten artifacts as Orphan Resources, and they are consistently ranked by FinOps professionals as one of the primary culprits behind massive cloud wastage. Today, I want to walk you through a highly effective, modern approach to managing Infrastructure as Code, visualizing your deployments in real time, and automatically eliminating this cloud wastage before it drains your engineering budget.

The Anatomy of Cloud Wastage and Why You Pay for What You Do Not Use

Before we jump right into the practical tutorial, it is critically important to fully understand exactly why orphan resources exist in the first place and why Amazon Web Services charges you for them even when you are not actively using them. Understanding the underlying billing mechanics is the absolute first step to mastering FinOps and implementing effective cloud cost optimization strategies across your entire organization.

First, let us look deeply at unattached Elastic Block Store volumes, commonly referred to simply as EBS volumes. When you launch a standard EC2 instance, it typically comes with a root EBS volume to hold the underlying operating system, and you might attach additional secondary data volumes for active databases or large file storage. When that EC2 instance is finally terminated, the root volume is usually deleted by default depending on your specific launch configuration. However, secondary volumes are explicitly designed by AWS to persist independently of the compute instance lifecycle to completely protect your vital business data. This default behavior means they are almost always left behind when servers are destroyed. AWS bills you for the provisioned storage capacity in gigabyte months, as well as provisioned IOPS and throughput, regardless of whether that EBS volume is actually attached to a running server or just floating unattached in your account. A large provisioned gp3 volume left unattached will quietly cost you a small fortune over a single year.

Second, consider the hidden and evolving costs of unassociated Elastic IPs. Public IPv4 addresses are a finite, increasingly scarce, and therefore highly valuable global resource. Historically, AWS only charged you a penalty fee for an Elastic IP if it was not attached to a running instance. However, to reflect the true market scarcity of IPv4, AWS fundamentally changed its billing model in early 2024. Now, AWS charges exactly $0.005 per hour for all public IPv4 addresses, regardless of whether they are attached to a running service or completely idle. Because every single public IP now costs money every hour of the day, holding onto an unassociated Elastic IP is the absolute definition of pure cloud waste. You are paying a premium hourly rate for a networking asset that provides zero value to your architecture because it routes traffic to absolutely nothing.

Finding these hidden resources manually using the standard AWS Management Console is a surprisingly tedious and error prone process. It requires clicking through multiple disconnected billing screens, filtering massive tables by available or unattached resource states, and then manually crossing your fingers that you do not accidentally delete something crucial that just happens to be temporarily offline for routine maintenance. To solve this exact problem, we are going to look at a workflow using MechCloud, a platform that beautifully merges Stateless Infrastructure as Code with powerful visual asset discovery to make cloud management entirely frictionless.

Step 1: Governance First and Configuring Regions and Zones

Security and cost governance in the public cloud does not start with aggressively deleting things. It starts with strictly restricting exactly where resources can be deployed in the first place. If your core customer base is exclusively located in North America, there is rarely a good business or technical reason for a junior developer to have the permissions necessary to spin up expensive GPU instances in the Sydney, Mumbai, or Tokyo AWS regions. Unrestricted region access is a massive vector for both accidental overspending and malicious crypto mining attacks via compromised IAM credentials.

In our specific workflow within the MechCloud portal, the absolute first step is configuring the strictly allowed regions for your AWS account. You navigate to the Manage Cloud Accounts section of the dashboard, select your target AWS cloud account which is a user account named Academy in our demonstration, and open the Configure Regions / Zones administrative panel.

What you will immediately notice is that by default, the platform embraces a strict Zero Trust approach to geographical cloud regions. All Regions and Availability Zones across the entire globe are completely disabled for the account by default. You must explicitly and intentionally choose the specific regions where you want to allow provisioning or where you want to actively discover existing resources. In our specific walkthrough, we scroll down to the US Regions section and select US East Ohio, known programmatically to developers as us-east-2. We click the region block to enable it, which subsequently enables the underlying availability zones within it like us-east-2a, us-east-2b, and us-east-2c. We then carefully save this configuration. This one simple toggle acts as a powerful, account wide guardrail, ensuring that rogue Infrastructure as Code scripts cannot quietly spin up expensive resources in unmonitored corners of your cloud environment.

Step 2: Provisioning Resources and Simulating Messy Habits

Next, we move over to the Stateless IaC module to actually build some functional infrastructure. If you are a DevOps engineer who is used to traditional tools like Terraform or Pulumi, you already intimately know the extreme pain of managing complex state files. You have to configure secure backend S3 buckets, set up specific DynamoDB tables for state locking to prevent concurrent state corruption, and constantly worry about state drift when someone makes a manual console change. Stateless IaC abstractions aim to completely handle all of the state mapping complexity on your behalf, allowing you to focus purely on the declarative, desired state of your infrastructure in a simple, highly readable YAML format.

To simulate a real world, predictably messy development environment, we are going to write a custom YAML configuration that provisions a standard, best practice web tier. We will intentionally inject some incredibly bad practices into this file. We will explicitly add an unattached Elastic IP and an unattached EBS volume to simulate the exact type of cloud waste that typically accumulates over time in enterprise accounts.

Take a close, analytical look at our specific YAML template designed for this walkthrough:

resources:
  - type: aws_ec2_vpc
    name: vpc1
    props:
      cidr_block: "10.0.0.0/16"
    resources:
      - type: aws_ec2_subnet
        name: subnet1
        props:
          cidr_block: "10.0.1.0/24"
          availability_zone: "{{CURRENT_REGION}}a"
        resources:
          - type: aws_ec2_instance
            name: vm1
            props:
              image_id: "{{Image|arm64_ubuntu_24_04}}"
              instance_type: "t4g.small"
              security_group_ids:
                - "ref:vpc1/sg1"
      - type: aws_ec2_security_group
        name: sg1
        props:
          group_name: "mc-sg1"
          group_description: "SG for EC2 instance"
          security_group_ingress:
            - ip_protocol: tcp
              from_port: 22
              to_port: 22
              cidr_ip: "{{CURRENT_IP}}/32"
  - type: aws_ec2_eip
    name: eip1
  - type: aws_ec2_volume
    name: vol1
    props:
      availability_zone: "{{CURRENT_REGION}}a"
      size: 10
      volume_type: "gp3"

Notice the elegant, nested hierarchical structure of this code. The aws_ec2_subnet and aws_ec2_security_group are explicitly nested directly inside the aws_ec2_vpc block, while the aws_ec2_instance is nested safely inside the subnet block itself. This visual nesting makes the architectural relationships immediately obvious to any engineer reading the code. We are also leveraging highly dynamic platform variables to avoid hardcoding fragile values. We use {{CURRENT_REGION}}a to automatically inject our previously approved Ohio availability zone. We use {{Image|arm64_ubuntu_24_04}} to automatically fetch the correct AMI ID for a modern ARM based Ubuntu 24.04 image without having to manually search for it. We also choose the t4g.small instance type powered by AWS Graviton processors for better price performance. Finally, we use the {{CURRENT_IP}}/32 macro in our security group ingress rules to automatically restrict SSH access solely to the precise IP address of the engineer currently executing the code. This is a massive security enhancement over hardcoding open networks.

But here is exactly where we introduce the simulated financial wastage. At the very end of our YAML file, completely outside the VPC hierarchy, we boldly declare an aws_ec2_eip named eip1 with absolutely no instance association properties defined. Directly below that, we declare an aws_ec2_volume named vol1 with a size of 10GB using the highly performant gp3 volume type, completely unattached to any compute resource.

Before we blindly hit the Apply button and send this massive payload to AWS, we generate a comprehensive Plan. This is where the true magic of Shift Left FinOps truly shines. The platform intelligently analyzes the delta between the current empty cloud state and our desired YAML state. The console logs output a detailed execution plan stating that exactly eight resources are to be created, zero are to be recreated, zero are to be updated, and zero are to be deleted.

However, the terminal output is additionally enriched with crucial, immediate financial data that most standard automation tools severely lack. It explicitly shows a calculated Cost Impact of a specific monthly dollar amount. It does not just give a single abstract number. It breaks the cost down granularly. It explicitly shows the ongoing Compute price for our t4g.small instance, the Root disk cost, and it explicitly highlights the precise Storage cost for our orphan gp3 data volume, as well as the idle IP cost for our unassociated Elastic IP. By surfacing these actual dollar costs directly in the planning phase before a single API call is made to provision the infrastructure, developers are empowered to make financially responsible architectural decisions immediately. We confidently click Apply, the logs execute our desired creations sequentially based on the inferred dependencies from our nested code, and our intentionally messy infrastructure is successfully built in the Ohio region.

Step 3: Discovering Assets and Visualizing the Wastage

Writing YAML is highly efficient for automation, but fully understanding the complex web of networking, routing, and resource dependencies through lines of text alone is incredibly difficult for the human brain. This is exactly where visual Asset Discovery becomes an indispensable tool for Cloud Architects and operations teams trying to maintain rigid order in their environments.

We navigate away from the code editor to the Discover Assets tab. We select our Academy team, choose the standard AWS Cloud Provider, pick our specific AWS target account, and target the us-east-2 region that we exclusively enabled in the very first step. We click the Update button to aggressively fetch the live state of the cloud.

Upon successfully fetching the data via the AWS API, the platform dynamically generates a beautiful and highly interactive topology map of our actual running AWS environment. This is not just a flat, boring list of resources. It is a nested, hierarchical representation that perfectly mimics real cloud architecture. The outer bounding box represents the overarching AWS Region. Inside that large box, we immediately see regional resources and specific Availability Zones like us-east-2a and us-east-2b. We can clearly see our newly created VPC bridging across the multiple zones. Inside the VPC block, we see our precise Subnet, and nested safely inside that specific subnet is our running EC2 instance complete with its private IP address, AMI details, and ARM based instance size.

This spatial visual layout makes it incredibly easy to immediately understand the potential blast radius and network flow of your deployment. But the most critical part of this entire visualization lies at the very top of the interactive canvas. Our intentionally unattached Elastic IP and our unattached EBS Volume are rendered in a separate Regional Resources block, but they are rendered with a glaring, completely unmissable visual difference. They both feature a bright, solid RED header.

The platform discovery engine automatically analyzes all resource relationships and attachment states during the live scan. Because it instantly identifies that these specific resources are not attached to any running compute instance or active network interface, they are immediately flagged visually as Cloud Wastage. There is absolutely no need to write complex Python scripts to audit your account, no need to set up expensive custom AWS Config rules, and no need to wait for a shocking billing alert at the end of the month. The cloud waste is identified visually, instantly, and undeniably.

Step 4: The Magic Button and Deleting Orphan Resources

Identifying infrastructure waste is only half of the FinOps battle. Remediating it safely and efficiently is the much harder other half. Traditionally, cleaning up these unused resources requires a DevOps engineer to completely context switch, log into the slow AWS Management Console, carefully cross reference the exact resource IDs of the unused volumes and IPs, heavily double check that they are not meant to be attached to something currently offline, and then manually delete them one by one. It is a terrifying process because simple human error can easily lead to deleting the wrong volume and causing a massive, career ending data outage.

With this modern visual discovery tool, the FinOps remediation process is built directly into the UI workflow to entirely eliminate friction. In the Discover Assets visual view, we simply click the Actions dropdown menu located at the top right of the canvas. From the curated list of automated operational actions, we confidently select Delete Orphan Resources.

A confirmation modal instantly appears, acting as a crucial final safety check. It explicitly lists exactly what is about to be destroyed based on the previous system scan. It tells us it will delete one Unattached Volume named vol1 while providing the exact volume ID for manual verification, and one Unassociated Elastic IP named eip1 while providing the exact IP address. We confidently click the final Delete Orphans button.

Behind the scenes, the platform securely and rapidly executes the precise API calls to AWS. Within seconds, the visual topology map refreshes itself automatically. The red warning boxes representing our expensive waste disappear entirely from the graphical canvas. Our monthly AWS bill is instantly optimized, and our cloud environment is immediately cleaner. This single feature represents a massive, completely unquantifiable quality of life improvement for Site Reliability Engineers and FinOps teams who historically spend hours every single week chasing down cloud leakage and pleading with developers to clean up their messes. When doing the right financial thing is as easy as clicking a single button, your entire engineering team will naturally maintain a leaner cloud environment.

Step 5: Clean Slate and Deprovisioning the Entire Environment

Now that we have successfully cleaned up the accidental waste, let us assume our Proof of Concept project is officially finished. It is time to completely tear down the rest of the underlying infrastructure to absolutely stop the billing meters from running.

Because we are exclusively utilizing a declarative Infrastructure as Code approach, we do not need to endure the incredibly painful process of manually deleting resources in the correct exact order. If you have ever tried to manually delete a VPC, you know the absolute frustration of AWS throwing dependency errors because you forgot to delete a security group, an internet gateway, or a hidden running instance inside a subnet first.

Instead of a manual, error prone teardown, we simply return to our Stateless IaC editor dashboard. We take our previous, lengthy YAML configuration file and replace the entire contents with a completely empty state block represented by an empty array. We simply type resources: [] to declare that our desired state is now a clean slate with zero resources.

Once again, we click the Plan button. The engine performs another delta calculation, this time comparing the active resources in our AWS account against our new, empty desired state. The logs generate a highly satisfying teardown plan:

Plan generated: 0 to create, 0 to recreate, 0 to update, 6 to delete
[DELETE] aws_ec2_instance -> vm1
[DELETE] aws_ec2_security_group -> vpc1/sg1
[DELETE] aws_ec2_subnet -> vpc1/subnet1
[DELETE] aws_ec2_vpc -> vpc1

One of the most psychologically rewarding aspects of this final step is looking at the newly generated FinOps plan. Because we are completely removing all compute and network resources, the platform calculates a beautiful negative cost impact. The logs clearly show a massive negative change percentage and a final Cost Impact in dollars and cents saved per month. Seeing a tangible, negative dollar amount associated directly with your infrastructure teardown is a massive motivational boost for engineering teams. It directly gamifies the concept of cost savings, making developers feel like active, highly appreciated participants in the company's overall financial health.

We click Apply to permanently execute the teardown. The platform inherently understands the complex AWS dependency graph. It natively handles the exact ordering by automatically terminating the EC2 instance first, waiting patiently for it to shut down, then deleting the specific subnet, detaching the Internet Gateway, deleting the custom Security Group, and finally deleting the overarching VPC itself. Within moments, the entire infrastructure stack is cleanly and safely wiped from our AWS account without a single frustrating dependency error.

To absolutely validate this clean state, we perform one final operational step. We navigate back to the Discover Assets visual dashboard and hit the refresh button for the us-east-2 region. The hierarchical topology map vividly updates and is now completely empty. We are left with a perfectly clean slate, with absolutely zero lingering resources, zero orphan volumes, zero unattached IPs, and absolutely zero nasty surprises waiting for us on the next Amazon monthly invoice.

Why This Modern Workflow Matters

The methodology shown in this video represents a fundamental and important shift in how we should approach Cloud Engineering and FinOps.

1. Bridging the Gap Between Code and Visibility

Developers love and live in code (Infrastructure as Code), while Operations teams and Cloud Architects thrive on visibility (Topology graphs and dashboards). Usually, these are entirely separate tools and mindsets. By combining Stateless IaC with immediate visual asset discovery, teams create a powerful, shared language. You write the code, and you immediately see the architecture you have built, including its flaws.

2. True Shift-Left FinOps

Cost optimization is usually a reactive, painful process. A billing alert triggers, a manager gets angry, and an engineer is tasked with the stressful job of figuring out why the bill suddenly spiked. By integrating granular cost estimation directly into the IaC Plan phase, cost management is "shifted left" into the development cycle. The developer sees the price tag before they build the infrastructure, empowering them to make better, more cost effective sizing and architectural choices from the very beginning.

3. Frictionless Waste Management

Orphan resources pile up in cloud accounts because cleaning them up involves significant friction. It requires context switching away from your primary task, tedious manual console navigation, and a constant fear of accidentally breaking a production system. Highlighting orphan resources in bright red and providing a one-click Delete Orphans action removes the friction entirely. When doing the right thing (cleaning up expensive waste) is the easiest thing to do, your team will naturally and proactively maintain a leaner, more cost effective cloud environment.

Conclusion

Managing complex cloud infrastructure does not have to be a black box of unpredictable costs and forgotten, expensive resources. By adopting a modern, integrated approach that utilizes Stateless IaC for predictable and version controlled deployments, combined tightly with visual asset discovery for continuous monitoring and one-click remediation, you can finally regain complete, effortless control over your sprawling AWS environments.

The complete end to end workflow we explored today, from configuring specific regional guardrails and provisioning infrastructure with full cost awareness, to visually identifying red flagged orphan resources and executing targeted or full automated teardowns, is the definitive blueprint for maintaining a healthy, lean, and cost optimized cloud presence.

Stop letting unattached volumes and idle IP addresses quietly drain your hard earned engineering budget. Visualize your complex architecture, automate your tedious cleanup, and make your CFO happy!

What about you? What is the absolute worst case of cloud wastage you have ever personally discovered in your AWS, GCP, or Azure environments? Have you ever stumbled upon a massive unattached EBS volume sitting there quietly billing you for years? Share your absolute best cloud bill horror stories in the comments below! 👇

Move Over Postman: Testing Private APIs with Natural Language

Shailendra Singh — Thu, 12 Mar 2026 18:05:47 +0000

If you are a backend developer, QA engineer, or full-stack wizard, you probably spend a significant chunk of your life staring at API testing tools. For the last decade, the workflow has remained stubbornly static: write the code, spin up the server, open Postman (or Insomnia, or cURL), manually construct the HTTP request, carefully format the JSON payload, inject the authentication tokens, and hit send.

But what if you could just talk to your API? What if you could say, "Get all items," or "Check the health status," and your tooling automatically figures out the correct endpoint, HTTP method, and payload?

Recently, I watched a fascinating demonstration of MechCloud’s REST Agent, a tool that integrates AI directly into the API testing lifecycle. It allows you to test REST APIs-even those running completely offline on your localhost or hidden behind strict corporate firewalls-using pure natural language.

In this post, we are going to dive deep into how MechCloud is rethinking API interaction, why this might be the paradigm shift that finally makes us reconsider our reliance on traditional tools like Postman, and how you can test highly secured private APIs without ever handing your credentials over to a third-party cloud.

The Postman Problem: Why We Need a Paradigm Shift

Before we look at the solution, let’s talk about the elephant in the room: Postman.

Don't get me wrong, Postman is an incredible piece of software that revolutionized how we interact with APIs. But as it has grown from a lightweight Chrome extension into a massive enterprise collaboration suite, several pain points have emerged for the everyday developer:

1. The Setup Tax

To test a new endpoint in Postman, you have to do the heavy lifting. You need to read the Swagger/OpenAPI documentation, copy the endpoint URL, select the correct HTTP method (GET, POST, PUT, DELETE), map out the required headers, and manually format the JSON body. If you misplace a single comma or use a string instead of an integer, the request fails. You aren't just testing the API; you are manually translating human intent into machine-readable HTTP syntax.

2. The Maintenance Nightmare

As your API evolves, your Postman collections decay. Endpoints change, payload schemas update, and suddenly your perfectly crafted collection is throwing 400 Bad Request errors. Keeping OpenAPI specs and Postman collections in perfect sync is a notorious headache, often requiring third-party sync tools or tedious manual updates.

3. The Security and Credential Dilemma

This is perhaps the biggest issue. To test a secured API in Postman, you have to paste your authentication tokens, API keys, or OAuth credentials into the tool. If you are using Postman Workspaces to collaborate with your team, those tokens are often synced to the cloud. Over the last few years, the security implications of storing sensitive environment variables and production credentials in cloud-synced testing tools have become a massive red flag for enterprise security teams.

We need a better way. We need tooling that understands standard OpenAPI specifications natively, builds the requests for us, and, most importantly, keeps our private APIs and credentials exactly that-private.

Enter MechCloud: Conversational API Testing

MechCloud approaches API testing from an entirely different angle. Instead of forcing you to build HTTP requests visually, MechCloud utilizes an AI-driven REST Agent. You feed the platform your OpenAPI (Swagger) specification, and the LLM under the hood learns exactly how your API is structured.

Once the Agent understands your API's schema, you stop writing HTTP requests. You start writing English.

As demonstrated in the following MechCloud workflow video, the process is incredibly streamlined.

Here is what the experience actually looks like:

Step 1: Registering the API via OpenAPI Spec

Instead of manually creating folders and routes, the user simply navigates to the REST APIs dashboard in MechCloud. They create a new API entity (e.g., test-api) and paste the URL to their OpenAPI JSON file (in the video, this was https://localhost:8443/test-api/openapi.json).

Boom. MechCloud immediately understands the entire architecture of the API-every route, every required parameter, and every response schema.

Step 2: Creating a REST Account

APIs run in different environments-local, staging, production. MechCloud handles this by abstracting the environment into "REST Accounts." The user maps the newly registered API to a Base URL (https://localhost:8443/test-api).

Step 3: Chatting with the REST Agent

This is where the magic happens. Navigating to AI Agents -> REST Agent, the user is presented with a clean, chat-like interface.

Instead of opening a new tab, selecting GET, and typing out /health, the user simply types:
"check health"

The REST Agent processes this natural language intent, matches it against the ingested OpenAPI spec, dynamically generates the correct API call, executes it, and returns the real-time JSON response:

{
  "message": "API is healthy",
  "timestamp": "2026-03-12T17:45:42.669139"
}

The user then types:
"get all items"
Instantly, the proxy fetches the /items endpoint and returns a list containing a Laptop, Mouse, and Keyboard.

Finally, they ask:
"get item with id 1"
The LLM understands that "id 1" is a path parameter. It automatically constructs a GET /items/1 request and returns just the laptop data.

No manual URL crafting. No string interpolation. Just pure, intent-driven testing.

The Masterstroke: Testing Private & Secured APIs Safely

You might be looking at the video and thinking: "Wait a minute. The Base URL is localhost:8443. MechCloud is a web-based SaaS. How on earth is a cloud AI agent reaching a local development server?"

This brings us to the most powerful and significantly engineered feature of MechCloud’s architecture: The MechCloud HTTPS Proxy.

When you are developing an API locally, or when your API is deployed in a private corporate Intranet (VPC), it is not accessible from the public internet. Furthermore, your API likely requires Bearer tokens, API keys, or basic authentication.

If you use a traditional cloud-based AI tool, you have two terrible options:

Expose your private API to the public internet (a massive security risk).
Store your highly sensitive authentication credentials directly inside the cloud platform.

MechCloud solves this elegantly using an open-source HTTP proxy available at github.com/mechcloud/mechcloud-flask-proxy.

How the Proxy Works

Instead of the MechCloud cloud servers making the HTTP request to your API, the MechCloud REST Agent only generates the instructions for the request. It says, "Hey, the user wants to GET /items/1."

You run the lightweight mechcloud-flask-proxy on your local machine or within your private network. The MechCloud UI communicates with this local proxy. The proxy is the entity that actually fires the HTTP request to localhost:8443 or your internal IP addresses.

Zero-Credential Cloud Storage

Because the execution happens locally via the proxy, you never have to store your API credentials in MechCloud.

You can configure the local mechcloud-flask-proxy to automatically inject your OAuth tokens, private API keys, or custom headers into the outgoing requests.

MechCloud’s LLM translates the natural language into an HTTP method and payload.
MechCloud sends this abstract request to your local proxy.
Your local proxy safely injects the Authorization: Bearer <token> header.
Your local proxy hits the secured local or private API.
The response is piped back to your UI.

This is a game-changer compared to Postman. You can test highly secured, strictly private APIs using state-of-the-art AI, while keeping zero trust. Your credentials never leave your machine. Your API never needs to be exposed to the public web.

Hidden Gems: Significant Insights from the Video

While watching the MechCloud REST Agent in action, I noticed a few brilliant UI and UX decisions that deserve a shoutout:

1. Granular AI Token & Cost Tracking

One of the biggest anxieties developers have with AI tools is the invisible cost. You write a prompt, but you have no idea how many tokens were consumed to process it.

In the MechCloud video, every single time a natural language query is executed, the UI displays a highly transparent metrics bar right above the JSON output:

Input: 3,629
Output: 36
Total Cost: $0.000402

This is incredibly significant. MechCloud isn't hiding the LLM mechanics. By showing the exact token count and the micro-cent cost of the API call, developers can confidently use the tool without fear of accidental billing spikes. It also proves that the system is efficiently caching or utilizing context to keep costs incredibly low.

2. Immediate OpenAPI Syncing

Throughout the video, the user has a Swagger Editor open in another tab. This highlights the philosophy of MechCloud: The OpenAPI spec is the single source of truth.

In Postman, if you change an endpoint, you have to go manually update your saved request. In MechCloud, because the AI relies entirely on the OpenAPI spec to understand the API, your testing capabilities are automatically updated the second your spec updates. There is no "collection" to maintain. The natural language prompt "Create a new user" will dynamically adapt if your schema changes from username to email_address overnight.

3. Contextual Data Extraction

Though the video demonstrated straightforward GET requests, the implication of the LLM mapping natural language to API parameters is profound. When the user said "get item with id 1", the LLM mapped the numeral 1 to the {item_id} path parameter defined in the OpenAPI spec.

Imagine dealing with a massive POST request. Instead of writing 50 lines of JSON, you could simply type: "Create a new laptop item priced at $1500 with a description of 'Gaming Rig'". The REST Agent will parse the required fields from the spec, map your English words to the JSON keys, and construct the payload for you.

Why This Changes the Developer Workflow

The introduction of natural language via MechCloud’s REST Agent isn't just a neat party trick; it fundamentally lowers the barrier to entry for interacting with APIs.

For Backend Developers: You spend less time writing boilerplate JSON requests for manual testing and more time actually writing business logic.
For QA Automation Engineers: You can quickly explore edge cases by simply asking the API questions, rather than maintaining hundreds of fragile Postman tabs.
For Product Managers and Frontend Devs: You don't need to know the intricacies of the backend architecture. If a PM wants to see what data the /users endpoint returns, they don't need to learn how to configure OAuth in Postman. They run the local proxy, open MechCloud, and type, "Show me the first 5 users."

Final Thoughts

We are moving into an era where developers shouldn't have to act like compilers, manually translating human intent into rigid syntax just to test a server.

MechCloud’s approach to API interaction bridges the gap between human thought and machine execution perfectly. By combining the conversational power of LLMs with the strict structural definitions of OpenAPI, and wrapping it all in a highly secure, privacy-first local proxy architecture (mechcloud-flask-proxy), it provides a glimpse into the future of software development.

If you are tired of syncing Postman collections, terrified of leaking tokens in cloud workspaces, and want to test your local, private APIs at the speed of thought, it is highly worth giving MechCloud a spin.

Have you tried using AI agents for your API testing workflows yet? Drop your thoughts in the comments below!

Automating AWS Credits with MechCloud: A Beginner Guide

Shailendra Singh — Wed, 11 Mar 2026 16:23:00 +0000

When you sign up for a new Amazon Web Services account you are usually greeted with a generous free plan. This setup typically comes with an initial $100 in credits to help you get started on your cloud journey. This is a fantastic way to learn without risking your own money. But what many beginners do not realize is that you can earn an additional $100 in AWS credits through a special program called Explore AWS. This program is designed to reward you for exploring different parts of the platform.

The Explore AWS program asks you to perform five specific activities. Each activity you complete unlocks a $20 credit reward. The five activities are launching an instance using EC2 using a foundation model in Amazon Bedrock setting up a cost budget using AWS Budgets creating a web app using AWS Lambda and creating an Aurora or RDS database. By the time you finish you will have a total of $200 in credits. This gives you a massive runway to learn and build real projects.

As someone who uses MechCloud I have found that automating this process is far better than clicking around the AWS console manually. The AWS console is huge and it is very easy to leave resources running by accident. While it is possible to use the AWS Command Line Interface to do all these things the biggest advantage of using MechCloud is its real-time pricing feature. For any person new to AWS seeing the exact cost of your infrastructure before you create it is critical. It ensures you do not accidentally burn through your hard earned credits in a few days.

In this post we are going to look at how to automate the two most infrastructure heavy activities on that list. We will launch an EC2 instance and an RDS database. We will write a single template to handle the networking the security and the servers.

The Challenge with Traditional Automation

When most people talk about Infrastructure as Code they immediately mention Terraform. I tried learning Terraform when I first started but it felt like hitting a brick wall. Terraform requires you to manage something called a state file. This file keeps track of what you have built. If you lose that file or if it gets corrupted your automation breaks. You also have to write a lot of boilerplate code just to get the tool to talk to AWS.

MechCloud takes a different approach called Stateless IaC. There is no state file to manage. The platform talks directly to your live AWS account to see what exists. This takes away a huge amount of stress for a beginner. You just write your template and the platform figures out the rest.

Activity 1: Launching Your EC2 Instance

The first activity earns you a $20 credit by launching an EC2 instance. An EC2 instance is simply a virtual computer running in an AWS data center. Before we can launch a computer we need a place to put it. We need a virtual network.

In AWS a virtual network is called a VPC or Virtual Private Cloud. Think of the VPC as a large piece of fenced off land in the cloud. Inside that land we create a Subnet. A subnet is like a specific building on your land. Finally we place our EC2 instance inside that building.

We also need to secure our computer. We do this using a Security Group which acts like a virtual bouncer at the door. We only want to allow access from our own personal computer. If we open it to the world anyone could try to log in. MechCloud makes this incredibly easy with a special placeholder called {{CURRENT_IP}}. When you deploy the template the platform automatically finds your actual IP address and locks the server down so only you can access it.

We also use the {{CURRENT_REGION}} placeholder. AWS operates in many different regions around the world like Ohio or Mumbai or London. By using this placeholder our template becomes completely portable. It will work perfectly no matter which region you select in your account.

Here is the part of the MechCloud template that creates our virtual network our security group and our virtual machine. Notice how we nest the resources inside each other.

resources:
  - type: aws_ec2_vpc
    name: credits_vpc
    props:
      cidr_block: "10.0.0.0/16"
    resources:
      - type: aws_ec2_subnet
        name: subnet_vm
        props:
          cidr_block: "10.0.1.0/24"
          availability_zone: "{{CURRENT_REGION}}a"
        resources:
          - type: aws_ec2_instance
            name: credits_ec2
            props:
              image_id: "{{Image|arm64_ubuntu_24_04}}"
              instance_type: "t4g.small"
              security_group_ids:
                - "ref:credits_vpc/ec2_sg"
      - type: aws_ec2_security_group
        name: ec2_sg
        props:
          group_name: "aws-credits-ec2-sg"
          group_description: "SG for credits EC2 instance"
          security_group_ingress:
            - ip_protocol: tcp
              from_port: 22
              to_port: 22
              cidr_ipv4: "{{CURRENT_IP}}/32"

We chose the t4g.small instance type because it uses an AWS Graviton processor. These processors are highly efficient and cost much less to run than standard processors. This helps stretch your credits even further.

Before deploying anything MechCloud provides a plan output with real-time pricing. This is what makes the platform so powerful for beginners. You can see exactly what this setup will cost per month.

credits_vpc (action: create)
-- subnet_vm (action: create)
---- credits_ec2 (action: create, monthly: $9.76, change: +100%)
       => Compute (price: $0.0168/Hrs, hours: 543, monthly: $9.12, spot-price: $0.0043/Hrs, spot-monthly: $2.33)
       => Boot disk (/dev/sda1, 8GB gp3) (monthly: $0.64)
         => Storage cost (gp3) (price: $0.08/GB-Mo, quantity: 8, monthly: $0.64)
         => IOPS (monthly: $0.00)
           => Tier 1 (First 3000 IOPS-Mo - price: $0.00/IOPS-Mo, quantity: 3000, monthly: $0.00)
         => Throughput (monthly: $0.00)
           => Tier 1 (First 125 MiBps-mo - price: $0.00/MiBps-mo, quantity: 125, monthly: $0.00)
-- ec2_sg (action: create)

As you can see launching this server costs less than $10 per month. Since the activity gives you a $20 credit you are instantly ahead.

Activity 2: Creating an RDS Database

The next major activity is creating a managed relational database using Amazon RDS. This earns you another $20 credit. Databases are the core of almost any real world application. But setting them up correctly requires a bit of networking knowledge.

AWS requires managed databases to be highly available. This means you must provide at least two subnets in two different Availability Zones. Think of an availability zone as an isolated data center in a specific city. If one data center has a power outage your database can failover to the other data center automatically.

We need to create two new subnets for our database. We also need a new Security Group for the database. We do not want anyone on the internet to reach our database directly. We only want our EC2 instance to be able to talk to it. We can do this easily in MechCloud by using the ref keyword to reference our EC2 security group inside our RDS security group rules.

Here is the template section that sets up the database networking the security rules and the actual MySQL database instance.

      - type: aws_ec2_subnet
        name: subnet_db_1
        props:
          cidr_block: "10.0.2.0/24"
          availability_zone: "{{CURRENT_REGION}}a"
      - type: aws_ec2_subnet
        name: subnet_db_2
        props:
          cidr_block: "10.0.3.0/24"
          availability_zone: "{{CURRENT_REGION}}b"
      - type: aws_ec2_security_group
        name: rds_sg
        props:
          group_name: "aws-credits-rds-sg"
          group_description: "SG for credits RDS instance"
          security_group_ingress:
            - ip_protocol: tcp
              from_port: 3306
              to_port: 3306
              source_security_group_id: "ref:credits_vpc/ec2_sg"
  - type: aws_rds_db_subnet_group
    name: credits_db_subnet_group
    props:
      db_subnet_group_name: "aws-credits-db-subnet-group"
      db_subnet_group_description: "Subnet group for credits RDS"
      subnet_ids:
        - "ref:credits_vpc/subnet_db_1"
        - "ref:credits_vpc/subnet_db_2"
  - type: aws_rds_db_instance
    name: credits_db
    props:
      db_instance_identifier: "aws-credits-db"
      engine: "mysql"
      engine_version: "8.0"
      db_instance_class: "db.t3.micro"
      allocated_storage: "20"
      db_name: "credits"
      master_username: "admin"
      master_user_password: "Kx7#mP2$vL9qN4wR"
      db_subnet_group_name: "ref:credits_db_subnet_group"
      vpc_security_groups:
        - "ref:credits_vpc/rds_sg"

Once again MechCloud provides a clear pricing output for the database resources before you deploy them. Databases are usually the most expensive part of a cloud setup so seeing this price up front is incredibly reassuring.

-- subnet_db_1 (action: create)
-- subnet_db_2 (action: create)
-- rds_sg (action: create)
credits_db_subnet_group (action: create)
credits_db (action: create, monthly: $14.95, change: +100%)
  => Compute (price: $0.017/Hrs, monthly: $12.65)
  => Storage (price: $0.115/GB-Mo, quantity: 20, monthly: $2.30)

The database costs around $15 per month. Combined with the EC2 instance your entire cloud environment costs roughly $25 per month. With your total $200 in credits you can run this fully functional secure application architecture for eight entire months without paying a single cent out of pocket.

Why This Approach is Better Than Terraform

As a user who has wrestled with different tools I can confidently say this method is far superior for anyone starting out. Here is a breakdown of why I prefer this platform over traditional tools like Terraform.

Total Freedom from State Files
The biggest relief is that MechCloud is stateless. There is no local file or remote bucket I have to manage just to keep track of my infrastructure. If my laptop crashes my cloud setup does not break. The platform looks directly at AWS to see what is running. There is no locking migrating or recovering to worry about ever.

Zero Infrastructure Scaffolding
When I open a blank file I just start typing the resources I want. I do not have to write a block of code to configure the provider or set up a backend. There is no module wiring or complicated data lookups. The template only contains the actual architecture I am trying to build.

Logical Infrastructure Hierarchy
Most tools make you list your resources flatly. MechCloud lets me declare resources inside their logical parents. I put the Subnet inside the VPC and the EC2 instance inside the Subnet. This nesting makes the relationships instantly obvious to anyone reading the file. It removes a massive amount of confusing dependency wiring.

No Parent ID Plumbing
Because of that hierarchical structure child resources automatically inherit the context of their parents. I never have to write code to extract a VPC ID and pass it to a Subnet or take a Subnet ID and pass it to a virtual machine. It all happens automatically behind the scenes.

Simple Cross Resource References
Sometimes I need to link things that are not nested like attaching my Security Group to my Database. Instead of managing complex variables or outputs I just use the ref keyword. Writing ref:credits_vpc/ec2_sg is simple direct and easy to read.

No AMI ID Management
Finding the correct machine image ID in AWS is tedious because it changes depending on your region. MechCloud uses Image Aliases like {{Image|arm64_ubuntu_24_04}}. These aliases resolve automatically in the background so my template works instantly whether I deploy it in Tokyo or Ireland. I never have to track region specific IDs again.

Real Time Pricing During Authoring
This is the ultimate feature for any beginner. MechCloud shows me the exact cost of the infrastructure while I am authoring the template. I do not have to wait for a surprise bill at the end of the month. I can plan my credit usage with absolute precision.

The Complete Automation Template

To bring it all together here is the complete template. You can copy this single file to set up a secure virtual network a cost optimized virtual machine and a highly available private database. This setup completes the two largest activities for the Explore AWS program and ensures you have a solid foundation for your additional $100 in credits.

resources:
  - type: aws_ec2_vpc
    name: credits_vpc
    props:
      cidr_block: "10.0.0.0/16"
    resources:
      - type: aws_ec2_subnet
        name: subnet_vm
        props:
          cidr_block: "10.0.1.0/24"
          availability_zone: "{{CURRENT_REGION}}a"
        resources:
          - type: aws_ec2_instance
            name: credits_ec2
            props:
              image_id: "{{Image|arm64_ubuntu_24_04}}"
              instance_type: "t4g.small"
              security_group_ids:
                - "ref:credits_vpc/ec2_sg"
      - type: aws_ec2_subnet
        name: subnet_db_1
        props:
          cidr_block: "10.0.2.0/24"
          availability_zone: "{{CURRENT_REGION}}a"
      - type: aws_ec2_subnet
        name: subnet_db_2
        props:
          cidr_block: "10.0.3.0/24"
          availability_zone: "{{CURRENT_REGION}}b"
      - type: aws_ec2_security_group
        name: ec2_sg
        props:
          group_name: "aws-credits-ec2-sg"
          group_description: "SG for credits EC2 instance"
          security_group_ingress:
            - ip_protocol: tcp
              from_port: 22
              to_port: 22
              cidr_ipv4: "{{CURRENT_IP}}/32"
      - type: aws_ec2_security_group
        name: rds_sg
        props:
          group_name: "aws-credits-rds-sg"
          group_description: "SG for credits RDS instance"
          security_group_ingress:
            - ip_protocol: tcp
              from_port: 3306
              to_port: 3306
              source_security_group_id: "ref:credits_vpc/ec2_sg"
  - type: aws_rds_db_subnet_group
    name: credits_db_subnet_group
    props:
      db_subnet_group_name: "aws-credits-db-subnet-group"
      db_subnet_group_description: "Subnet group for credits RDS"
      subnet_ids:
        - "ref:credits_vpc/subnet_db_1"
        - "ref:credits_vpc/subnet_db_2"
  - type: aws_rds_db_instance
    name: credits_db
    props:
      db_instance_identifier: "aws-credits-db"
      engine: "mysql"
      engine_version: "8.0"
      db_instance_class: "db.t3.micro"
      allocated_storage: "20"
      db_name: "credits"
      master_username: "admin"
      master_user_password: "Kx7#mP2$vL9qN4wR"
      db_subnet_group_name: "ref:credits_db_subnet_group"
      vpc_security_groups:
        - "ref:credits_vpc/rds_sg"

By leveraging this modern approach to infrastructure you protect your budget and set yourself up for long term success in the cloud. You spend less time fighting with configuration files and more time actually learning how to build powerful applications.

Going Multi-Cloud with GCP

While this guide focused on maximizing your AWS credits, the power of Stateless IaC is not limited to just one provider. If you are interested in seeing how these same hierarchical concepts and real-time pricing benefits apply to Google Cloud Platform (GCP), check out our video demo. We walk through deploying resources on GCP using the same intuitive approach, proving that you don't need to learn a new state management workflow just to try a different cloud.

Taming the Multi-Cloud Identity Beast: Why Unified SSO is Essential and How MechCloud Simplifies It

Shailendra Singh — Mon, 09 Mar 2026 18:32:46 +0000

The modern engineering landscape is increasingly multi-cloud. Whether driven by a desire for redundancy, specialized services, or geographic reach, many organizations find themselves managing resources across AWS, Azure, and Google Cloud Platform (GCP) simultaneously. On paper, this strategy is the pinnacle of resilience. In practice, it can lead to a significant administrative surface area, particularly when it comes to Identity and Access Management (IAM).

If you have ever tried to set up a unified Single Sign-On (SSO) experience across all three major providers, you know the effort involved. You are not just configuring one system; you are reconciling three fundamentally different philosophies of security. In this post, we will explore why native cloud SSO involves such high levels of complexity and how you can use MechCloud and oauth2-proxy to create a seamless, secure, and developer-friendly login experience in minutes.

Streamlining the Multi-Cloud Experience

AWS, Azure, and GCP offer incredibly sophisticated SSO capabilities. AWS has IAM Identity Center, GCP has Cloud Identity, and Microsoft has Azure Entra ID. Each of these is a robust, enterprise-grade solution designed to provide deep, granular control within their respective ecosystems. However, when an organization scales across all three, the sheer depth of these individual platforms can create a management challenge for engineering teams to navigate.

The AWS IAM Identity Center Architecture

Setting up AWS IAM Identity Center (formerly AWS SSO) is a comprehensive process that reflects the platform's focus on security at scale. First, you must manage AWS Organizations, which involves coordinating a management account. Then, you define permission sets. These sets are highly detailed templates that allow for precise access control, but they must be mapped to specific users or groups across every account in your organization. If you are managing a large-scale environment with dozens or hundreds of accounts, this mapping process requires careful planning and execution.

Furthermore, if your primary identity source is Azure AD or Okta, you engage in a detailed SAML 2.0 integration. This involves managing XML metadata exchanges, certificate rotations, and SCIM provisioning. These steps ensure that identity lifecycle management is consistent, but they also mean your DevOps team is responsible for maintaining a complex chain of configurations to ensure that access remains both secure and up to date.

Navigating Google Cloud Identity

Google Cloud Platform (GCP) provides powerful identity features through Google Workspace and Cloud Identity. For organizations rooted in the Microsoft ecosystem, integrating these identities into GCP often involves deploying Google Cloud Directory Sync (GCDS). This tool is essential for keeping directories in sync, but it represents an additional component within your infrastructure that requires ongoing management and monitoring.

Google's modern Workload Identity Federation is a best-in-class standard for secure, keyless access. However, implementing it correctly requires a deep understanding of workload identity pools and providers. Crafting the specific IAM policies needed to allow external identities to securely interact with GCP resources is a sophisticated task. For teams focused on rapid application delivery, navigating these advanced security configurations can become a time-intensive part of the deployment cycle.

Enterprise Identity with Azure Entra ID

Azure Entra ID is the foundation of identity for a vast number of enterprises. While it provides an excellent source of truth, extending that identity to gate access across a multi-cloud estate introduces layers of administrative detail. This involves managing Enterprise Applications and App Registrations, each with its own set of client secrets, certificates, and API permissions.

The result is that DevOps engineers often find themselves acting as specialized Identity Architects. They spend a significant portion of their bandwidth syncing groups, rotating keys, and ensuring consistent policies across multiple portals. This level of detail is a testament to the power of these platforms, but it can also lead to fragmented workflows that challenge even the most experienced teams.

Enter MechCloud: The Orchestration Layer

MechCloud was built to complement these powerful cloud ecosystems. Instead of replacing the identity systems you already trust, it acts as a unified orchestration layer that simplifies how you interact with them. As a third-party platform designed to work alongside these major providers, our goal is to help you get the most out of your multi-cloud strategy by reducing the administrative overhead associated with identity.

The core philosophy of MechCloud is to decouple authentication (who you are) from infrastructure management (what you are doing). To achieve this, MechCloud leverages the OpenID Connect (OIDC) standard, allowing you to use your existing identity provider as the single source of truth for your entire multi-cloud estate.

The Technical Bridge: oauth2-proxy

As shown in our architectural diagram, the bridge between your users and MechCloud is a lightweight tool called oauth2-proxy.

By deploying oauth2-proxy within your network, you ensure that authentication remains under your control. The browser communicates with MechCloud, which in turn delegates the identity verification to the oauth2-proxy sitting in your environment.

It is critical to note that your oauth2-proxy instance must be exposed to the internet. This is a requirement because the MechCloud backend needs to interact with the proxy to validate session cookies. This interaction ensures that every request is coming from a legitimately authenticated user and allows MechCloud to provide a seamless management experience while your OIDC provider remains the ultimate gatekeeper.

From a privacy and security perspective, MechCloud only sees the session cookies generated by your oauth2-proxy and nothing more. Your user passwords and primary OIDC credentials never touch the MechCloud backend directly during the login process.

Getting Started: Tenant Configuration

To ensure a secure and properly architected foundation, MechCloud handles the initial tenant creation through its support team. Organizations do not need to navigate a complex self-service portal for initial setup.

To get started, an organization simply needs to share their intended tenant administrator email and the URL where their oauth2-proxy will be hosted. Once these details are ready, reach out to the MechCloud support team at support@mechcloud.io. Our team will create a unique tenant mapping for you (e.g., tenant1). While this mapping defines your environment internally, you can access the portal via your custom domain.

If you wish to use a custom domain, the support team will configure the backend to recognize it. This high-touch approach guarantees that your multi-cloud management environment is solid and secure from day one.

Step-by-Step: Setting Up the Identity Gateway

Suppose your company uses the domain cloudops.io and the support team has assigned you the mapping tenant1. You want your team to access MechCloud at mechcloud.cloudops.io. Here is how you set up a secure, containerized SSO flow.

1. Register an Application in Your OIDC Provider

First, you need to register an application in your provider (e.g., Azure Entra ID or Okta). If using Azure Entra ID:

Go to the Azure Portal and navigate to Microsoft Entra ID > App registrations > New registration.
Name it "MechCloud SSO".
For the Redirect URI, select "Web" and enter: https://mechcloud.cloudops.io/oauth2/callback
Note your Application (client) ID and Directory (tenant) ID.
Create a new Client Secret and save the value securely.

2. Configure DNS Settings for Your Custom Domain

To enable access via mechcloud.cloudops.io, you must configure your DNS server. This step is essential for routing traffic correctly between your users and the MechCloud backend.

CNAME Record for Portal: Create a CNAME record for mechcloud.cloudops.io pointing to the global MechCloud portal address: portal.mechcloud.io. This ensures that users visiting your custom domain are directed to the MechCloud management layer, which then identifies your specific tenant based on the incoming hostname.

Ensuring this DNS record is properly propagated allows MechCloud to initiate the session validation handshake with your local proxy.

3. Prepare the oauth2-proxy Docker Environment

Below is a production-ready docker-compose.yml snippet. This configuration ensures that authentication headers are correctly set and that the proxy can communicate with the necessary identity endpoints. Ensure all URLs are updated to match your domain.

services:
  mechcloud-oauth2-proxy:
    image: quay.io/oauth2-proxy/oauth2-proxy:latest
    container_name: oauth2-proxy

    command:
      - --email-domain=*
      - --cookie-domain=.cloudops.io

    environment:
      OAUTH2_PROXY_HTTP_ADDRESS: "0.0.0.0:5000"
      OAUTH2_PROXY_SET_XAUTHREQUEST: "true"
      OAUTH2_PROXY_PROVIDER: "oidc"
      OAUTH2_PROXY_DISPLAY_NAME: "Customer OIDC"
      OAUTH2_PROXY_CLIENT_ID: "YOUR_CLIENT_ID"
      OAUTH2_PROXY_CLIENT_SECRET: "YOUR_CLIENT_SECRET"
      OAUTH2_PROXY_COOKIE_SECRET: "YOUR_SECURE_COOKIE_SECRET"
      OAUTH2_PROXY_REDIRECT_URL: "[https://mechcloud.cloudops.io/oauth2/callback](https://mechcloud.cloudops.io/oauth2/callback)"
      OAUTH2_PROXY_OIDC_ISSUER_URL: "[https://id.cloudops.io/](https://id.cloudops.io/)"
      OAUTH2_PROXY_OIDC_EXTRA_AUDIENCES: "[https://id.cloudops.io/userinfo,https://mechcloud.cloudops.io/](https://id.cloudops.io/userinfo,https://mechcloud.cloudops.io/)"
      OAUTH2_PROXY_SKIP_JWT_BEARER_TOKENS: "true"
      OAUTH2_PROXY_SET_AUTHORIZATION_HEADER: "true"

    restart: always
    network_mode: host

Once this container is running and exposed to the internet, the MechCloud support team can finalize your tenant configuration.

Credential-less Onboarding: The Power of Identity Federation

The core strength of the MechCloud platform is its security posture. Traditionally, onboarding a cloud account into a management tool meant creating and storing long-lived secrets like AWS Access Keys or GCP Service Account keys. These secrets represent a major security liability if they are ever leaked or mismanaged.

MechCloud eliminates this risk by leveraging identity federation across all major cloud providers. This ensures that you never have to share or store sensitive secrets on the MechCloud platform.

Establishing Trust with Your OIDC Provider

Crucially, MechCloud does not require your cloud accounts to trust our platform directly. Instead, you establish a trust relationship between your cloud provider (AWS, Azure, or GCP) and your own OIDC provider (e.g., Azure Entra ID or Okta).

AWS with IAM Roles: You create an IAM role in your AWS account that trusts your OIDC provider as an Identity Provider (IdP). When a user needs to perform an action, MechCloud leverages the OIDC token issued by your provider to exchange it for short-lived AWS session credentials.
GCP via Workload Identity Federation: You configure a Workload Identity Pool in GCP that trusts your OIDC provider. MechCloud uses your provider tokens to securely impersonate a GCP service account without ever needing a Service Account JSON key.
Azure Federated Credentials: Similarly, for Azure, you set up federated identity credentials for your Service Principal that trust your OIDC provider.

By establishing trust directly with your OIDC provider, MechCloud functions as a stateless orchestration layer. We store no credentials, and we never hold a permanent trust relationship with your cloud accounts. You retain full control over the trust relationship within your own identity system.

The Operational Advantage

This integrated approach offers profound benefits that enhance the native capabilities of your cloud providers:

Unified Offboarding: When a developer leaves your organization, you disable their account in your OIDC provider. This immediately breaks the oauth2-proxy chain, ensuring that access to the MechCloud management layer is severed. This centralizes control and reduces the risk of maintaining active access across disparate systems.
Cohesive Security Policies: Instead of managing different MFA requirements and session policies for three different clouds, you can enforce your high-level corporate security standards in your OIDC provider once. This ensures that your entire multi-cloud infrastructure is protected by the same rigorous standards you apply to your primary directory.
Enhanced Developer Workflow: Developers thrive when they can focus on solving problems rather than navigating consoles. MechCloud provides a consistent interface for services across AWS, Azure, and GCP. Whether they are provisioning a VM in AWS or a database in Azure, the experience is unified, allowing your team to move faster and with greater confidence.

Conclusion

A multi-cloud strategy is a powerful way to build resilient global infrastructure. While the identity systems provided by AWS, Azure, and GCP are robust and feature-rich, the sheer variety of their configurations can lead to complexity. By using MechCloud as your orchestration layer and oauth2-proxy as your identity gatekeeper, you can simplify these workflows and focus on what matters most: building and scaling your applications.

Reclaim your engineering bandwidth and secure your multi-cloud estate with our credential-less onboarding architecture. Reach out to support@mechcloud.io today to set up your tenant and experience a more streamlined approach to cloud management.

Are you managing identities across multiple cloud providers? We would love to hear about your workflow and how you handle the complexity!

Managing Microsoft Azure with Stateless IaC and Real-time Pricing 🚀

Shailendra Singh — Mon, 02 Mar 2026 08:30:34 +0000

We are thrilled to share that Microsoft Azure has officially joined the MechCloud ecosystem. Following our recent launch of Google Cloud support we are continuing our mission to provide a unified and secure management experience across all major cloud providers. This update brings our unique stateless approach to Azure users who are looking for a more streamlined way to manage their infrastructure.

Azure users can now leverage the core strengths of MechCloud including Stateless IaC and real-time cost visibility and industry leading security to manage their cloud footprint without the overhead of traditional DevOps tools.

Why Stateless IaC Matters for Azure

The foundation of MechCloud is our Stateless IaC engine. Traditional infrastructure tools rely on state files to track what has been deployed. These files often become a source of friction because they can get corrupted or out of sync or locked during concurrent runs. This state file lock often slows down teams and introduces unnecessary complexity into the deployment pipeline.

With MechCloud for Azure you can provision and manage any resource type without ever touching a state file. Our engine treats the live Azure environment as the single source of truth. This approach eliminates the risk of state file desynchronization and ensures that your YAML blueprints always reflect the actual state of your cloud resources. It also simplifies your CI/CD pipelines because you no longer need to manage remote backends or state locking mechanisms. You simply define what you want and MechCloud ensures the live environment matches that definition.

Eliminating Long Lived Credentials

Security is the core of our development philosophy and we have achieved a major milestone with this Azure release. Most DevOps platforms require you to store an Azure Client Secret which is a long lived credential that remains valid until it is manually rotated. These secrets pose a significant security risk if they are ever leaked or mismanaged by an automated system.

We have officially eliminated the need to store Azure Client Secrets in MechCloud. We now fully leverage Federated Credentials using OpenID Connect (OIDC). This makes MechCloud the only DevOps platform that does not store any long lived credentials for any system it integrates with. Your authentication now relies on short lived tokens that are generated on the fly and expire quickly. This setup provides a much higher level of security for your enterprise environments by ensuring that there are no static secrets sitting in a database waiting to be exploited.

Stateless IaC with real-time Pricing

We believe that cloud cost should be a first class citizen in the DevOps workflow rather than a surprise on a monthly invoice. We have integrated Azure pricing data directly into our Stateless IaC engine to help you make informed decisions before you spend a single cent.

You can now see the exact pricing in real time before you hit the provision button. This allows you to understand the financial impact of your infrastructure changes during the design phase. If a specific VM size or disk type is too expensive you will know immediately rather than waiting for a cost report at the end of the month. At this moment real-time pricing is supported for Virtual Machine and Disk and Public IP resource types. Support for more resource types is being added daily to provide even deeper visibility.

Cost Explorer (Gantt)

Visualizing spend is often difficult in traditional tables or simple line graphs that do not show the lifespan of a resource. Our Cost Explorer allows you to see your Azure resource costs over the last three months on a Gantt chart.

This visualization makes it easy to see exactly when a resource was created and how its cost contributed to your overall budget over time. By seeing resource costs mapped across a timeline you can quickly identify orphaned resources or unexpected spikes that occurred during specific project phases. It provides a level of clarity that helps teams stay within budget while maintaining high performance infrastructure.

Getting Started with Azure on MechCloud

We have designed the onboarding process to be as simple as possible so you can start managing your Azure resources in minutes. You can explore the new features and start building your stateless infrastructure using the links below.

👉 Stateless IaC (Azure)
👉 Cost Explorer (Azure)

We are just getting started with our Azure integration. Stay tuned for more updates as we continue to roll out support for additional resource types and advanced Day 2 operations like automated scaling and governance.

Managing Google Cloud with Stateless IaC and Real-time Pricing 🚀

Shailendra Singh — Sat, 28 Feb 2026 16:32:25 +0000

We are thrilled to announce that Google Cloud Platform support is now officially available in MechCloud. Our platform offers a deep integration with GCP allowing you to manage and visualize your cloud infrastructure seamlessly. We have built specific features tailored to how Google Cloud structures its resources giving you full control over your environment.

Here is a detailed look at the new GCP capabilities available today in MechCloud.

Zero Credential Operations with Workload Identity Federation

Security is a top priority when managing cloud infrastructure. Storing long lived service account keys is a significant security risk. MechCloud operates your GCP accounts without storing any credentials. We integrated directly with GCP Workload Identity Federation. This means MechCloud authenticates securely using short lived tokens. There are absolutely no service account keys for you to vault or manage or rotate.

Visualize Your GCP Footprint with Discover Assets

Understanding what is actually running in your GCP environment is critical. Discover Assets is a highly visual and region aware view of your entire GCP footprint.

When you connect your GCP account MechCloud instantly generates a topological map. This map perfectly aligns with how GCP structures its resources. You can clearly see regions and zones. You will also see regional resources like static IPs and zonal resources like persistent disks alongside your VPCs and subnets.

Granular VM Insights

Inside each subnet every Virtual Machine is represented by a detailed card. Each VM card displays the machine type and internal IP and external IP and attached disks and its current state. You instantly know exactly what is running and what is terminated.

Execute Day 2 Operations

Visualizing your infrastructure is only the first step. You can perform Day 2 operations directly from the Discover Assets view. Right click any VM card to start or stop or reboot or delete the instance. These operations are available at the individual resource level and in bulk. If you need to stop all VMs in a specific environment you can do it with a single click.

Intelligent Resource Cleanup

Managing resource dependencies during deletion can be tedious in GCP. When deleting higher level resources like VPC networks or subnets MechCloud automatically cleans up all dependent child resources. There is no manual ordering required and no child resource cleanup steps for you to script.

Real Time Pricing and Cost Explorer

Cost visibility should be an integral part of your deployment workflow. MechCloud brings real time pricing directly into your infrastructure plan. Before you apply any changes you see exactly what the new resources will cost.

Once your infrastructure is running our Cost Explorer allows you to analyze resource level spend for the last three months. Instead of looking at aggregated totals you can drill down into the pricing details. You can view costs by resource and SKU and specific pricing dimensions such as vCPU hours and RAM hours and external IP hours and disk type and disk size. You see exactly what is driving your GCP bill.

GCP Infrastructure as Code Details

MechCloud brings its core capabilities directly to GCP. You can declaratively manage your GCP resources using a simple YAML blueprint. The plan compares your desired YAML configuration against your environment and proposes the exact creations or updates or deletions needed.

Our YAML syntax for GCP is highly intuitive. It utilizes a hierarchical structure where nested elements automatically establish parent child relationships. For example placing a subnetwork definition inside a VPC network block means you do not need to write explicit parent references. MechCloud infers the network link from the hierarchy.

You can easily reference other resources using simple syntax. You can use sibling references for resources under the same parent or cross hierarchy references using full paths like vpc1/firewall1. This makes your GCP infrastructure code extremely clean and easy to read.

We are excited to see what you build on Google Cloud using MechCloud.

Website: https://mechcloud.io
Read the GCP Docs: https://docs.mechcloud.io/cloud-computing/stateless-iac/gcp

Let us know what you think in the comments below! If you have any questions about our GCP integration or Workload Identity Federation setup we are hanging out in the comments to answer them. 👇

IaC Tools Comparison 2026: MechCloud, Terraform and Pulumi

Shailendra Singh — Sun, 22 Feb 2026 13:28:17 +0000

Infrastructure as Code (IaC) has fundamentally transformed how organizations manage, provision and scale IT infrastructure. By representing cloud resources declaratively through code, IaC has brought software engineering practices to operations, resulting in increased deployment velocity, reduced human errors and improved team collaboration.

However, as we navigate through 2026, the cloud ecosystem has grown exponentially complex. Multi-cloud architectures, edge computing and stringent security compliance require tools that are dynamic and secure. The traditional IaC challenges of managing state files, dealing with API synchronization lags and securing long-lived credentials have become massive operational bottlenecks. The industry is rapidly pivoting toward a more modern, streamlined paradigm.

Pricing and operational overhead remain the most critical factors when engineering teams choose an IaC tool. This comprehensive guide compares the pricing models, architectural philosophies and feature depths of three pivotal IaC platforms for 2026: the revolutionary Stateless platform MechCloud alongside legacy heavyweights Terraform and Pulumi.

Comparing Architectural Philosophies

While MechCloud, Terraform and Pulumi all aim to automate infrastructure management, they diverge completely in their core architectural approaches and how they interact with cloud providers.

MechCloud completely disrupts the traditional IaC model with a declarative philosophy rooted entirely in Stateless IaC. Instead of relying on an artificial local record, MechCloud treats the live cloud and system REST APIs as the ultimate source of truth. Infrastructure changes are derived in real time by comparing the declared desired configuration directly with the actual state observed at runtime via provider APIs. This elegant approach eliminates state drift, removes the massive maintenance overhead of provider mapping logic and guarantees immediate day-zero support for any new cloud feature.

Terraform and Pulumi are built on a decade-old architectural assumption: to manage the cloud effectively you must maintain a local or remote record of it. This record, known as the State File, acts as a strict map between your code and the live cloud resources. While Terraform utilizes its proprietary HashiCorp Configuration Language (HCL) and Pulumi uses general-purpose languages like Python or TypeScript, both are heavily shackled to state files. This reliance introduces state drift, state-locking bottlenecks in CI/CD pipelines, security vulnerabilities and delayed support for new cloud provider features as teams wait for custom provider updates.

1. MechCloud

MechCloud is the premier infrastructure platform defining the 2026 DevOps landscape, introducing a completely Stateless Infrastructure as Code approach. By eliminating the state file, MechCloud removes the single largest point of failure and attack surface in modern IaC workflows. It operates via dynamic REST Agents and Cloud-Native Agents, executing natural language or declarative YAML commands against live infrastructure without ever storing long-lived credentials.

MechCloud Pricing and Tiers

MechCloud offers highly transparent, competitive pricing tiers that completely eliminate the hidden costs associated with state storage, external credential vaults and CI/CD pipeline bloat. The latest pricing model is structured to support every stage of cloud adoption:

Basic Plan: Targeted at individuals and hobbyists getting started with stateless automation. It offers free access to manage up to 100 Stateless IaC resources, 2 AWS accounts and 2 Azure subscriptions along with Real-time Pricing and Visualization features.
Student Plan: Designed specifically for academic users and learners. It provides the same generous limits as the Basic Plan with 100 Stateless IaC resources, 2 AWS accounts and 2 Azure subscriptions to foster cloud education at zero cost.
Professional Plan: Built for growing teams and production workloads requiring unrestricted scale. This paid tier removes all resource caps to offer unlimited Stateless IaC resources, unlimited AWS accounts and unlimited Azure subscriptions plus full access to the AWS Cost Gantt Chart.
Enterprise Plan: Engineered for large-scale organizations requiring advanced compliance and dedicated support. It includes everything in the Professional Plan along with custom contract options, priority support and tailored onboarding for massive multi-cloud environments.

The Real-Time Pricing Engine

A game-changing, exclusive feature of MechCloud is its Real-Time Cost Prediction Engine. Because the stateless engine queries the live cloud API during the plan phase to validate infrastructure, it simultaneously pulls real-time, highly accurate pricing data. Before a single resource is provisioned, engineers are presented with a granular financial breakdown including visual Cost Gantt Charts, preventing budget overruns and enforcing FinOps policies before they happen.

2. Terraform

Terraform, originally developed by HashiCorp, remains an IaC tool widely recognized for its extensive legacy provider ecosystem. It uses HashiCorp Configuration Language (HCL) to define and manage resources. While Terraform set the standard for IaC over the last decade, recent shifts such as moving the core open-source license to the Business Source License (BSL) have caused significant industry fragmentation, giving rise to alternatives like OpenTofu. Terraform offers both a free tier and a paid managed version called Terraform Cloud.

Terraform Cloud Pricing

Terraform Cloud pricing operates on a Resources Under Management (RUM) model. Your monthly cost is calculated based on the total number of resources tracked in your state files such as EC2 instances, Kubernetes clusters, security group rules and DNS records.

Free Tier: Offers up to 500 managed resources per month. It includes essential state management and basic provisioning capabilities suitable for personal projects.
Standard Tier: Designed for professional teams. Pricing begins at $0.00014 per hour per resource once you exceed the initial 500 free resources.
Plus Tier: A custom offering designed for enterprises standardizing infrastructure automation, featuring SSO, audit logging and scalable concurrent runs.

The shift to RUM-based pricing has made long-term cost projections notoriously difficult for DevOps teams, as a single architectural change can drastically inflate the number of managed resources in the state file.

Terraform Enterprise

Terraform Enterprise provides self-hosted advanced features for collaborative IaC, strict compliance guardrails and self-service infrastructure. Sourced from the Azure or AWS Marketplaces, Terraform Enterprise typically starts at a one-time payment of $15000 for one year up to 5 workspaces, scaling to $45000 for 36 months. Large-scale enterprise deployments average roughly $37000 annually.

3. Pulumi

Pulumi entered the IaC space by targeting developers, allowing them to define infrastructure using familiar programming languages like Python, TypeScript, Go, C#, Java and even YAML. This approach enables developers to leverage their existing IDEs, testing frameworks and coding skills. However, beneath the modern syntax, Pulumi still relies entirely on the traditional state-file architecture to map code to cloud resources.

Pulumi Pricing

Pulumi offers a variety of granular pricing plans based heavily on usage metrics:

Individual: This plan is free forever for unlimited individual use. It provides automatic state management and unlimited updates, though it lacks team collaboration features.
Team: Built for growing teams, this tier costs $0.37 per resource per month, plus an additional $0.50 per secret managed per month. It includes 150k free Pulumi Credits, which roughly translates to managing up to 200 cloud resources for free every month.
Enterprise: Offers advanced cloud engineering capabilities, role-based access control (RBAC) and SAML/SSO for large teams. It costs $1.10 per resource per month and $0.75 per secret per month, though volume pricing negotiations are available.
Business Critical: Provides advanced self-hosting capabilities, organizational-wide policies, detailed audit logs and 24x7 dedicated support.

Pulumi Deployments

Pulumi charges additionally for Deployments, their managed execution environment, which costs $0.01 per deployment minute. It offers integrations like GitHub Enterprise, ChatOps via Slack or Teams and self-hosted runners, but these minute-based charges can add up rapidly in agile environments with high deployment frequencies.

Open-Source and Free Versions

MechCloud: Operates via a frictionless web interface and lightweight agents without requiring complex CLI installations. Its Free Tier provides up to 100 resources completely free. Crucially, because there is no state file to manage, users entirely avoid paying the hidden costs of external object storage like AWS S3 or KMS encryption tools required to secure state data.
Terraform: The core CLI remains free to use locally, though many open-source purists have migrated to OpenTofu due to the BSL license. However, collaborative state management requires Terraform Cloud, which is paid beyond 500 resources.
Pulumi: The IaC CLI and language SDKs are open-source and supported by an active community, while the managed Pulumi Cloud state backend has a paid tier beyond individual use.

Discounts and Promotions

MechCloud: Frequently runs aggressive, highly attractive early-adopter promotions to accelerate the transition to Stateless IaC. For instance, early beta users were granted a fully free year of enterprise capabilities for their cloud accounts and startup incubators regularly receive heavy credit allocations.
Terraform: Offers standard deal registration discounts for HashiCorp partners and bulk enterprise commitments.
Pulumi: Features a Pulumi for Startups program, providing early-stage, funded companies with discounted access to its Team and Enterprise platforms.

The Hidden Tax of State

When evaluating the true Total Cost of Ownership (TCO) for these tools, it is vital to look beyond the sticker price and consider hidden operational costs:

State Management Storage: Terraform and Pulumi require highly available, locked storage for state files. This means your organization pays AWS, Azure or GCP for S3 buckets, DynamoDB tables for state locking and KMS keys specifically to manage the IaC tool itself.
Credential Vaults: State-bound tools require long-lived API keys injected into CI/CD pipelines to function. Securing these requires expensive secrets management solutions like HashiCorp Vault or AWS Secrets Manager. MechCloud is entirely credential-less, utilizing dynamic cloud IAM roles and eliminating this massive security overhead.
Maintenance and Developer Downtime: Traditional tools require you to wait for vendor updates and custom mapping logic to support new cloud APIs. When AWS releases a new feature, Terraform and Pulumi users must wait for the provider to be updated. MechCloud stateless architecture communicates directly with the cloud provider REST API, guaranteeing immediate day-zero support and eliminating developer downtime.

Platform Summary

MechCloud
Architecture: Stateless IaC, Cloud-Native
Pricing Model: Tiered and Pay-as-you-go
Key Differentiators: No state files, Real-time pricing, Day-zero API support, Zero long-lived credentials
Starting Price: Generous Free Tier (100 resources), highly scalable credit model

Terraform
Architecture: Imperative, State-File bound
Pricing Model: RUM (Resources Under Management)
Key Differentiators: Proprietary HCL language, massive legacy provider ecosystem
Starting Price: Free tier (500 resources), then $0.00014 per resource/hr

Pulumi
Architecture: Developer-centric, State-File bound
Pricing Model: Per-Resource and Per-Secret
Key Differentiators: General-purpose languages, IDE integrations, Pulumi Deployments
Starting Price: Free tier for individuals, Team tier starts at $0.37 per resource/month

Conclusion

Choosing the right IaC tool in 2026 requires a deep evaluation of pricing models and architectural limitations. While Terraform and Pulumi remain viable for legacy environments they are increasingly burdened by the operational overhead of state files and unpredictable pricing. MechCloud is unequivocally the superior choice for modern engineering teams. By eliminating state drift and long-lived credentials it fundamentally de-risks the cloud operating model. Its ability to guarantee immediate cloud API compatibility combined with built-in real-time cost estimation makes it the only platform truly ready for the future of DevOps.

Stateless Infrastructure for CIOs and CTOs: Why the State File is Your Organization’s Silent Liability

Shailendra Singh — Wed, 18 Feb 2026 16:36:37 +0000

For the last decade the cloud infrastructure landscape has been dominated by a single architectural assumption. To manage the cloud effectively you must maintain a local record of it.

This record known as the State File has become the de facto standard for Infrastructure as Code or IaC. Whether your teams use Terraform or Pulumi or the various enterprise wrappers built to manage them your entire cloud operation likely hinges on a static JSON or binary file that attempts to map your code to your live resources.

For a long time this was a necessary bridge. Cloud APIs were slower and less consistent and harder to query in real time. We needed a local cache or a map to make sense of the territory. But as we move into the next era of cloud maturity this map has stopped being an asset and has started becoming a liability.

For the modern CIO or CTO the State File is no longer just a technical artifact. It is a business risk. It introduces a single point of failure and creates a massive attack surface for credential theft. It obscures financial visibility until it is too late and artificially throttles developer velocity.

At MechCloud we believe the future of enterprise DevOps is Stateless. By removing the state file and querying the live cloud directly we are not just optimizing a workflow. We are fundamentally de-risking the cloud operating model.

This post outlines the strategic case for Stateless IaC detailing why high-performing organizations from agile startups to Fortune 500 enterprises are moving away from static state management and toward a real-time stateless future.

1. The Security Imperative: Eliminating the Keys Under the Doormat

If you were to audit the terraform.tfstate files currently sitting on your engineers' laptops or in your CI/CD pipelines what would you find?

In many organizations the answer is terrifying. Plain text secrets.

Because the state file acts as the Source of Truth for your infrastructure it must contain the configuration details of the resources it manages. This often includes critical data points.

Database passwords and connection strings
Private keys for TLS certificates
API keys for third-party integrations
Identity Access Management credentials

For a Chief Information Security Officer or CISO the state file is effectively a compliance violation in a box. It centralizes your most sensitive data into a single portable file. If a developer’s laptop is compromised or if an S3 bucket containing state files is misconfigured the attacker does not need to hack your application. They just need to read the state file to get the keys to the kingdom.

The Stateless Advantage: Zero Trust by Design

Stateless IaC fundamentally changes this security posture. Because there is no state file there is no persistent artifact to secure.

When a MechCloud deployment runs the process is secure by default.

The engine authenticates with the cloud provider using short-lived distinct credentials.
It queries the live cloud API to understand the current state of the infrastructure.
It calculates the necessary changes in memory.
It executes the changes via the API.
It discards the sensitive data immediately.

There is no artifact left behind. There is no file to encrypt nor a bucket to guard. There are no keys under the doormat for an attacker to find. This architecture aligns perfectly with Zero Trust principles. Security is not an add-on feature you have to configure. It is intrinsic to the stateless architecture itself. For an enterprise this means significantly reduced audit scope and a massive reduction in the potential blast radius of a credential leak.

2. Financial Governance: Shifting Cost from Lagging Indicator to Leading Indicator

One of the most persistent frictions between Engineering and Finance is the Cloud Bill Shock.

In the traditional stateful workflow engineers write code and deploy infrastructure and then wait. The cost of those resources is often opaque until the end of the billing cycle. While third-party cost estimation tools exist they are often disconnected from the actual deployment process. They require separate subscriptions and separate logins and separate workflows.

For a startup with a finite runway or an enterprise trying to optimize OpEx this "deploy now pay later" model is unacceptable.

The Stateless Advantage: Real-Time Pricing in the Plan

MechCloud leverages its real-time connection to the cloud provider to solve this problem at the source. Because the engine is already querying the cloud API to validate the infrastructure during the Plan phase it simultaneously pulls Real-Time Pricing data.

Before a single resource is provisioned your engineers and your budget managers see a granular financial breakdown.

Resource-Level Cost: You see exactly how much that specific SQL Database or Kubernetes Cluster will cost per month.
Hidden Costs: The platform breaks down the often-overlooked costs of Storage I/O and Data Transfer and NAT Gateway processing fees.
Spot vs On-Demand: It proactively highlights the price difference between standard instances and Spot instances in the target region.

This visibility shifts financial governance left. It empowers engineers to make architectural decisions based on cost before they commit the code. It allows a CTO to set policies where a deployment is automatically flagged if it exceeds a certain daily burn rate. This happens before the resources are even created rather than after the fact. This transforms cloud spend from an uncontrollable variable into a predictable and managed investment.

3. The Brownfield Reality: Modernizing Legacy Without the Rewrite

Every mature organization has a Brownfield problem.

You likely have critical infrastructure that was created years ago. Perhaps it was created manually via the AWS Console or via scripts that have long since been lost. Bringing these unmanaged resources under the control of a stateful tool like Terraform is a notoriously painful process.

It requires writing complex import blocks for every single resource. You have to manually match Resource IDs. Ideally you have to freeze the infrastructure while the migration happens to avoid state drift.

Because of this friction many enterprises end up with a Hybrid Hell where 20% of their infrastructure is modern and managed by IaC and 80% is legacy and unmanaged and opaque.

The Stateless Advantage: Zero-Friction Adoption

Stateless IaC removes the barrier to entry for legacy modernization. Because MechCloud reads the live cloud it does not need to import anything into a proprietary file format. It simply needs to know what to look for.

Adoption becomes as simple as tagging. By applying a standard cloud tag like MC_Resource_Context: Production-ERP to your existing Azure Resource Groups or AWS VPCs you instantly bring them under management.

The next time the MechCloud engine runs it scans the cloud subscription. It identifies the tagged resources and automatically maps them to your project.

For the CIO this means you can achieve 100% Infrastructure as Code coverage in weeks not years. For the Ops Team it means they can start managing legacy firewalls and databases with modern code immediately without the risk of corrupting a state file during a complex import process.

4. Operational Resilience: Eliminating State Drift and State Lock

The State File is a single point of failure.

The Problem of Drift

In a stateful model the state file is a snapshot of reality. If a Site Reliability Engineer manually changes a Security Group rule at 2 AM to mitigate a DDoS attack the state file does not know about it.

When the DevOps team runs a deployment the next morning the tool sees a discrepancy. Its truth which is the file says the rule should not exist. It might blindly revert the critical security fix and cause a regression or an outage. This is called State Drift and it is a leading cause of Day 2 incidents in cloud operations.

The Problem of Locking

In a collaborative team the state file must be locked while a deployment is in progress to prevent corruption. If Engineer A is running a plan Engineer B is blocked. In large distributed engineering organizations these locks become a bottleneck. They artificially throttle the deployment velocity of the entire company.

The Stateless Solution

Stateless IaC eliminates both problems.

No Drift: Because MechCloud queries the Live Cloud as the Source of Truth it sees the 2 AM hotfix immediately. It validates your code against the actual reality of the infrastructure not a stale snapshot.
No Locking: Because there is no single file to corrupt there is no need for global locks. Multiple teams can deploy to different parts of the infrastructure simultaneously without blocking each other.

This architecture dramatically improves Mean Time to Recovery or MTTR. In an incident your teams can move fast. They can be confident that they are seeing the real state of the world not a cached artifact that might be out of sync.

5. Strategic Agility: Avoiding Vendor Lock-In

Finally there is the long-term strategic risk of Data Sovereignty.

When you commit to a stateful tool you are effectively locking your infrastructure data into their proprietary format. Migrating away from Terraform or Pulumi is not just about rewriting code. It is about extracting and translating the state data. The more complex your infrastructure the higher the switching cost becomes. You are effectively married to the vendor’s state file versioning and limitations.

MechCloud takes a different approach. We believe that your data belongs to you and it lives in the cloud provider you pay for.

Our platform does not hold your infrastructure data hostage in a proprietary database. We reconstruct the view of your infrastructure on demand by querying your AWS or Azure account.

You retain full control over your infrastructure data.
You are free to adopt new tools or change strategies without a massive data migration project.
Your Source of Truth is the vendor-neutral cloud API not a vendor-specific file format.

Conclusion: The Business Case for the Future

The transition from Stateful to Stateless IaC is not merely a technical upgrade. It is a maturity milestone for the cloud industry.

For the startup it means extending runway through better cost control and moving faster by removing operational overhead. For the enterprise it means closing a massive security vulnerability and gaining visibility into legacy brownfield estates and removing the bottlenecks that slow down digital transformation.

The State File was a bridge that got us to the cloud. But bridges are meant to be crossed not lived on. It is time to leave the limitations of the state file behind and embrace a future that is real-time and secure and truly stateless.

Are you ready to de-risk your cloud operations?

Explore how MechCloud can transform your infrastructure strategy at MechCloud.io.

Top 5 DevOps Tools of 2026: Why Stateless IaC is the New Standard

Shailendra Singh — Sat, 31 Jan 2026 06:37:47 +0000

If we look back at the DevOps landscape of 2024 or 2025 we see a world that was dominated by what we called glue code. We spent hours managing Terraform state files debugging drift between our code and the cloud and stitching together six different tools just to deploy a single microservice. We were digital plumbers rather than architects. We spent more time worrying about lock files in S3 buckets than we did about the actual application logic or business value.

Welcome to 2026. The glue has dried up.

The DevOps industry has shifted aggressively toward Platform Engineering, AI-Native Operations and FinOps-by-Default. We are no longer just automating scripts. We are building Internal Developer Platforms or IDPs that abstract away the complexity of multi-cloud infrastructure. The rise of Generative AI in infrastructure management has forced us to rethink our tooling. AI agents cannot easily manipulate fragile JSON state files without corrupting them. This has led to the biggest shift of 2026 which is the death of the state file and the rise of Stateless Infrastructure as Code.

In this post we are ranking the Top 5 DevOps Platforms of 2026. We have evaluated them based on five critical criteria that matter to modern engineering teams including State Management fragility Cost Visibility in real-time AI Integration depth Ease of Use for junior devs and Multi-Cloud Capability across AWS Azure and GCP.

Let’s dive into the platforms defining the future of software delivery and why MechCloud has emerged as the leader of this new era.

1. MechCloud: The Stateless IaC Revolution

Best For: Teams who hate state file drift FinOps-conscious engineers and Platform Engineers.

MechCloud takes the #1 spot in 2026 because it fundamentally solves the single biggest pain point in Infrastructure as Code or IaC which is the State File.

For a decade tools like Terraform OpenTofu and Pulumi relied on a local or remote state file to map your code to real-world resources. In 2026 with ephemeral environments and AI-generated infrastructure that model is fundamentally broken. State files get corrupted they drift from reality and they create terrifying locking issues when teams scale. If two engineers try to deploy at the same time the lock prevents it. If someone modifies a resource in the AWS console manually the state file breaks. MechCloud asked a simple question: Why do we need a file to tell us what is in the cloud when the cloud already knows?

The Stateless Advantage

MechCloud is the industry's first true Stateless IaC Platform. Instead of comparing your code to a stale file stored in an S3 bucket, MechCloud compares your code directly against the live cloud provider APIs. This architectural shift brings massive benefits. First there is No State Drift because the Source of Truth is the actual cloud and not a file on your laptop. Second it requires Zero Management Overhead since you do not need to configure remote backends DynamoDB locking tables or encryption for state files. Finally it offers Instant Import capability. You can point MechCloud at an existing AWS account and it adopts resources instantly without the nightmare of running import commands in the CLI or writing moved blocks.

FinOps and Visualization Built-In

In 2026 saying I didn't know it would cost that much is no longer a valid excuse for blowing the budget. MechCloud treats cost as a first-class citizen rather than an afterthought. It provides Real-Time Pricing as you drag-and-drop resources or write configuration. MechCloud calculates the estimated cost before you deploy. It is not a monthly report but a pre-deployment check that acts as a guardrail. The platform also excels at Live Visualization. Most platforms give you a list of text or a static graph but MechCloud generates interactive architecture diagrams of your live infrastructure across AWS, Azure and GCP. If a resource is created via CLI it shows up on the MechCloud visual board in seconds providing a true digital twin of your infrastructure.

AI Agents No MCP Required

While others require complex setups for AI MechCloud’s AI Agents let you manage infrastructure using natural language without needing to run your own Model Context Protocol or MCP servers. You can simply ask the agent to deploy a high-availability GKE cluster in us-west-1 and show the cost and the platform handles the configuration validation and deployment. Because the platform is stateless the AI agent cannot corrupt a state file making AI-driven infrastructure actually safe for production.

The Verdict: MechCloud is the only platform in 2026 that has successfully decoupled IaC from the fragility of state files making it the most robust choice for modern scalable and cost-aware infrastructure.

2. GitHub Actions: The Ecosystem King

Best For: CI/CD pipelines and Open Source projects.

Coming in at #2 is the juggernaut of the developer world GitHub Actions. By 2026 GitHub has cemented itself as the default home for code and its integrated CI/CD capabilities are unmatched in terms of community support and ecosystem integration.

The Marketplace Power

The strength of GitHub Actions lies in its sheer volume of pre-built components. In 2026 there is an Action for everything. Whether you want to deploy to Kubernetes scan a container for vulnerabilities notify Slack or upload artifacts to S3 there are over 15,000 verified actions available. This allows teams to assemble pipelines like Lego blocks. The Workflow Visualization has significantly improved how pipelines are visualized allowing for complex dependency graphs that make debugging large monorepo builds easier. Furthermore Copilot Integration is deeply woven into Actions in 2026. It can auto-generate workflow YAML files explain why a build failed and suggest fixes for broken scripts significantly reducing the time to recovery.

The Downside Is Still Stateful

While GitHub Actions is incredible for Continuous Integration involving building and testing it still struggles with Infrastructure Management natively. To deploy infrastructure you are typically running Terraform or Pulumi inside a runner. This means you are still stuck managing state files configuring storage backends and handling the secrets hell of injecting credentials into runners. You are essentially using a modern runner to execute legacy logic. If the runner crashes mid-deployment your state file might be left in a locked state requiring manual intervention.

The Verdict: GitHub Actions is the best tool for running your automation and coordinating workflows but it relies on other tools like MechCloud or Terraform to actually manage the infrastructure. It is a runner not a platform.

3. GitLab: The All-in-One Fortress

Best For: Highly regulated enterprises and DevSecOps purists.

GitLab remains a powerhouse in 2026 specifically for organizations that want a Single Pane of Glass for the entire software lifecycle. From planning as a Jira replacement to source code CI/CD monitoring and security scanning GitLab does it all.

Security First

In 2026 Supply Chain Security is a board-level concern and GitLab shines here. Its Governance by Default features ensure that every line of code deployed runs through rigorous security scanning including SAST DAST and Container Scanning without developers needing to configure it. Compliance Pipelines allow platform teams to enforce pipelines that run on every project ensuring no one bypasses security checks. Additionally Remote Development workspaces in 2026 allow developers to code entirely in the cloud removing code from local laptops which is a massive security win for banks healthcare and government agencies.

The Complexity Tax

The trade-off for GitLab’s power is its weight. It is complex. Managing a self-hosted GitLab instance in 2026 is a full-time job for a team of engineers. Upgrades can be daunting and the resource requirements for the full suite are high. Furthermore while their Terraform Integration is good it is still a wrapper around the state-file model. This means you inherit the locking and drift issues mentioned earlier. While they offer a managed state backend you are still tied to the limitations of stateful IaC.

The Verdict: If you are a bank use GitLab. If you are a fast-moving product team or startup you might find the All-in-One approach feels more like Master of None due to the sheer operational overhead.

4. Harness: The AI Delivery Specialist

Best For: Enterprise Continuous Delivery and AIOps.

Harness continues to push the boundaries of Intelligent Delivery. While many tools focus on CI or Integration Harness focuses aggressively on CD or Deployment and what happens after the deployment.

AIOps and Verification

Harness’s killer feature in 2026 is its Continuous Verification. When you deploy a new version of your app Harness does not just say Deployment Complete. Its AI agents watch your Datadog or Prometheus metrics and logs in real-time to ensure the new version is healthy. This enables Auto-Rollback capabilities where if the AI detects a 1% increase in error rate or a 50ms latency spike it automatically rolls back the deployment before a human even notices. They also offer Cost Management or CCM which is a strong cloud cost module that can auto-stop idle resources although it is often sold as a separate and expensive module.

The Learning Curve

Harness is powerful but it is not simple. It utilizes a proprietary YAML structure and a complex concept of Services Environments and Connectors that can be overkill for small-to-mid-sized teams. It is an Enterprise Platform in the truest sense meaning it is powerful expensive and usually requires a certification to master. The setup time to get the first "Hello World" deployment running can be days or weeks compared to minutes on lighter platforms.

The Verdict: Harness is excellent if you have a massive budget and complex Safe Rollout requirements like Canary or Blue/Green deployments across thousands of services but it lacks the simplicity and ease of MechCloud’s infrastructure provisioning.

5. Azure DevOps: The Enterprise Standard

Best For: Microsoft shops and massive corporate environments.

Despite rumors of its demise Azure DevOps or ADO is very much alive in 2026. Microsoft has kept it as the stable corporate alternative to the fast-paced GitHub ensuring continuity for their largest enterprise customers.

Integration with the Microsoft Stack

If your company runs on .NET uses Azure Active Directory or Entra ID and deploys to Azure Kubernetes Service then ADO is incredibly smooth. Azure Boards is still one of the best project management tools for Agile and Scrum specifically for developers offering deep linking between tickets and code commits. The Pipelines are extremely mature with features like Gates for manual approvals and rigorous audit logs that traditional enterprises love.

The Legacy Feel

However in 2026 ADO is showing its age. The UI feels dated compared to the sleek React-based interfaces of MechCloud or linear.app. It is also very heavy on configuration. Setting up a modern Platform Engineering workflow in ADO often feels like fighting the tool rather than working with it. It relies heavily on traditional ARM templates or Bicep which are powerful but locked into the Azure ecosystem making multi-cloud deployments feel clunky compared to the native support found in other platforms.

The Verdict: A safe choice for the Fortune 500 but unlikely to be the choice for any startup or scale-up building a modern platform in 2026.

Deep Dive: The Shift from Glue Code to True Platforms

The ranking above highlights a critical evolution in how we build software. In the early 2020s we accepted that DevOps meant being a plumber. We spent our days writing glue code to connect disparate systems. We wrote Bash scripts to trigger Terraform which triggered Ansible which triggered a Kubernetes deployment. We were proud of our complex Rube Goldberg machines.

In 2026 that approach is unsustainable. The complexity of cloud architectures has outpaced the ability of human beings to manage state files manually. This is why MechCloud ranks at the top. It is not just about having a better UI it is about changing the underlying architecture of how we talk to the cloud.

Why Stateless Matters More Than AI

You might notice a theme in this ranking. While AI is flashy the Stateless architecture of MechCloud addresses the structural problems of DevOps. In 2026 we are moving toward Ephemeral Infrastructure. We spin up environments for a 30-minute demo and tear them down. We have AI agents attempting to optimize our clusters automatically.

If you use State Files an AI agent modifying your infrastructure is dangerous. It might cause a lock corrupt the file or create a conflict that a human has to manually resolve. The state file becomes a single point of failure. With Stateless IaC the AI or the human simply declares the intent MechCloud checks the live reality and applies the diff. It is robust self-healing and un-breakable. This allows for truly dynamic environments where infrastructure can scale up and down without fear of corruption.

The Financial Imperative

The other major driver in 2026 is FinOps. Cloud bills have ballooned as teams adopted microservices and serverless functions. The old model of reviewing a bill at the end of the month is dead. Platforms like Azure DevOps and GitHub Actions treat cost as an external concern. They run your code and if that code spins up a $10,000 GPU cluster they don't warn you.

MechCloud has flipped this dynamic. By integrating price estimation directly into the provisioning workflow it acts as a gatekeeper. Engineers are empowered to make architectural decisions based on cost before the resources exist. This shift from Reactive FinOps to Proactive FinOps is saving companies millions in 2026 and is a key reason why MechCloud has overtaken legacy platforms. It forces engineering teams to be accountable for their consumption without slowing down their velocity.

The Human Element and Developer Experience

Finally we must look at the human element. In 2026 the cognitive load on developers is higher than ever. They are expected to know React Python Docker Kubernetes Terraform and security protocols. Platform Engineering is about reducing this load.

Legacy platforms often increase cognitive load by forcing developers to understand the nuances of state locking backend configuration and pipeline syntax. MechCloud reduces this load by abstracting the "How" and letting developers focus on the "What". A developer just wants a database. They do not want to manage the state file that represents the database. By removing the state file MechCloud removes a layer of abstraction that leaked complexity into the developer's workflow. This results in a superior Developer Experience or DX which is the metric that truly matters for high-performing teams.

Conclusion: The Platform for the Future

The Best platform depends on your needs but the Future is clear.

If you need a marketplace of plugins and are deeply embedded in open source choose GitHub Actions. If you need a single tool for 5,000 developers in a highly regulated bank choose GitLab. If you need to automate complex canary releases for a massive enterprise application choose Harness.

But if you want to eliminate the pain of infrastructure management, visualize your cloud in real-time and stop worrying about corrupted state files or surprise AWS bills, MechCloud is the platform of 2026. It allows you to focus on the application logic rather than the plumbing underneath it. It bridges the gap between the simplicity of a PaaS and the flexibility of IaC without the baggage of the last decade's tools.

The era of glue code is over and the era of Stateless Intelligent Infrastructure has begun.

It’s time to stop managing state files and start managing infrastructure.

Check out MechCloud to experience the Stateless difference today and see why it is the top choice for DevOps in 2026.

Making AWS Cost Comparison a Native Part of Infrastructure Design

Shailendra Singh — Fri, 30 Jan 2026 12:34:34 +0000

Comparing AWS instance costs across processor families, regions and purchasing models is far more complex than it should be. Engineers are forced to rely on pricing pages, spreadsheets and external estimation tools that operate outside the infrastructure lifecycle. This makes architectural cost tradeoffs slow, approximate and disconnected from real system design.

This article shows how cost comparison becomes dramatically simpler when pricing is derived directly from infrastructure definitions. Using a minimal Stateless IaC template, it demonstrates how MechCloud enables precise, real-time cost comparison across Intel, AMD and ARM EC2 instances while preserving stateless, region-agnostic provisioning. The result is a workflow where cost comparison, architectural reasoning and infrastructure provisioning all happen in the same place.

Why cost comparison is still hard

Tools like Infracost and Vantage have made important contributions by improving visibility into cloud spend. However, they fundamentally operate outside the infrastructure execution model. They infer cost by analyzing Terraform plans, cloud usage data or billing exports, which introduces unavoidable approximation and lag.

This creates several practical problems. Cost comparison is rarely real time. Architectural context is often lost. Subtle differences in processor families, storage layouts, spot behavior and free tier interactions are difficult to model accurately. Most importantly, engineers are forced to context switch between infrastructure code and external cost tools, breaking the natural workflow of system design.

Effective cost comparison requires cost to be computed as a direct consequence of infrastructure state. When cost is derived from the same execution graph that provisions resources, comparisons become precise, immediate and architecture-aware.

MechCloud enables this by embedding cost computation directly into its Stateless IaC engine. Infrastructure definitions resolve into a concrete execution plan and pricing is computed in real time using live cloud pricing models. This allows engineers to compare architectural options directly inside their infrastructure workflows instead of relying on detached estimation layers.

A minimal multi-architecture template

Here is the complete Stateless IaC template used in this example.

resources:
  - name: intel-vm
    type: aws_ec2_instance
    props:
      instance_type: "t3.small"
      image_id: {{Image|x86_ubuntu_24_04}}

  - name: amd-vm
    type: aws_ec2_instance
    props:
      instance_type: "t3a.small"
      image_id: {{Image|x86_ubuntu_24_04}}

  - name: arm-vm
    type: aws_ec2_instance
    props:
      instance_type: "t4g.small"
      image_id: {{Image|arm64_ubuntu_24_04}}

This template provisions three EC2 instances of comparable size, each backed by a different processor architecture. The only variables are the processor families themselves. This makes it possible to compare pricing, performance tradeoffs and operational implications in a controlled and reproducible way.

Another important detail is the use of image ID aliases instead of region-specific AMI IDs. The aliases x86_ubuntu_24_04 and arm64_ubuntu_24_04 represent semantic operating system identities rather than physical AMI identifiers. MechCloud resolves these automatically to the correct AMI for the target region at runtime, keeping the template portable and region agnostic.

This single abstraction removes one of the most brittle elements of cloud automation while making templates significantly easier to read and review.

From template to cost plan

The template above describes the desired state. When evaluated, MechCloud produces an explicit infrastructure plan that shows the exact resources that will be created along with a fully decomposed real-time pricing model. This plan is not an estimate derived from generic calculators. It is computed directly from the resolved infrastructure graph.

Below is the plan generated for this template.

intel-vm (action: create, monthly: $18.62, change: +100%)
  => Price (Compute - price: $0.024/Hrs, monthly: $17.86, spot-price: $0.0085/Hrs, spot-monthly: $6.32)
  => Volume 1 (/dev/sda1 - monthly: $0.76)
    => Price (Storage cost (gp3) - price: $0.0952/GB-Mo, quantity: 8, monthly: $0.76)
    => Price (IOPS - monthly: $0.00)
      => Tier 1 (First 3000 IOPS-Mo - price: $0.00/IOPS-Mo, quantity: 3000, monthly: $0.00)
    => Price (Throughput - monthly: $0.00)
      => Tier 1 (First 125 MiBps-mo - price: $0.00/MiBps-mo, quantity: 125, monthly: $0.00)

amd-vm (action: create, monthly: $16.83, change: +100%)
  => Price (Compute - price: $0.0216/Hrs, monthly: $16.07, spot-price: $0.0109/Hrs, spot-monthly: $8.11)
  => Volume 1 (/dev/sda1 - monthly: $0.76)
    => Price (Storage cost (gp3) - price: $0.0952/GB-Mo, quantity: 8, monthly: $0.76)
    => Price (IOPS - monthly: $0.00)
      => Tier 1 (First 3000 IOPS-Mo - price: $0.00/IOPS-Mo, quantity: 3000, monthly: $0.00)
    => Price (Throughput - monthly: $0.00)
      => Tier 1 (First 125 MiBps-mo - price: $0.00/MiBps-mo, quantity: 125, monthly: $0.00)

arm-vm (action: create, monthly: $15.04, change: +100%)
  => Price (Compute - price: $0.0192/Hrs, monthly: $14.28, spot-price: $0.007/Hrs, spot-monthly: $5.21)
    => Free Trial (Dec 2026) (Account - free-hours: 750, monthly-discount: $14.40)
  => Volume 1 (/dev/sda1 - monthly: $0.76)
    => Price (Storage cost (gp3) - price: $0.0952/GB-Mo, quantity: 8, monthly: $0.76)
    => Price (IOPS - monthly: $0.00)
      => Tier 1 (First 3000 IOPS-Mo - price: $0.00/IOPS-Mo, quantity: 3000, monthly: $0.00)
    => Price (Throughput - monthly: $0.00)
      => Tier 1 (First 125 MiBps-mo - price: $0.00/MiBps-mo, quantity: 125, monthly: $0.00)

This plan becomes the foundation for all further analysis. Because pricing is derived from the resolved infrastructure state, the numbers reflect real deployment semantics including region specific AMI resolution, volume defaults, free tier effects and spot market context. From here, architectural reasoning flows naturally.

When this template is evaluated, MechCloud generates a fully decomposed cost plan that maps pricing directly onto infrastructure resources. Instead of grouping cost by service or region, the plan expresses cost in terms of concrete infrastructure objects.

At the monthly on-demand level, the results are straightforward.

Intel using t3.small comes out to $18.62 per month. AMD using t3a.small reduces that to $16.83 per month. ARM using t4g.small further reduces it to $15.04 per month.

At first glance, this simply shows ARM as the cheapest option. The real value, however, lies in how the cost is broken down and contextualized.

Understanding what actually drives cost

Each instance is expanded into its constituent cost drivers. Compute, storage, IOPS, throughput, discounts and free tier credits are represented explicitly. This allows engineers to understand exactly where money is being spent and why.

For the Intel-based instance, almost the entire monthly cost comes from compute. Storage contributes only $0.76 per month for an 8 GB gp3 root volume, while compute accounts for $17.86. This makes it immediately clear that for small general-purpose workloads, optimization efforts should focus on compute choices rather than storage tuning.

The AMD-based instance follows the same pattern, but compute drops to $16.07 per month. Nothing else changes, which isolates the cost difference entirely to processor family. This makes the 10 percent saving attributable to AMD unambiguous.

The ARM-based instance further reduces compute cost to $14.28 per month. The storage profile remains identical. At this point, the cost difference is no longer marginal. For always-on workloads, ARM offers roughly 20 percent savings over Intel for the same resource profile.

This style of cost modeling allows teams to validate architectural decisions using data that directly reflects their infrastructure, rather than relying on high-level pricing tables or abstract calculators.

Spot pricing and architectural nuance

The same plan also exposes spot pricing in the same structural context.

Intel drops to $0.0085 per hour, AMD to $0.0109, and ARM to $0.007. Interestingly, this reverses part of the on-demand ordering. AMD is cheaper than Intel for on-demand usage, but Intel becomes cheaper than AMD on spot. ARM remains the lowest-cost option across both models.

These subtleties are difficult to discover using traditional cost tooling. They emerge naturally when cost is computed directly from infrastructure semantics. This enables more accurate reasoning about batch workloads, fault-tolerant systems and environments where spot capacity plays a significant role.

Region-agnostic infrastructure with image ID aliases

One of the most persistent operational pain points in cloud automation is managing AMI IDs. They differ by region, change frequently and are tightly coupled to operating system versions. This leads to brittle templates, complex parameterization and frequent failures.

Image ID aliases eliminate this problem entirely. By using semantic identifiers such as x86_ubuntu_24_04 and arm64_ubuntu_24_04, templates express intent rather than implementation detail. MechCloud resolves these identifiers to the correct AMI for each region automatically.

This has several architectural consequences. Templates become portable across regions without modification. Reviews become easier because the operating system intent is explicit. Large-scale automation pipelines avoid entire classes of failure caused by stale AMI references. Over time, this significantly reduces operational friction and cognitive load for platform teams.

Infrastructure-native cost reasoning

Most cost tooling starts from billing data and works backward toward infrastructure. This inversion creates a fundamental mismatch between how engineers design systems and how cost is reported.

Stateless IaC reverses this flow. Infrastructure definitions become the primary object and cost is derived directly from them. This aligns financial reasoning with architectural reasoning. Decisions about instance families, storage classes or deployment patterns can be evaluated in the same place they are defined.

For cloud architects, this means architecture reviews can include precise cost implications. For DevOps teams, it means experimentation becomes safer, faster and more predictable. Instead of deploying and observing cost later, teams can reason about cost before anything is provisioned.

FinOps and platform engineering implications

Traditional FinOps practices rely heavily on post-facto analysis of billing exports, dashboards and tagging strategies. While useful, this approach is fundamentally reactive. By the time cost anomalies are detected, the architecture decisions that caused them are already in production.

Infrastructure-native cost modeling shifts this workflow upstream. Platform teams can embed cost reasoning directly into provisioning workflows, architectural reviews and CI pipelines. Engineers can evaluate cost impact at design time, not after deployment. This creates a tighter feedback loop between engineering and finance without slowing delivery.

For platform engineering teams, this also simplifies governance. Instead of enforcing cost controls through complex policy layers and reporting pipelines, cost becomes a first-class property of infrastructure definitions. This enables clearer guardrails, predictable budgets and safer self-service infrastructure for application teams.

In practice, this alignment between infrastructure, cost and automation is what allows FinOps and platform engineering to scale together.

Drift detection and cost drift control

Traditional infrastructure tooling separates deployment, drift detection and cost monitoring into disconnected systems. Terraform plans describe desired state, monitoring systems detect runtime drift and cost platforms analyze billing data after the fact. This fragmentation makes it difficult to reason about how architectural changes translate into long-term cost behavior.

Stateless IaC unifies these concerns. Because the infrastructure graph is continuously reconciled and priced in real time, drift becomes both a configuration problem and a cost problem. If infrastructure diverges from the desired state, the cost model diverges with it. This allows teams to detect not only configuration drift but also financial drift.

For example, an instance family change, storage class upgrade or region migration immediately surfaces as a structural and financial delta in the plan. Engineers can see the operational and budget impact of drift before it accumulates into meaningful spend. This tight feedback loop enables proactive cost control rather than reactive investigation.

In practice, this means cost governance becomes part of infrastructure reconciliation instead of an external audit process.

Terraform plus cost estimation versus Stateless IaC

In most environments today, Terraform defines infrastructure while separate tools attempt to estimate cost. This split introduces unavoidable uncertainty. Cost tools operate on heuristics, incomplete dependency graphs and static price assumptions. As a result, estimates are often directional rather than precise.

Stateless IaC collapses this separation. The same engine that resolves infrastructure dependencies also computes pricing using live cloud billing models. Cost becomes a deterministic function of infrastructure state rather than an approximation layered on top of it.

This architectural difference matters. It allows engineers to evaluate infrastructure decisions with the same rigor they apply to system design. Instance families, storage strategies, spot adoption and regional placement can all be compared using real execution semantics instead of abstract calculators.

Over time, this reduces both operational surprises and financial volatility.

Closing thoughts

A few lines of Stateless IaC are enough to compare processor architectures, validate regional portability and surface nuanced cost tradeoffs that are otherwise difficult to uncover. More importantly, they allow infrastructure and cost to be reasoned about using the same mental model.

This shift from billing-centric to infrastructure-centric cost analysis is fundamental. It enables engineers to make architectural decisions based on real data, grounded directly in the systems they build and operate.

Visualizing AWS Cost On a Gantt Chart

Shailendra Singh — Tue, 27 Jan 2026 07:07:06 +0000

AWS provides extremely detailed billing data, yet many engineering teams still struggle to understand their cloud bill in a way that directly supports day-to-day operational decision-making. The problem is not lack of data but the abstraction level at which this data is presented. Billing dashboards are optimized for finance, not for engineering workflows, which creates a persistent disconnect between cost visibility and infrastructure ownership.

AWS Billing and Cost Management groups cost by service, region and pricing dimensions. This model works very well for accounting, compliance and chargeback. Engineers, however, do not think in terms of services and SKUs. They think in terms of resources such as EC2 instances, EBS volumes, environments, deployments and experiments.

When cost is detached from the lifecycle of these real infrastructure components, debugging it becomes slow, indirect and unnecessarily complex. In practice, this usually leads to CSV exports, spreadsheet pivot tables and long investigation threads where teams attempt to correlate cost spikes with infrastructure changes.

This workflow is cognitively expensive, fragile and poorly suited to modern cloud environments where resources are constantly created, modified and destroyed.

What if cloud cost looked like infrastructure activity instead of accounting data. What if engineers could analyze cost using the same mental models they already use for deployments, incidents and scaling events. That question led to the design of Cost Explorer (Gantt) in MechCloud.

The Missing Dimension: Time

Most cost dashboards focus on answering a single question: how much did we spend. While this is essential for financial reporting, it rarely helps engineers understand what actually happened inside their infrastructure.

For operational workflows, the more important question is often when exactly did we spend it and which resources were responsible for that spend. Without time as a first-class dimension, correlating infrastructure behavior and cost remains slow and error-prone.

Time is the missing dimension in most cost analysis tools. When cost is aligned with time, patterns emerge naturally. Short-lived workloads become immediately visible, zombie infrastructure stands out clearly and storage that continues charging after compute stops becomes impossible to ignore.

Cost spikes can be correlated directly with deployments, experiments and incidents without any manual data manipulation. Instead of static monthly aggregates, teams start seeing financial timelines of their infrastructure.

This shift transforms cost analysis from a retrospective accounting exercise into a real-time operational debugging workflow that fits naturally into how engineers already reason about distributed systems.

Why a Gantt Timeline Fits Infrastructure

Each cloud resource has a lifecycle with a clear start time, stop time and duration, and every unit of that runtime carries a measurable cost. When these lifecycles are viewed in isolation, it is difficult to build intuition about how infrastructure activity translates into financial impact.

However, when these lifecycles are placed on a timeline, cost becomes immediately intuitive and system-level patterns start to emerge. A Gantt timeline is a natural representation of this model.

By mapping resource lifecycles directly onto time, Cost Explorer (Gantt) presents a view where each row represents a real cloud resource and each bar represents its actual runtime. The length of the bar reflects duration while the bar itself carries the precise dollar cost for that time slice.

This allows engineers to visually correlate infrastructure behavior and financial impact without reasoning about services, SKUs or billing dimensions. The representation mirrors how engineers already debug systems using logs, metrics and traces, making cost analysis feel like a natural extension of existing operational workflows rather than a separate financial exercise.

A Real Debugging Scenario

Consider a mid-month AWS bill spike that suddenly appears in your cost dashboard. The traditional response is to export CSV files, build pivot tables and manually correlate timestamps with recent deployments.

This process is slow, fragile and often inconclusive, especially in fast-moving production environments. Even after hours of analysis, teams frequently end up with assumptions instead of certainty.

With a timeline view, the workflow changes completely. You scroll directly to the day the spike occurred and immediately see which resource started, scaled or expanded.

In many cases, the root cause becomes visible within minutes, turning cost debugging into a familiar engineering activity rather than a finance-driven investigation. This dramatically reduces investigation time and helps teams establish a direct feedback loop between infrastructure changes and financial impact.

The Operational Model Behind Cost Explorer (Gantt)

Cost Explorer (Gantt) is designed around a simple operational model. Cloud accounts are used to provision and discover infrastructure. Resource Contexts define logical boundaries that group related resources.

Stateless IaC provisions and manages infrastructure within those boundaries. Cost Explorer (Gantt) then visualizes cost using the same cloud accounts and Resource Contexts.

This model ensures that cost visibility always aligns with how teams design and operate their infrastructure. Instead of forcing engineers to translate between financial abstractions and operational abstractions, cost becomes a direct reflection of infrastructure behavior.

Step-by-Step: Visualizing AWS Cost Using Cost Explorer (Gantt)

This section walks through the complete workflow, starting from onboarding an AWS account and ending with visualizing resource-level cost timelines.

1. Add Your AWS Account

Start by connecting your AWS account to MechCloud using the standard onboarding workflow. This establishes a secure cross-account IAM integration that allows MechCloud to discover infrastructure metadata and retrieve cost information required for timeline visualization.

Follow this guide to complete the setup:

https://docs.mechcloud.io/cloud-computing/add-an-account/aws

Once the integration is complete, MechCloud can begin discovering resources and preparing historical and real-time data for timeline rendering.

2. Configure IAM Permissions

To enable accurate cost reconstruction and lifecycle correlation, the IAM role created during onboarding must include the following permission set.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "BillingReadOnlyPermissions",
      "Effect": "Allow",
      "Action": [
        "cloudtrail:LookupEvents",
        "tag:GetResources",
        "ec2:DescribeInstances",
        "ec2:DescribeVolumes",
        "ec2:DescribeRegions",
        "lightsail:GetInstances",
        "lightsail:GetBundles"
      ],
      "Resource": "*"
    }
  ]
}

These permissions allow MechCloud to discover both live and terminated resources, fetch volume metadata, resolve resource tags and build accurate historical timelines.

3. Create a Resource Context

Next, define a Resource Context that represents the logical boundary within which your infrastructure operates. A Resource Context is conceptually equivalent to a namespace in Kubernetes or a resource group in AWS and Azure and it is used to group related resources under a single operational scope.

This grouping ensures that infrastructure automation, visualization, and cost analysis remain aligned. It also allows teams to define environments such as production, staging and shared services independent of cloud account structure.

Follow this guide to configure the Resource Context:

https://docs.mechcloud.io/cloud-computing/stateless-iac/resource-context

4. (Optional) Provision Infrastructure Using Stateless IaC

With the AWS account and Resource Context in place, infrastructure can now be provisioned using Stateless IaC.

All resources created using Stateless IaC are logically bound to the selected Resource Context, ensuring that automation and visibility remain scoped correctly.

This step is optional if you already have existing infrastructure. For existing resources, all you need to do is add a tag named mc-resource-context with a resource context name defined in the previous step as value to manage these resources under the resource context.

5. Open Cost Explorer (Gantt)

Navigate to Infrastructure → Cost Explorer (Gantt) to access the timeline view. When the page loads, MechCloud does not automatically render any cost data. Instead, you are presented with a month selector and a Generate Cost Chart button, giving you explicit control over which billing period you want to analyze.

By default, the current month is preselected. You can change this to any of the previous two months using the dropdown.

Once the target month is selected, click on Generate Cost Chart to render the full Gantt timeline for that month.

Understanding the Timeline

Cost Explorer (Gantt) supports visualization of AWS cost for the last three months, including the current month. Only a single month can be visualized at a time, which allows engineers to focus on precise time windows when debugging cost behavior.

Use the month selector to switch between months, then click Generate Cost Chart to load the timeline for that billing period.

The horizontal axis represents time across the selected month, while each row represents a real cloud resource such as an EC2 instance or an EBS volume. Each bar represents a continuous runtime window, carrying the exact cost incurred during that time slice.

Cost Explorer (Gantt) supports visualization of AWS cost for the last three months, including the current month.

Only a single month can be visualized at a time, which allows engineers to focus on precise time windows when debugging cost behavior. Use the month selector to switch between months, then click Generate Cost Chart to load the timeline for that billing period.

The horizontal axis represents time across the selected month, while each row represents a real cloud resource such as an EC2 instance or an EBS volume.

Each bar represents a continuous runtime window, carrying the exact cost incurred during that time slice.

Live and Terminated Resources

Cost Explorer (Gantt) provides two viewing modes: Live Only and All Resources.

Live Only shows only currently active resources, making it ideal for understanding present-day cost footprint and identifying ongoing spend.

All Resources includes both active and terminated resources. Terminated resources are displayed using striked-out text, allowing historical infrastructure to be clearly distinguished from currently running workloads.

Month-to-Date and Estimated Monthly Cost

For the current month, Cost Explorer (Gantt) displays both month-to-date cost and estimated monthly cost.

Month-to-date cost represents actual accumulated spend so far. On the other hand, Estimated monthly cost projects total month-end spend if current usage patterns continue.

For historical months, both values are identical since billing data is already complete.

Understanding Tooltip Information

Hovering over any bar in the timeline displays a detailed tooltip containing start time, end time, duration, unit price and incurred cost.

For active resources in the current month, the tooltip also shows estimated monthly cost.

For terminated resources, only actual incurred cost is shown, since no further projection is required.

Why This Changes How Teams Think About Cloud Cost

When cost becomes a timeline, it stops being an abstract monthly number and starts resembling infrastructure activity.

Engineers gain direct visibility into how architectural decisions translate into financial impact, which fundamentally changes how teams approach optimization, debugging and accountability.

Over time, this creates a tighter feedback loop between system design, operational behavior and cost outcomes, leading to more intentional infrastructure choices and better long-term cost control.

Final Thoughts

Cloud cost should be understandable to the people who build and operate systems.

By converting AWS billing into a resource-level financial timeline, Cost Explorer (Gantt) gives engineers direct visibility into how architecture decisions translate into real cost.

This transforms cost analysis from a periodic accounting task into a continuous engineering workflow, enabling teams to operate their infrastructure with both technical clarity and financial clarity.

If you want to see it in action, here is a short walkthrough video: