DEV Community: Improving

Cost Optimization in Amazon ECS: Leveraging Spot Instances the Right Way

Improving — Wed, 18 Mar 2026 11:03:16 +0000

Cost efficiency is often as critical as performance and scalability. For modern containerized applications, the need to manage infrastructure costs becomes important, as microservices often translate to a large number of continuously running tasks. If not managed properly, these costs can spiral quickly.

We aren't just talking about a few extra dollars — we are talking about the kind of financial disaster where a team chose CloudWatch for a small project because it was "quick to set up," only to find it eating up 40% of their entire budget. Or another instance where a recursive loop in a Lambda Edge function caused their application to essentially DDoS itself through CloudFront.

"Basically, running on default is expensive."

For Amazon Elastic Container Service (ECS), the "default" is often to run every task on On-Demand or FARGATE capacity. While safe, it means you are paying a 70–90% premium for every single microservice, regardless of its priority.

In this post, we'll move past the fear of a surprise bill. We will explore how to build a high-reliability, cost-optimized engine using ECS Capacity Providers. You'll learn how to blend the guaranteed stability of On-Demand with the massive discounts of AWS Spot Instances so you can transform your computing spending from a risk into a strategic advantage.

Understanding ECS Launch Types

Before diving into Spot Instances, it's essential to understand the two fundamental Launch Types available for running tasks in ECS: EC2 and Fargate. These are the distinct compute models that determine how your containers are hosted and managed.

Running Tasks on EC2 Launch Type

With the EC2 launch type, we have full control over the underlying infrastructure. We provision and manage a cluster of EC2 instances that act as container hosts for our ECS tasks.

Running Tasks on Fargate Launch Type

Fargate is the serverless compute engine for containers. It removes the need for us to provision, configure, or scale clusters of virtual machines. We simply specify the CPU and memory required for our task, and Fargate handles the underlying infrastructure management.

Fargate vs. EC2

	EC2	Fargate
Infrastructure Management	You manage it	AWS manages it
Cost Control	Maximum control	Less granular
Spot Availability	EC2 Spot	Fargate Spot
Best For	Cost optimization, specialized instances	Simplicity, rapid deployment

When to choose which:

EC2 instance: When you need maximum cost control, have consistent resource utilization, or require specialized instance types. This is where you can realize the highest savings by aggressive use of Spot Instances.
Fargate instance: When simplicity, security isolation, and a rapid deployment model are priorities. While Fargate is premium-priced, you can still leverage a form of Spot via Fargate Spot.

Why Cost Optimization Matters in ECS

Running containerized workloads on AWS involves paying for the underlying compute resources, whether they are Amazon EC2 instances or AWS Fargate compute units. In an ECS environment, controlling this expenditure is key to maintaining a healthy operational budget.

Leveraging smart cost-saving mechanisms means we can run the same — or even larger — workloads for significantly less money, maximizing our return on investment (ROI).

Where Spot Instances Fit in the Cost Optimization

Cost optimization for containers often begins with choosing the right deployment model. Once we select the underlying compute, the next step is tapping into AWS's surplus capacity — the unused virtual machine capacity within an AWS Region — which is offered at a steep discount.

Spot Instances allow us to utilize this spare compute capacity in the AWS cloud, typically offering savings of up to 90% compared to on-demand prices. Such discounts are game changers for fault-tolerant and flexible ECS workloads.

Optimizing Cost with ECS on Spot

AWS offers two ways to leverage discounted Spot capacity for our ECS workloads.

Fargate Spot

Fargate Spot is a specialized version of Fargate that allows us to run interruptible Fargate tasks at a discount, similar to EC2 Spot Instances.

Pros: Serverless simplicity, instant provisioning, high savings (typically 70% off Fargate On-Demand).
Cons: Less granular control than EC2 Spot; not suitable for tasks that cannot tolerate interruption.

EC2 Spot Capacity Providers

Capacity Providers allow ECS to manage the scaling of the underlying EC2 Auto Scaling Group (ASG), automatically requesting and maintaining the desired capacity. We configure one or more ASGs (for On-Demand and Spot) and define a strategy for how tasks should be distributed across them. This is the most flexible and powerful mechanism for cost optimization in ECS.

Choosing the Right Spot Instance: Manual Data vs. Automated Selection

To successfully integrate EC2 Spot Instances, we must understand their interruptible nature. AWS can reclaim a Spot Instance with a two-minute warning if the capacity is needed elsewhere. The key is to select instance types that are less frequently interrupted and to diversify our fleet.

1. Manual Selection and Diversification using Spot Capacity Advisor

The initial step is to understand the core trade-offs: cost savings versus interruption risk.

The AWS EC2 Spot Instance Advisor is a vital tool for making informed decisions. It provides historical data on an instance type's saving potential and, critically, its Frequency of Interruption.

You might find that an instance type offering a slightly lower discount (e.g., 54% for c6a.2xlarge) is worth the trade-off for its <5% interruption rate, making it a more reliable choice for critical, cost-optimized workloads.

Reducing interruptions by diversifying capacity

For EC2 Spot instances, we must create a dedicated Auto Scaling Group (ASG) for our Spot fleet. Within this ASG, using a Mixed Instance Policy is critical for both cost and reliability.

Select Multiple Instance Types: Instead of relying on a single instance size (e.g., only c6a.4xlarge), the Mixed Instance Policy allows us to specify a mix of suitable instance families and sizes (e.g., c6a.2xlarge, c5.xlarge, c4.xlarge, etc.). This diversification is paramount — the loss of one type won't halt our cluster.
Use Different Availability Zones (AZs): Spread Spot requests across multiple AZs. Capacity availability varies by AZ, ensuring greater capacity stability.

2. Automated Selection with Attribute-Based Selection (ABS)

Manually listing a diverse set of instance types in ASG works, but managing that list becomes complex as AWS constantly releases new generations. Attribute-Based Instance Type Selection (ABS) provides a superior, future-proof approach.

ABS allows you to express your workload requirements (such as minimum/maximum vCPU, memory, networking bandwidth, and instance generation) rather than listing specific instance types.

How it helps Spot: ABS automatically translates your requirements into a vast list of hundreds of potential instance types. The massive diversification ensures your ASG can access the broadest possible pool of Spot capacity, dramatically lowering the risk of interruption.

Maintenance-Free: When AWS releases a new instance type (e.g., a new generation of C7 or M7), ABS automatically considers it for provisioning if it matches your specified attributes — meaning you never have to update your configuration manually.

Understanding Spot Allocation Strategies

When using a Mixed Instance Policy in our ASG, we must choose an allocation strategy that dictates how AWS fulfills our Spot capacity request across the specified instance types.

Strategy	Description	Best For
`lowest-price`	Fills from the cheapest pool(s) first	Maximum cost savings, higher interruption risk
`capacity-optimized`	Fills from the pool with the most available capacity	Lower interruption risk
`price-capacity-optimized`	Balances price and capacity availability	Recommended — best of both worlds

Capacity Provider Strategies

Capacity Provider Strategies are the engine behind flexible task provisioning. They allow us to define a logic for distributing tasks across our available capacity pools (e.g., On-Demand ASG and Spot ASG).

Baseline Reliability Strategy

The main idea for achieving both high reliability and significant cost savings simultaneously is:

Use On-Demand capacity to establish a reliable baseline.
Rely on Spot capacity only for dynamic scale-out.

This means a minimum number of critical ECS tasks are always running on guaranteed On-Demand compute. Only the tasks created as part of horizontal scaling or traffic surges are directed to the highly discounted, but interruptible, Spot Instances.

Base and Weight Explained

The strategy is composed of capacity providers, each with a base and a weight:

base: The minimum number of tasks that must run on a specific capacity provider. Tasks are placed on the base capacity provider before considering any weight distribution.
weight: The relative proportion of the remaining capacity that should be fulfilled by the associated capacity provider after the base is satisfied.

Example: Distributing 100 tasks

Given the following strategy:

Capacity Provider	base	weight
On-Demand	10	1
Spot	0	3

Here's how ECS places the tasks:

Fulfill the base: The first 10 tasks go to the On-Demand provider.
- Remaining tasks: 100 − 10 = 90
Apply weights to remaining tasks: Total weight = 1 + 3 = 4
- On-Demand (weight 1): 1/4 × 90 = ~23 tasks
- Spot (weight 3): 3/4 × 90 = ~67 tasks

Result: ~33 tasks on On-Demand, ~67 tasks on Spot — significant savings with a guaranteed baseline.

Cost vs. Reliability Tradeoff

Strategy	On-Demand %	Spot %	Reliability	Cost Savings
All On-Demand	100%	0%	Highest	None
High base, low weight on Spot	High	Low	High	Moderate
Low base, high weight on Spot	Low	High	Moderate	High
All Spot	0%	100%	Lowest	Maximum

Step-by-Step: Running ECS Workloads on Spot

Here's how to implement a high-reliability, cost-optimized strategy using Capacity Providers:

Create an ECS cluster with capacity providers: Define an ECS Cluster linked to two separate EC2 Auto Scaling Groups — one for On-Demand and one for Spot.
Configure Spot and On-Demand in the strategy: Define the Capacity Provider Strategy when creating an ECS service.
On-Demand Capacity Provider: Set a high base for guaranteed resources.
Spot Capacity Provider: Set a higher weight to ensure most flexible tasks land here.
Deploy the service: Run your ECS service referencing the defined Capacity Provider Strategy.

💡 You can explore a practical Terraform implementation of this setup on GitHub.

Final Words

Cost optimization within Amazon ECS is a continuous process, and mastering AWS Spot Instances is the most powerful lever for maximizing savings without sacrificing critical performance.

By adopting the right approach, we move beyond simply requesting the cheapest compute and embrace a strategic methodology:

Establishing a resilient baseline: Use the On-Demand base in the Capacity Provider Strategy to ensure the most critical ECS tasks are always running on guaranteed capacity.
Optimizing scale: Leverage a high Spot weight to ensure all scale-out tasks are launched on deeply discounted capacity, maximizing cost savings for dynamic workloads.
Enhancing stability: Mitigate interruptions by utilizing the Spot Capacity Advisor and diversifying the EC2 fleet through Mixed Instance Policies and intelligent allocation strategies like price-capacity-optimized.

Ultimately, leveraging ECS Capacity Providers with Spot Instances transforms infrastructure management from a high cost overhead into a strategic advantage — allowing your team to scale faster and smarter while maintaining excellent resilience.

Originally published on improving.com

Backup and Restore Kubernetes Resources Across vCluster using Velero

Improving — Wed, 18 Mar 2026 10:59:04 +0000

In Kubernetes environments, teams are constantly looking for ways to move faster without sacrificing security or efficiency. Managing multiple environments like development, testing, and staging often leads to cluster sprawl, higher costs, and complex maintenance. This is where virtual clusters come in.

Virtual clusters make it possible to create isolated, on-demand Kubernetes environments that share the same underlying infrastructure. They give developers the freedom to spin up their own clusters quickly for testing new features, running experiments, or deploying temporary workloads — all without waiting on cluster admins or consuming extra resources. Each virtual cluster runs its own control plane, offering stronger isolation and flexibility than namespace-based setups. We'll be using vCluster, an implementation of virtual clusters by Loft, to illustrate the concept in practice.

Managing workloads across multiple virtual clusters is a common pattern in multi-tenant environments. However, while virtual clusters make isolation easy, moving workloads across them is not straightforward. That's where Velero comes in — it is a powerful Kubernetes backup tool that migrates workloads from one virtual cluster to another.

In this blog post, we'll understand the importance of backups, how Velero works, and walk you through a practical migration of resources using Velero — from backing up one virtual cluster to restoring it in another.

What is Velero?

Velero is an open source tool to back up and restore your Kubernetes cluster resources and persistent volumes. You can run Velero with a cloud provider or on-premises.

Velero lets you:

Take backups of your cluster and restore in case of loss
Migrate cluster resources to other clusters
Replicate your production cluster to development and testing clusters

Velero consists of:

Velero CLI
- Runs on your local machine.
- Used to create, schedule, and manage backups and restores.
Kubernetes API Server
- Receives backup requests from the Velero CLI.
- Stores Velero custom resources (like Backup) in etcd.
Velero Server (BackupController)
- Runs inside the Kubernetes cluster.
- Watches the Kubernetes API for Velero backup requests.
- Collects Kubernetes resource data and triggers backups.
Cloud Provider / Object Storage
- Stores backup data and metadata.
- Creates volume snapshots using the cloud provider's API (e.g., Azure Disk Snapshots).

How it works:

User runs a Velero backup command using the CLI: velero backup create my-backup
CLI creates a backup request in Kubernetes
The Velero server detects the request and gathers cluster resources
Backup data is uploaded to cloud object storage
Persistent volumes are backed up using cloud snapshots (if enabled)

Velero supports a variety of storage providers for different backup and snapshot operations. In this blog post, we will focus on the Azure provider.

What is vCluster?

vCluster enables building virtual clusters — a certified Kubernetes distribution that runs as isolated, virtual environments within a physical host cluster. They enhance isolation and flexibility in multi-tenant Kubernetes setups. Multiple teams can work independently on shared infrastructure, helping minimize conflicts, increase team autonomy, and reduce infrastructure costs.

A virtual cluster:

Runs inside a namespace of the host cluster
Has an API server, control plane, and syncer
Maintains its own set of Kubernetes resources, operating like a full cluster

Why Backup and Migrate Workloads Using vCluster?

Common reasons to back up or migrate workloads between vClusters:

Promoting apps from dev to staging or prod: Backing up and restoring workloads between vClusters allows smooth promotion of applications across environments, ensuring consistent configurations and deployments without manual rework.
Replicating test environments: It helps recreate identical test setups quickly, enabling developers to reproduce issues, validate fixes, or test new features in isolated environments.
Disaster recovery (DR) setup: Regular backups across vClusters ensure business continuity by allowing workloads to be restored rapidly in another cluster if the primary one fails.
Tenant migration in multi-tenant environments: vClusters make it easier to move tenants between isolated environments without affecting others, maintaining data security and minimizing downtime.
Cluster version upgrades or deprecations: When upgrading or decommissioning a cluster, backing up workloads to another vCluster ensures a seamless transition without losing data or configurations.

Why Use Velero with vCluster?

Virtual clusters built with vCluster are lightweight and isolated, but they don't provide built-in mechanisms for backing up workloads, restoring them, or moving applications between clusters. Without a backup solution, recovery and migration can be risky.

Using Velero with vCluster fills this gap by enabling simple backup, restore, and migration workflows directly inside virtual clusters. It allows you to move applications between clusters with minimal setup and perform migrations with little to no downtime, especially for stateless workloads.

How to Backup and Migrate Workloads Between vClusters

Let's see how to use Velero to back up workloads from one vCluster and restore them into another. Think of it as moving your app from dev to staging across two clusters running on two different Azure clusters.

Prerequisites

Before starting, make sure you have the following:

Two clusters up and running on Azure (any cloud offering works)
Two running vClusters (source and destination)
Velero CLI installed on your machine

Step-by-step Guide

In the source vCluster and destination vCluster, we will install Velero with the same configuration, deploy a sample MySQL Pod, take its backup at source, and restore it in the destination vCluster. We will be using the Azure provider to run Velero.

To set up Velero on Azure, you have to:

Create an Azure storage account and blob container
Get the resource group details
Set permissions for Velero

Velero needs access to your Azure storage account to upload and retrieve backups. You'll need to assign the "Storage Blob Data Contributor" role (or equivalent) to the identity or service principal Velero uses, ensuring it can read, write, and manage backup data in the blob container.

1. Create Azure Resources

Create a resource group:

AZURE_RESOURCE_GROUP=<YOUR_RESOURCE_GROUP>
az group create --name $AZURE_RESOURCE_GROUP --location <YOUR_LOCATION>

Create the storage account:

AZURE_STORAGE_ACCOUNT=<YOUR_STORAGE_ACCOUNT>
az storage account create \
  --name $AZURE_STORAGE_ACCOUNT \
  --resource-group $AZURE_RESOURCE_GROUP \
  --sku Standard_GRS \
  --encryption-services blob \
  --https-only true \
  --kind BlobStorage \
  --access-tier Hot

Create a blob container:

BLOB_CONTAINER=velero
az storage container create \
  --name $BLOB_CONTAINER \
  --public-access off \
  --account-name $AZURE_STORAGE_ACCOUNT

2. Create a Service Principal with Contributor Privileges

AZURE_SUBSCRIPTION_ID=$(az account list --query '[?isDefault].id' -o tsv)
AZURE_TENANT_ID=$(az account list --query '[?isDefault].tenantId' -o tsv)

az ad sp create-for-rbac \
  --name "velero" \
  --role "Contributor" \
  --scopes /subscriptions/$AZURE_SUBSCRIPTION_ID \
  --query '{clientId: appId, clientSecret: password, tenantId: tenant}'

This outputs clientId, clientSecret, subscriptionId, and tenantId. Store these values.

Get the Client ID and store it in a variable:

AZURE_CLIENT_ID=$(az ad sp list --display-name "velero" --query '[0].appId' -o tsv)

Assign additional permissions to the Client ID:

az role assignment create \
  --assignee $AZURE_CLIENT_ID \
  --role "Storage Blob Data Contributor" \
  --scope /subscriptions/$AZURE_SUBSCRIPTION_ID/resourceGroups/$AZURE_RESOURCE_GROUP/providers/Microsoft.Storage/storageAccounts/$AZURE_STORAGE_ACCOUNT

3. Prepare Credentials

With the output received above, create bsl-creds and cloud-creds for the Velero setup.

BSL (Backup Storage Location) — the blob container where Velero stores backups. Velero needs a secret to access this storage location.
cloud-creds — credentials required to access the Azure cluster.

You will need the following values:

AZURE_SUBSCRIPTION_ID=<YOUR_SUBSCRIPTION_ID>
AZURE_TENANT_ID=<YOUR_TENANT_ID>
AZURE_CLIENT_ID=<YOUR_CLIENT_ID>
AZURE_CLIENT_SECRET=<YOUR_CLIENT_SECRET>
AZURE_RESOURCE_GROUP=<YOUR_RESOURCE_GROUP>
AZURE_CLOUD_NAME=AzurePublicCloud
AZURE_ENVIRONMENT=AzurePublicCloud

4. Log in to vCluster and Create Velero Namespace

kubectl create namespace velero

5. Create BSL and Cloud Credentials

bsl-creds.yaml:

apiVersion: v1
kind: Secret
metadata:
  name: bsl-creds
  namespace: velero
type: Opaque
data:
  cloud: <BASE64_ENCODED_VALUE>
  # Encode the following as base64:
  # [default]
  # storageAccount: <YOUR_STORAGE_ACCOUNT>
  # storageAccountKey: <YOUR_STORAGE_ACCOUNT_KEY>
  # subscriptionId: <YOUR_SUBSCRIPTION_ID>
  # resourceGroup: <YOUR_RESOURCE_GROUP>

cloud-creds.yaml:

apiVersion: v1
kind: Secret
metadata:
  name: cloud-creds
  namespace: velero
type: Opaque
data:
  cloud: <BASE64_ENCODED_VALUE>
  # Encode the following as base64:
  # AZURE_SUBSCRIPTION_ID=<YOUR_SUBSCRIPTION_ID>
  # AZURE_TENANT_ID=<YOUR_TENANT_ID>
  # AZURE_CLIENT_ID=<YOUR_CLIENT_ID>
  # AZURE_CLIENT_SECRET=<YOUR_CLIENT_SECRET>
  # AZURE_RESOURCE_GROUP=<YOUR_RESOURCE_GROUP>
  # AZURE_CLOUD_NAME=AzurePublicCloud

Apply the secrets:

kubectl apply -f bsl-creds.yaml -n velero
kubectl apply -f cloud-creds.yaml -n velero

6. Install Velero Using Helm

Use the following values.yaml. Both the source and destination vClusters use the same file:

configuration:
  backupStorageLocation:
    - name: default
      provider: azure
      bucket: velero
      config:
        resourceGroup: <YOUR_RESOURCE_GROUP>
        storageAccount: <YOUR_STORAGE_ACCOUNT>
        subscriptionId: <YOUR_SUBSCRIPTION_ID>
      credential:
        name: bsl-creds
        key: cloud

  volumeSnapshotLocation:
    - name: default
      provider: azure
      config:
        resourceGroup: <YOUR_RESOURCE_GROUP>
        subscriptionId: <YOUR_SUBSCRIPTION_ID>
      credential:
        name: cloud-creds
        key: cloud

credentials:
  useSecret: true
  existingSecret: cloud-creds

deployNodeAgent: true

nodeAgent:
  podVolumePath: /var/lib/kubelet/pods
  privileged: true

Install the Helm chart:

helm install velero vmware-tanzu/velero \
  --namespace velero \
  -f values.yaml

Once installed, you will see velero and node-agent pods running in the velero namespace:

kubectl get pods -n velero

Repeat the same Velero installation steps in the destination vCluster.

Backup and Restore a Sample MySQL Pod

Deploy MySQL in Source vCluster

mysql-pod.yaml:

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: mysql-pvc
  namespace: default
spec:
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 1Gi
---
apiVersion: v1
kind: Pod
metadata:
  name: mysql-pod
  namespace: default
  labels:
    app: mysql
spec:
  containers:
    - name: mysql
      image: mysql:8.0
      env:
        - name: MYSQL_ROOT_PASSWORD
          value: rootpassword
        - name: MYSQL_DATABASE
          value: testdb
      volumeMounts:
        - name: mysql-storage
          mountPath: /var/lib/mysql
  volumes:
    - name: mysql-storage
      persistentVolumeClaim:
        claimName: mysql-pvc

Apply the manifest:

kubectl apply -f mysql-pod.yaml

Add Test Data

Exec into the pod:

kubectl exec -it mysql-pod -- /bin/bash

Run the following commands inside the pod to add test files:

echo "test data 1" > /var/lib/mysql/test1.txt
echo "test data 2" > /var/lib/mysql/test2.txt

This creates test1.txt and test2.txt.

Take a Backup

velero backup create mysql-backup \
  --include-namespaces default \
  --default-volumes-to-fs-backup \
  --wait

Check backup status:

velero backup get

The backup status should show Completed.

Restore in Destination vCluster

Update values.yaml for Destination

Make sure the Velero config is the same as the source. Use the same values.yaml, but update these two parameters:

# Change these in values.yaml for destination cluster
configuration:
  backupStorageLocation:
    - name: default
      # Keep all values the same as source — point to the same blob container
      accessMode: ReadOnly   # Destination reads from source's storage

After Velero is installed at the destination vCluster, verify you can see the source backups:

velero backup get

You will see the same backup list as the source vCluster.

Create a Restore

restore.yaml:

apiVersion: velero.io/v1
kind: Restore
metadata:
  name: mysql-restore
  namespace: velero
spec:
  backupName: mysql-backup
  includedNamespaces:
    - default
  restorePVs: true
  itemOperationTimeout: 4h

Apply the restore:

kubectl apply -f restore.yaml -n velero

Check restore status:

velero restore get
velero restore describe mysql-restore --details

To verify the restore, attach the PVC (created after restore completes) to a pod, exec into it, and confirm the data (test1.txt and test2.txt) is present.

Troubleshooting Tips

Issue 1: Backup status is `PartiallyFailed` or `FailedValidation`

Solution: Describe the backup for details:

velero backup describe mysql-backup --details

Check the backup logs:

velero backup logs mysql-backup

If nothing useful appears, check the Velero pod logs:

kubectl logs -n velero deployment/velero | grep mysql-backup

After running the above three commands, you'll likely find the root cause. Common causes include permission issues or incorrect credentials. Sometimes partial failures occur because the node-agent pod isn't running on a node — in that case, manually schedule a pod on that node.

Issue 2: Node Agent Pod is Not Running

node-agent-xxxxx   0/1   Pending   0   5m

Solution: There is a node with no pods running on it, so the node-agent DaemonSet pod is also not scheduled. Manually schedule a sample pod on that node to trigger scheduling. Once a sample pod is running, the node-agent pod will also be scheduled and start running.

Issue 3: Restore Fails Without Specific Errors

Solution: Restart the restore process from scratch:

Delete all resources created by the restore job (pods, statefulsets, deployments, PVCs, etc.)

OR

If restoring a whole namespace, delete the entire restored namespace.
Delete the restore job:

velero restore delete mysql-restore

After the restore job is deleted, ArgoCD (if used) will automatically sync and recreate the restore job, triggering the Velero restoration.

Conclusion

Using Velero to back up and restore workloads across vClusters provides a robust and flexible approach for managing multi-tenant Kubernetes environments. Whether you're migrating applications between development and production, setting up disaster recovery, or replicating environments for testing, Velero simplifies the process significantly.

In this blog post, we explored how to back up and restore Kubernetes clusters using Velero. While the process is straightforward in principle, production environments can introduce added complexity — factors like cluster size, workloads, and configurations often make a difference.

Originally published at improving.com

When MCP Is Not The Right Choice

Improving — Wed, 18 Mar 2026 10:54:59 +0000

Model Context Protocol (MCP) has quickly moved from concept to conversation starter across the AI engineering community. The concept is promising — give your AI models structured access to real tools and watch them transform from chatbots into agents that get work done.

But introducing MCP introduces real complexity, costs, and risks that don't appear in the initial stage. It's powerful when your users need it, and expensive over-engineering when they don't. In this post, we'll cut through the hype to examine the trade-offs that matter in production: when benefits of MCP justify the costs, where simpler approaches work better, and what hidden challenges emerge once you move past the POC phase.

What is MCP and an MCP Server?

MCP is an emerging standard that helps large language models (LLMs) interact with external tools, services, and data in a consistent and predictable way. In simple terms, MCP gives AI models a common language for using tools.

Think of it like a universal plug adapter for AI. Instead of teaching every model how to talk to every API or database separately, MCP defines one standard way to do it. Once a tool is connected through MCP, different AI models can use it without needing custom integrations each time.

An MCP Server runs this protocol and acts as a middle layer between AI models and real-world systems like APIs, databases, or internal apps. Developers define tool connections once on the MCP server and can then reuse them across models from different providers, saving time and reducing duplicated work.

The architecture at a high level: the LLM talks to the MCP server using the MCP protocol, and the MCP server handles communication with the actual tools and data sources behind the scenes.

Benefits of Adding MCP Servers to Your Software

MCP servers provide a durable architectural layer that helps organizations scale AI capabilities without locking into specific models or vendors. They shift AI integrations from short-term hacks to long-term infrastructure.

Standardization and Interoperability

MCP introduces a unified, model-agnostic protocol for accessing tools and resources, allowing AI systems to interact with enterprise data and services through a consistent interface. This abstraction decouples AI applications from individual model providers, allowing organizations to integrate new models or switch providers without rewriting downstream integrations.

Developer Velocity and Resource Efficiency

By separating model reasoning from tool execution, MCP simplifies system design and reduces integration complexity. Tools implemented once on an MCP server can be reused across multiple applications, models, and teams — eliminating duplicated effort and accelerating delivery of new AI capabilities. Over time, this reuse compounds: each new tool becomes shared infrastructure, increasing overall development efficiency and lowering marginal costs for future AI initiatives.

Centralized Control and Governance

An MCP server provides a single point of control for managing tool behavior, permissions, updates, and access policies across all AI clients. This centralization makes it easier to enforce compliance requirements, maintain audit trails, and implement consistent security controls — while supporting multi-client and multi-model architectures.

Architectural Flexibility for Growth

MCP enables organizations to add, modify, or remove tools without redeploying AI applications, reducing operational risk and increasing adaptability. As business needs, workflows, and regulatory environments change, the architecture can evolve without costly rewrites. MCP becomes a durable foundation that grows alongside an organization's AI maturity.

Hidden Costs: What MCP Adoption Really Means

While MCP promises elegant AI-tool integration, the path from proof-of-concept to production adds operational, performance, and organizational complexity that teams must be prepared to absorb.

Operational Burden and Complexity Tax

An MCP server is not a thin abstraction layer — it is a long-lived distributed system. It requires deployment pipelines, configuration management, backward-compatible schema evolution, and capacity planning. Unlike one-off integrations, MCP introduces ongoing responsibilities that scale with usage, appearing gradually during incident handling and dependency changes.

Performance Trade-offs

Introducing MCP adds an extra network hop for each tool invocation, often in the range of tens to hundreds of milliseconds, which can compound noticeably in multi-step or agentic workflows. Under high load, the MCP server can become a bottleneck if not properly scaled, cached, or tuned. Achieving acceptable performance typically requires additional engineering investment in concurrency management, caching strategies, and performance monitoring.

Security Risks if Misconfigured

MCP centralizes access to powerful tools and sensitive data, which increases the blast radius of configuration errors. Overexposed tools or overly permissive schemas can lead to unintended data access, while prompt-driven misuse can cause models to invoke tools in unsafe ways. Without carefully designed permission models, input validation, and guardrails, misconfigurations can be exploited either accidentally or maliciously.

A Nascent Ecosystem

MCP is still an evolving standard, with fewer mature, off-the-shelf tools compared to traditional API ecosystems. Best practices, architectural patterns, and operational playbooks are still emerging — which increases uncertainty and experimentation costs. For simple or single-purpose integrations, MCP may introduce more complexity than value.

Debugging and Observability Challenges

Failures in an MCP-based system often span multiple boundaries: model reasoning, protocol translation, network calls, and downstream services. Non-deterministic LLM behavior makes issues harder to reproduce and diagnose, increasing mean time to resolution. Effective operation requires sophisticated observability infrastructure — logging, tracing, and metrics — adding further tooling and operational investment.

When MCP Is the Wrong Choice: Critical Red Flags

Customer-Facing Latency Sensitivity

MCP introduces per-call overhead that degrades real-time UI experiences, where streaming connections amplify delays in interactive workflows. Transactional paths suffer from routing everything through the protocol, as burst requests from LLMs overwhelm simpler direct APIs. Sidecar integrations or non-blocking patterns deliver better responsiveness here.

Minimal Tool or Static Integrations

Stable, limited tools lead to bloated schemas repeated across interactions, wasting context without delivering dynamic benefits. Direct function calls or basic RAG pipelines handle these more efficiently. Short sessions accumulate unnecessary history, favoring prompt-level optimizations over protocol layers.

Regulated or Enterprise Security Gaps

Absence of built-in SSO, audit trails, and fine-grained authorization leaves regulated setups vulnerable to unmonitored shadow servers and injection risks in containerized deployments. Tool poisoning enables scope overrides, requiring custom gateways beyond the core spec.

Immature Teams or Shadow Deployments

When servers are set up without clear ownership or rules, it leads to inconsistent configurations, poor visibility, and slower troubleshooting. Teams without platform discipline may find that MCP increases complexity instead of improving efficiency. For smaller or early-stage use cases, simple direct LLM API calls are usually enough. You don't need full orchestration until your AI usage becomes more central and complex.

AI as a Peripheral Feature

If AI is just an occasional enhancement — like "adding a chatbot to a settings page" — MCP's architecture is overkill. In these cases, a simple call to your LLM provider's API with some context from your database is enough. You don't need servers, tool schemas, or protocol layers. MCP only makes sense when AI needs to orchestrate multiple tools or capabilities.

Decision Framework for Adopting MCP Servers

Complexity Assessment

Begin by assessing both current and anticipated AI requirements, including the number of models, tools, integrations, and teams involved. The key question is whether complexity is already causing friction or is credibly projected based on the roadmap — rather than being hypothetical. MCP introduces an abstraction layer, so ask yourself: does this layer solve a real coordination, scaling, or governance problem, or does it simply add unnecessary infrastructure?

Team Capability Audit

Evaluate whether your organization has the platform engineering maturity required to implement and operate an MCP server effectively. This includes operational capabilities such as monitoring, incident response, versioning, and access control — as well as a realistic skills gap analysis around distributed systems and API design. MCP can create long-term leverage, but only if the team can properly build, maintain, and evolve the platform without becoming a bottleneck.

Total Cost of Ownership (TCO) Calculation

Look beyond initial implementation costs to understand the full TCO over time. This should include migration effort, infrastructure and operational overhead, training or hiring costs, and opportunity costs. Weigh these against benefits in your specific context: reduced rework, faster delivery, improved governance, and increased vendor optionality.

Strategic Alignment

Assess whether MCP aligns with your broader business and AI strategy. Vendor optionality is most valuable when AI is central to your product or operating model, or when regulatory, cost, or performance considerations may force provider changes. Consider your risk tolerance for adopting an emerging standard and whether MCP supports your long-term AI roadmap rather than short-term experimentation.

Pilot Before Commitment

Before committing broadly, start with a constrained pilot using a non-critical application and a limited set of tools. This allows teams to validate assumptions, uncover operational challenges, and measure real-world benefits in their environment.

Common Pitfalls Organizations Fall Into

Exposing Overly Powerful Tools

A frequent mistake is exposing broad, high-privilege tools to models instead of narrowly scoped capabilities. This increases the risk of unintended actions, data leakage, or destructive operations — especially when models behave unpredictably or are influenced by ambiguous prompts.

Treating MCP As a Security Boundary By Itself

MCP is an integration protocol, not a security control. Relying on it as the sole line of defense — without downstream authorization, validation, and rate limiting — creates a false sense of safety and leaves systems vulnerable to misuse or exploitation.

Skipping Monitoring and Logging

Without comprehensive logging and monitoring, MCP-driven systems become opaque and difficult to debug. Teams often underestimate how essential visibility is for understanding tool usage, diagnosing failures, and responding quickly to incidents in non-deterministic AI workflows.

Allowing Unrestricted Model Access to Production Systems

Giving models direct, unrestricted access to production resources dramatically increases operational risk. Safe architectures enforce environment boundaries, approval gates, and least-privilege access — ensuring that models cannot independently execute high-impact actions without safeguards.

Conclusion

While MCP servers offer powerful capabilities for connecting AI models to tools and data, they also introduce trade-offs in complexity, performance, and operational overhead. Using them indiscriminately adds unnecessary costs and security risks — MCP may not be the right choice for every application. Success depends on careful design, strong security, and platform engineering maturity.

Organizations should evaluate MCP adoption based on their specific use cases, weighing benefits against operational and architectural costs. When in doubt, consult experts before making the decision.

Originally published on Improving.com

End-to-End Observability with Prometheus, Grafana, Loki, OpenTelemetry and Tempo

Improving — Wed, 18 Mar 2026 10:43:47 +0000

Observability provides complete insights into the health, performance, and behavior of your Kubernetes cluster and the applications deployed within it. Companies, whether or not they use Kubernetes, have leveraged open-source observability tools like Prometheus, Grafana, Loki, and OpenTelemetry (OTel) to achieve significant improvements in cost, efficiency, and incident response.

For example, companies that reduced observability costs with OpenTelemetry reported notable savings — 84% of these companies saw at least a 10% decrease in costs. A real-world case study shows how Loki helped Paytm Insider save 75% of logging and monitoring costs. Similarly, a 2025 survey by Apica found that nearly half of organizations (48.5%) are already using OpenTelemetry, with another 25.3% planning implementation soon.

Why Observability is Important

Observability — which uses logs, metrics, and traces to provide deep system insights — is particularly crucial for navigating the complexity of modern cloud-native and microservices-based architectures. It helps organizations reduce downtime, increase efficiency, improve developer productivity, and boost revenue.

The setup combining Prometheus, Grafana, Loki, Tempo, Kube-State-Metrics, Node Exporter, and OpenTelemetry offers an open-source alternative to the ELK stack (Elasticsearch, Logstash, and Kibana), providing seamless integration across metrics, logs, and traces. It scales from local development (Minikube) to enterprise-grade clusters, making it cost-effective and easy to adopt.

In this blog post, we will understand the open source observability setup and deploy it. At the end, we'll deploy a sample Java application to demonstrate how to collect logs, metrics, and traces in action.

Understanding the Observability Setup

Let's dive into the observability setup and clearly understand the role of each component.

Prometheus: A time-series monitoring system used to collect metrics from Kubernetes components and services. It supports powerful querying and alerting.
Kube-State-Metrics: An add-on service that generates detailed metrics about the state of Kubernetes objects like deployments, pods, and nodes. These metrics are consumed by Prometheus.
Node Exporter: A Prometheus exporter that exposes hardware and OS metrics from your Kubernetes nodes.
Grafana: A visualization and analytics tool that connects to Prometheus and other data sources to display real-time dashboards for your metrics.
Loki: A log aggregation system from Grafana Labs that works seamlessly with Prometheus and Grafana. It collects logs from your Kubernetes workloads and enables easy correlation with metrics.
Tempo: A distributed tracing backend used to collect and visualize traces. It helps in tracking requests as they flow through different services, enabling root-cause analysis.
OpenTelemetry (OTel): A collection of tools, APIs, and SDKs for collecting telemetry data (traces, metrics, and logs) from your applications. It standardizes observability data collection.

Prerequisites

Minikube — used to set up a local Kubernetes cluster
Helm — the package manager for Kubernetes
App Repo — the test application we will clone

Step 1: Installing Prometheus

Once you clone the repository, change directory to the observability folder and run the command below. A Prometheus Helm chart with custom config is included to get labels of all the applications to be deployed in Minikube.

Note: The ConfigMap is configured to enable a limited set of metrics, but you can enable any metrics from the Prometheus configuration docs as required.

helm upgrade --install prometheus prometheus-helm

Step 2: Install kube-state-metrics and Node Exporter

helm install kube-state-metrics prometheus-community/kube-state-metrics
helm install node-exporter prometheus-community/prometheus-node-exporter

Once both steps are completed successfully and the pods are up and running, verify that all targets are green in Prometheus by port-forwarding the service:

kubectl port-forward service/prometheus-service -n monitoring 9090:9090

Then, access Prometheus at http://localhost:9090.

To confirm metrics are populating, run the following queries:

kube_pod_info
node_cpu_seconds_total

Step 3: Installing Grafana

helm install grafana grafana/grafana --namespace monitoring

After the Grafana pods are in the Running state, port-forward the Grafana service and retrieve the login credentials from the Grafana secret.

Access the UI at http://localhost:3000, then use the fetched credentials to log in.

Navigate to Connections → Data Sources → Add data source. Set the name to prometheus and the connection URL to:

http://prometheus-service.monitoring.svc.cluster.local:9090

Save and exit.

To verify the metrics, go to the Explore section and run the query below. You will see a time series showing the memory utilisation of all running pods:

avg(container_memory_usage_bytes{pod=~".*"}) by (pod) / (1024 * 1024)

Step 4: Install Loki and Tempo

Run the following commands and wait until all pods are in the Running state:

helm upgrade --install loki -f loki.yaml grafana/loki-stack --namespace monitoring
helm upgrade --install tempo -f tempo.yaml grafana/tempo --namespace monitoring

📄 Note: You can find loki.yaml and tempo.yaml in the Git repository. Promtail in the Loki configuration allows you to parse log lines into labels. Refer to the Promtail stages docs on how to extract labels.

Once the pods are ready, follow the same steps used for Prometheus to add Loki and Tempo as data sources in Grafana:

Loki URL: http://loki.monitoring.svc.cluster.local:3100
Tempo URL: http://tempo.monitoring.svc.cluster.local:3100

To view logs, go to Explore in Grafana, select Loki as the datasource, and run the following query to fetch logs from all namespaces:

{namespace=~".+"} |= ``

Step 5: Install OpenTelemetry and Sample Application

Run the following commands to install OpenTelemetry:

helm repo add open-telemetry https://open-telemetry.github.io/opentelemetry-helm-charts
helm upgrade --install opentelemetry-collector open-telemetry/opentelemetry-collector \
  --namespace monitoring

Once the OpenTelemetry pods are in the Running state, update the sample application's Helm chart to include an init container for trace collection.

To deploy the application, run:

helm upgrade --install calc helm-chart/ --namespace monitoring

In the deployment.yaml file of the Helm chart, you'll find the following init container configuration:

initContainers:
  - name: opentelemetry-auto-instrumentation
    image: ghcr.io/open-telemetry/opentelemetry-operator/autoinstrumentation-java:latest
    command: ["cp", "/javaagent.jar", "/otel-auto-instrumentation/javaagent.jar"]
    volumeMounts:
      - mountPath: /otel-auto-instrumentation
        name: opentelemetry-auto-instrumentation

To generate traces, port-forward the application's service and interact with the app using some inputs to generate trace data. To view traces, navigate to the Explore page in Grafana, select Tempo as the datasource, and run the query:

{}

Why Use This Stack Over ELK?

All these tools together provide a modern, cloud-native, cost-efficient, and tightly integrated observability solution compared to the traditional ELK stack. Key advantages include:

Native support for metrics, logs, and traces: A unified experience and correlation across telemetry types (ELK is primarily log-centric).
Lower resource & storage cost: Loki indexes only metadata (labels), not full log content, making it lighter and cheaper to operate.
Better scalability & resilience in cloud/Kubernetes environments: These tools are built for distributed, elastic infrastructure.
OpenTelemetry compatibility & vendor neutrality: Instrumentation is portable and standards-based.
Operational simplicity & lower overhead: Fewer cluster tuning demands, simpler scaling, and less JVM burden compared to Elasticsearch.

Final Words

You cannot fix what you cannot see. With the sheer amount of data and complexity in modern tech, having a proper observability system in place is critical. The primary aim of this guide was to establish full-stack observability for a Kubernetes cluster by enabling metrics, logs, and traces using Prometheus, Loki, Tempo, and OpenTelemetry — and finally visualizing them with Grafana.

With this setup, you can now monitor, visualize, and troubleshoot applications in real time using metrics, logs, and traces all in one unified observability stack. This not only enhances visibility into the cluster's health and performance but also enables faster root cause analysis and proactive incident response, aligning with modern DevOps and SRE practices.

AI Strategy & Roadmap Assessment: How Enterprises Avoid 88% AI Failure

Improving — Thu, 12 Feb 2026 09:01:54 +0000

Enterprises across industries are investing heavily in AI to improve decision-making, automate complex workflows, and unlock new sources of value. Most organizations today have little difficulty identifying AI use cases or launching initial pilots. The real challenge emerges later, when those experiments need to integrate into core systems, operate under real-world constraints, and deliver measurable business outcomes at scale.

This challenge plays out consistently across sectors:

Healthcare: AI diagnostic tools struggle when privacy, compliance, and audit requirements are not built in from day one.
Financial services: Fraud detection and risk models stall when regulators require transparency and explainability that were never planned for.
Manufacturing: Predictive maintenance pilots often succeed in controlled environments, only to fail when connected to legacy systems and operational realities.

The Scale of the Problem

The data reflects this challenge clearly:

42% of enterprise-scale companies already have AI in production (IBM), and another 40% are actively piloting initiatives.
88% of AI proof-of-concepts never reach production (MIT, IDC).
95% of enterprise AI solutions fail due to data issues (MIT, IDC).
77% of companies are exploring AI, but only 20% achieve significant ROI (McKinsey).

These outcomes stem from repeatable mistakes:

Starting with technology instead of business problems
Underestimating data quality and governance requirements
Treating AI as an isolated IT initiative
Deferring MLOps and production planning
Relying on strategy that lacks execution depth

The difference between success and failure is not ambition or budget, but how AI strategy is approached from the beginning.

What Is an AI Strategy & Roadmap Assessment?

An AI Strategy & Roadmap Assessment is a structured engagement that helps organizations understand where AI can deliver real business value and how to implement AI responsibly at scale.

Rather than jumping straight into tools or models, the assessment evaluates:

Business goals
Data readiness
Technology foundations
Governance requirements
Organizational maturity

Outcome: A clear AI strategy aligned to business priorities, paired with a phased roadmap outlining what to build, when to build it, and what capabilities are required at each stage.

AI Strategy Assessment Engagement Models

Organizations have different needs depending on their AI maturity.

AI/ML Discovery Engagement (2–4 Weeks)

Best for: Organizations exploring AI potential or validating initial use cases

Investment: $25,000+

What's Included

Structured workshops to identify high-ROI AI opportunities
Assessment of data quality, technology readiness, and organizational capabilities
Feasibility analysis for priority use cases with ROI estimates
Phased implementation roadmap with timelines and resource requirements
Skills gap analysis and training recommendations

Deliverables

Prioritized AI use case portfolio
Technology readiness scorecard
Strategic roadmap with success metrics

AI-Driven Organizational Role Assessment (4 Weeks per Department)

Best for: Organizations preparing for AI-driven workforce transformation

AI excels at “collapsible tasks” — work completed in a fraction of the usual time. When tasks taking 8 hours can be completed in 2 hours using AI (75% reduction), organizations must plan for capacity reallocation and role evolution.

Dual-Coach Approach

Process Coach: Evaluates workflows and identifies optimization opportunities
Technology Coach: Assesses AI and automation feasibility

Assessment Focus

Identify tasks where AI achieves ≥75% time savings
Determine whether acceleration creates new demand or reduces resources needed
Design role evolution paths with upskilling requirements
Plan workforce capacity reallocation

Roles most impacted: Payroll processing, quality assurance, administrative coordination, sales operations, software development.

Deliverables

Role-by-role AI impact analysis
Workforce reallocation recommendations
Upskilling roadmap
Change management plan

Why Most AI Strategies Fail

The Hard Numbers Behind AI Failure

88% of AI proof-of-concepts never reach production
56% of organizations remain stuck in “pilot purgatory”
95% of failures stem from data issues
18–24 months wasted on failed pilots
$500,000–$3 million lost per failed initiative

Common Causes

Solving for technology instead of business problems
Spending 60–80% of time on data preparation while budgeting only 20–30%
Treating AI as an IT-only initiative
Skipping MLOps until after models fail
Hiring strategy firms without implementation capability

What Separates Success from Failure

Organizations that scale AI successfully share three traits:

Engineering-backed strategy
Data-first approach
Production mindset from day one

How a Successful AI Strategy & Roadmap Assessment Works

Business & Use-Case Discovery
AI & Data Readiness Assessment
Technology & Architecture Evaluation
Governance & Risk Analysis
Roadmap & Execution Planning

Data Readiness: The Foundation of AI Strategy

Before any AI strategy can succeed, organizations must confront data reality.

Five Data Readiness Questions

Can we access required data in real time or near real time?
What percentage meets AI quality standards?
Do we have documented governance policies?
Can our infrastructure support AI workload volume and velocity?
Have we defined regulatory and compliance standards (GDPR, HIPAA, etc.)?

From Pilot to Production: The AI Validation Journey

Proof-of-Concept Best Practices (4–8 Weeks)

Well-designed PoCs answer:

Technical feasibility
Data sufficiency
Integration viability

The Scale-Up Framework

Infrastructure transition
Data pipeline industrialization
MLOps implementation
Governance activation
Organizational change management

How to Measure AI Strategy Success

Time-to-Value Metrics

30–45 days from strategy to first PoC
30–60 days PoC-to-pilot
6–9 months target for production deployment

Business Impact Metrics

15–20% cost reduction
3–8% revenue increase
26–55% productivity improvement
10–20% improvement in customer satisfaction

Financial Benchmarks

ROI >150% within 18–24 months
Payback <12 months (operational AI)
Payback <18 months (customer-facing AI)

Red Flags to Avoid

Strategy-only firms without engineering capability
One-size-fits-all frameworks
No industry-specific references
Overselling AI as universal solution
Ignoring failure statistics
Proprietary platform lock-in
Unrealistic timelines

11 Common AI Strategy Mistakes

Starting with technology instead of business problems
Underestimating data quality requirements
Ignoring change management
Running too many pilots
Choosing strategy-only consultants
Skipping governance planning
Neglecting MLOps infrastructure
Underinvesting in talent development
Expecting immediate ROI
Treating AI as an IT-only initiative
Overlooking user-centric design

AI Strategy Trends to Watch in 2026

Agentic AI and autonomous systems
AI governance as regulatory requirement
Small language models and edge AI
AI-accelerated software development
Multimodal AI integration
AI cost optimization with FinOps controls
Platform engineering for AI
Operationalized Responsible AI
AI-driven workforce transformation

Final Words

AI adoption is not simply about selecting the right models or tools. It is a strategic transformation in how organizations use data, infrastructure, governance, and operations to create measurable business impact.

Organizations that succeed:

Start with clear business objectives
Assess data and technology readiness early
Plan for production and scale from day one

A structured AI Strategy & Roadmap Assessment reduces risk, accelerates deployment, and increases the probability of measurable ROI.

Offshore Engagement Models: 7 Options Compared for Cost & Risk

Improving — Mon, 09 Feb 2026 08:44:52 +0000

Offshore Engagement Models: Choosing the Right Fit for Scalable Software Delivery

A mismatch between business expectations and the selected IT engagement model often leads to cost overruns, delays, or quality issues. Choosing the right engagement model plays a critical role in offshore software development success.

Engagement models in software development define how teams collaborate, share responsibility, and manage risk. A well-aligned offshore development model bridges geographical distance and ensures offshore partners deliver predictable and scalable outcomes.

In this article, we provide a practical breakdown of when each offshore engagement model works, when it fails, and how to avoid common contract traps.

What Is an Offshore Development Model?

An offshore development model refers to the structured approach used to collaborate with software teams located in another country. The model defines:

Ownership
Pricing
Communication flow
Delivery accountability
Risk distribution

Software development engagement models help organizations align technical execution with business objectives while leveraging global talent. Each offshore business model fits a specific project type, budget pattern, and maturity level. Selecting the right offshore development center model directly impacts productivity, quality, and long-term sustainability.

Offshore Development Models Comparison

Below is a practical comparison of all seven offshore engagement models across cost predictability, flexibility, delivery accountability, and governance effort.

Image: Offshore Engagement Models – 7 Options Compared for Cost & Risk

Let’s take a closer look at each model.

#1 Fixed Price Model

A Fixed Price Model defines scope, deliverables, timeline, and total cost upfront. The vendor commits to delivery within the agreed budget and schedule, regardless of effort. This model assumes stable requirements with minimal change.

Ideal Use Cases

Small to medium-sized projects

Internal tools, microservices, or feature-specific builds with limited complexity.
Clearly defined requirements

Well-documented functional and non-functional requirements, wireframes, and acceptance criteria.
MVPs with minimal expected change

Early-stage validation with controlled experimentation and scope stability.

Advantages

Predictable budget

Easier financial planning and procurement approvals.
Simple contract structure

Clear deliverables and milestones reduce legal and administrative overhead.
Low management overhead

Minimal day-to-day supervision required.

Limitations

Low flexibility

Changes require renegotiation.
Quality risks if scope is underestimated

Vendors may optimize for speed over craftsmanship under margin pressure.

#2 Dedicated Development Team Model

A Dedicated Development Team Model provides a full-time offshore team working exclusively on the client’s product. The team operates as an extension of the internal organization, prioritizing long-term collaboration over transactional delivery.

Ideal Use Cases

Long-term product development

SaaS platforms, internal tools, and developer platforms.
Scaling engineering capacity

Growth without local hiring overhead.
Complex domains

Cloud platforms, AI systems, and distributed architectures.

Advantages

High ownership and accountability
Deep domain and business context
Predictable scalability and team continuity

Limitations

Requires long-term budget commitment
Ongoing client-side involvement needed

#3 Time & Material Model

The Time & Material (T&M) Model charges based on actual engineering effort (hourly or daily). Scope evolves over time, making this model ideal for exploratory or complex work.

Ideal Use Cases

Agile and iterative development
Innovation-driven initiatives
Unclear or evolving requirements
Early-stage product development

Advantages

High adaptability to change
Outcome-focused product thinking
Faster project initiation
Transparent cost visibility

Limitations

Lower budget predictability
Strong governance required
Challenging for procurement-heavy organizations

#4 Staff Augmentation Model

The Staff Augmentation Model embeds offshore engineers into existing teams. The client retains full control over architecture, timelines, and delivery standards.

Ideal Use Cases

Skill gaps and niche expertise
Short-term capacity spikes
Mature internal engineering teams
Parallel execution needs

Advantages

Full control over execution
Fast onboarding
Flexible scaling
Strong cultural alignment

Limitations

Delivery accountability remains with the client
High internal management effort
Depends heavily on internal maturity

#5 Managed Services Model

Managed Services transfer end-to-end responsibility for delivery, operations, and performance to the offshore partner, measured against SLAs and KPIs.

Ideal Use Cases

Application maintenance and support
Cloud and platform operations
Predictable workloads
Cost optimization initiatives

Advantages

Outcome-driven accountability
Reduced internal operational load
Measurable performance standards
Predictable operational costs

Limitations

Limited flexibility
Vendor dependency risk
Not suitable for rapidly evolving systems

#6 SLA / Milestone-Based Model

The SLA/Milestone-Based Model ties delivery success and payments to predefined milestones or performance metrics.

Ideal Use Cases

Regulated and enterprise environments
Performance-critical platforms
Vendor transition scenarios
Programs with fixed delivery commitments

Advantages

Clear, enforceable accountability
Reduced client-side delivery risk
Improved stakeholder confidence
Strong procurement alignment

Limitations

Low execution flexibility
Heavy upfront planning
Risk of compliance over innovation

#7 Hybrid Engagement Model

The Hybrid Engagement Model combines multiple engagement models across workstreams.

Ideal Use Cases

Large enterprises with parallel initiatives
AI platforms and data-intensive systems
Phased digital transformations
Organizations balancing innovation and stability

Advantages

High operational flexibility
Balanced risk distribution
Improved cost efficiency

Limitations

Complex governance
Dependency on vendor maturity
Higher upfront planning effort

Selecting the Right Engagement Model

Key factors to consider:

Project purpose: Innovation vs. stability
Technical expertise: AI, LLMs, or niche domains
Budget constraints
Scope stability
Team size and structure
Product lifecycle stage

Strong alignment between business goals and delivery responsibility leads to successful offshore partnerships.

FAQs

Which engagement model is most cost-effective?

Fixed Price works best for small, well-defined projects. Dedicated teams offer better value for long-term initiatives.

Which model enables the fastest delivery?

Time & Material supports rapid iteration and parallel execution.

Which model minimizes risk?

SLA or milestone-based models reduce delivery risk through measurable commitments.

Which model ensures high quality?

Dedicated Development Teams promote ownership and long-term quality.

Which model suits long-term projects best?

The offshore development center model supports continuity and scalability.

Which model works best for AI projects?

Hybrid models are ideal for LLM and AI initiatives, balancing experimentation with accountability.

Conclusion

Each offshore development model presents trade-offs between cost, control, flexibility, and accountability. Organizations that align their business goals with the right IT engagement model unlock sustainable value and predictable outcomes.

However, engagement models alone don’t guarantee success. The right offshore partner helps reduce risk, adapt to change, and embed proven engineering, security, and operational practices.

Improving consultants help organizations design engagement models built on clarity, accountability, and measurable impact.

What Nobody Tells You About Golden Paths at Scale

Improving — Mon, 09 Feb 2026 08:33:28 +0000

Your platform team just celebrated hitting 85% golden path adoption. Everyone is excited. Onboarding time for new members dropped from three weeks to two days. New services spin up in minutes. Leadership loved the improved metrics.

Six months later, you've got 23 capability requests in your backlog. Your platform team is drowning. ML teams need custom GPU scheduling. The data team wants streaming pipeline patterns. API teams are rolling their own rate limiting because yours doesn’t fit their needs.

You nailed Day 1.

You're dying on Day 50.

This is the hidden scaling problem with golden paths. And it’s not solved by building more golden paths.

Golden Path Promise vs. What Actually Happens

The platform engineering playbook says golden paths reduce cognitive load and bring standardization across teams. They give developers a blessed path from code to production through self-service, accelerating feature development.

This works well for onboarding and early development. But creating new projects and features is maybe 1% of an application’s lifetime. The remaining 99% is operations, debugging, scaling, adding features, and handling edge cases.

Golden paths excel at the first 1%. They struggle with the rest.

Netflix learned this the hard way. They built a polished developer portal with documentation, recommended tools, and curated paths. Developers said it “wasn’t compelling enough” to change habits. Why?

Because it helped them start things, not run things.

The real work happens after deployment. That’s where centralized golden paths become bottlenecks.

Why Your Platform Team Hits a Ceiling

Your platform team can’t scale linearly with the organization. It’s just math.

Imagine:

200 engineers across 20 teams
Each team with distinct needs:
- ML teams need GPU scheduling, Kubeflow, model serving
- Data teams want Kafka, Airflow, stream processing
- API teams need rate limiting, circuit breakers, tracing
- Mobile backend teams need push notification infrastructure
Platform team size: 6 generalists

What Goes Wrong

Queue problem

Every capability funnels through the platform team. Prioritization becomes about who shouts loudest, not what delivers the most value.

Expertise problem

You build “good enough” solutions. ML teams need 12 GPU configurations. They get 3. It checks the box but doesn’t solve the problem.

Maintenance trap

You ship 30 capabilities over two years. Now you maintain all 30.

Kubernetes upgrade? Update 30 configs
Security patch? Test 30 capabilities
Team that requested capability #17 moved on? You still own it

Rigidity issue

Abstractions cover the 80% use case. The remaining 20% fights the platform or bypasses it entirely. This is abstraction debt.

Your platform team becomes the bottleneck for every capability, edge case, and new tool. That’s not sustainable.

Go With a Marketplace Approach

At KubeCon Atlanta, I discussed a different model.

Why should the platform team be the sole provider?

Why not turn the platform into a marketplace?

At a certain point, platform teams should stop being the builders of everything and become marketplace operators.

ML team contributes GPU scheduling
Data team contributes streaming pipelines
API team contributes rate limiting
Security team contributes authorization patterns

The platform provides the infrastructure for contribution, not every capability.

How the IDP Marketplace Model Works

Define clear interfaces

Expose APIs and standards for capability integration. Teams know exactly what to implement.

Build contribution templates

Provide scaffolding so teams don’t guess how to package their capability.

Automate validation

Every contribution must pass automated checks:

Metrics exposure
Security scans
Documentation
Health checks

Create recognition systems

Contribution isn’t charity. Track it. Reward it. Make it count in performance reviews.

Advantages of the IDP Marketplace Model

Parallel capability development instead of queues
Domain expertise embedded where it belongs
Platform team focuses on primitives, not products
Network effects drive adoption and value

Organizations running mature marketplace models see 3–4x faster capability development compared to centralized teams.

But Here’s the Part Nobody Talks About

After KubeCon Atlanta, many teams shared failed attempts at this approach.

Governance Breakdown

No quality standards lead to capability sprawl
Developers don’t trust community contributions
Multiple poorly maintained implementations of the same thing

One organization had three different Postgres operators, none properly maintained. Teams gave up and installed Postgres manually.

Quality Problems

Capabilities work for the original team but fail later:

Security CVEs
Kubernetes upgrades
Hidden network assumptions

Nobody owns the fix. Capabilities become orphaned and unusable.

Contribution Friction

Platform APIs are complex. Contributing requires understanding:

Service meshes
CI/CD pipelines
Monitoring
Security policies

Only senior engineers contribute. Participation dies out.

Maintenance Nightmare

Kubernetes 1.35 drops. Who updates 40 capabilities?
Security patch lands. Who validates everything?
Production breaks at 3am. Who’s on call?

Prerequisites for Making Marketplaces Work

1. Platform Primitives That Enable Contribution

Capabilities must plug in without platform code changes. If every addition requires core modifications, your platform isn’t ready.

2. Enforced Quality Standards

Automated testing
Mandatory metrics and health checks
Security scanning for CVEs and secrets
Documentation requirements:
- Runbooks
- Troubleshooting guides
- Usage examples

No documentation means no shipment.

3. Ownership Beyond Initial Contribution

Define maintenance responsibilities upfront
Clear security patching ownership
Deprecation and migration policies
Explicit handoff mechanisms

“You build it, you own it for 12 months” is a valid rule.

4. Cultural Readiness

Inner-source culture already exists
Contributions count toward goals and reviews
Leadership supports contribution time

If leadership sees contribution as “not real work,” the marketplace fails.

Hybrid Approach

Don’t go all-in immediately.

Golden capabilities for common needs (70–80%)
Marketplace capabilities for specialized domains

Capability Tiers

Platform-blessed

Maintained by platform team, SLAs guaranteed
Community-maintained

Supported by contributors, use at own risk
Experimental

No stability guarantees

Clear expectations prevent surprises.

Next Step for You

If you’re hitting scaling issues:

Audit your backlog for domain-specific requests
Identify teams with deep expertise
Start with a low-risk pilot capability
Build templates and validation, not just docs
Establish governance before scale

If you’re building your first platform:

Start centralized
Design extensibility from day one
Avoid premature marketplace complexity

Real Insight

Platform maturity isn’t “build golden paths and stop.”

It’s:

Build golden paths
Recognize when they become bottlenecks
Evolve your model intentionally

Centralization gives control and consistency.

Marketplaces give scale and expertise.

Neither is perfect.

The right choice depends on your organization’s stage.

I explored platform marketplaces, governance models, and real-world failure modes at KubeCon Atlanta.

Want to discuss platform scaling or share your experience?

Connect with me on LinkedIn. If you’re struggling with platform engineering, contact our consultants—we help teams build platforms that actually scale.

Security: The Thing That Everyone Loves to Hate

Improving — Mon, 09 Feb 2026 08:27:57 +0000

Security often gets pushed to "later" in cloud native development as teams rush to ship features, optimize costs, or scale faster. However, incidents like Log4j (an OSS program behind the 34% increase in vulnerability exploitation between 2020 and 2021) have shown that “later” usually means crisis mode, late-night calls, patching under pressure, and scrambling to contain the damage.

The truth is that cloud native security is as much about how teams think, collaborate, and prioritize it as it is about tools or compliance checklists. And here lies the real challenge: security is still seen as someone else’s problem. Due to this, 50% of organizations now have critical security debt, with high-severity issues left open for more than one year, according to ITPO. Developers focus on shipping, product managers focus on revenue, and platform engineers juggle complexity, while security risks quietly pile up.

At KubeCon + CloudNativeCon India 2025, I, Sonali Srivastava, brought together a panel of cloud native experts. Ram Iyengar, Bhavani Indukuri, Anusha Hegde, and I took this challenge head-on to spread awareness about prioritizing security. Our message was clear: to build truly resilient systems, security must be everyone’s responsibility, baked into the culture from day one, not bolted on at the end.

In this blog post, we explore how security spans differently across roles and why understanding these perspectives is essential for building a security-first organization. From spotting new-age threats like QR phishing to shifting security left in the SDLC and building a culture where accountability replaces blame.

Wake-up Call: New Threats and Everyday Risks

Security threats today evolve faster than awareness. Attack vectors are no longer limited to traditional phishing or endpoint breaches. They are dynamic, social, and increasingly AI-driven.

Emerging Threats Include

Quishing (QR phishing)

Users are tricked into scanning malicious QR codes during daily activities such as payments, restaurant menus, or opening URLs, leading to compromised devices or accounts.
Prompt injection attacks

Attacks targeting LLM-integrated applications that manipulate AI systems into revealing sensitive data.
Jailbreaks

Techniques used to bypass model restrictions or gain elevated access in sandboxed environments.
Dependency confusion attacks

Exploits of package naming conventions to inject malicious code into software supply chains.
Configuration drift exploits

Unsupervised or AI-generated cloud infrastructure changes that introduce unintended vulnerabilities.

The threat landscape is expanding faster than organizational readiness. Security awareness, tooling, and culture must evolve just as quickly, starting with the foundation.

Security Through Different Lenses

Developers’ Lens: Simplicity and Early Detection

Developers are often caught between the pressure to deliver fast and the need to maintain secure practices. Every dependency added, every library imported, and every base image chosen introduces potential risk.

What developers can focus on:

Simplify the stack

Fewer dependencies mean fewer unknowns and a lower vulnerability risk. Question every third-party library.
Use simple base images

Complex images add unnecessary packages that expand the attack surface.
Integrate SBOMs early

Software Bill of Materials (SBOM) generation should be part of the build process, not an afterthought.
Enforce security at the PR stage

Use security linters in IDEs and make vulnerability checks part of standard code reviews.

“You should think of having less dependencies when you are trying to choose your base images. That’s where SBOMs are really important.”

— Bhavani Indukuri

A developer’s role is to make choices that minimize the blast radius of failures.

Security Engineers’ Lens: Discipline Over Band-Aids

Security engineers are often perceived as the people who slow things down, but their focus is on preventing recurring issues instead of applying temporary fixes.

What security engineers can focus on:

Treat governance as discipline, not bureaucracy

Standards like Pod Security Standards (PSS) and regulations such as GDPR act as guardrails, not blockers.
Build resilience through prevention

The goal is not just passing audits, but making insecure configurations difficult to deploy.
Establish security gates

Automated checks that block vulnerable code from reaching production must be mandatory.

“There are governances and compliances in place for a reason; it’s like when you used to go to school, you stood in a straight line.”

— Sonali Srivastava

Security engineers create systems where secure behavior is the default.

Product Managers’ Lens: Security as Strategic Investment

Product managers often face pressure to trade security for speed, treating security as tech debt. This framing is flawed. The average time to fix security flaws has increased 47% in five years, from 171 to 252 days, according to ITPO.

What product managers can focus on:

Reframe security as a product feature

Security directly impacts trust, reliability, and brand reputation.
Prioritize security alongside features

Security requirements must be part of feature specs from day one.
Understand different risk types
- Vulnerabilities: Known CVEs in dependencies
- Misconfigurations: Policy violations and access control issues
Use the right tools for visibility
- VEX for vulnerability management
- Policy engines like Kyverno for misconfigurations

“You have vulnerabilities which are a whole big class of problems. The other class of problems is misconfigurations.”

— Anusha Hegde

When PMs factor security into roadmaps, it becomes a competitive advantage instead of a scramble.

DevOps and Platform Engineers’ Lens: Infrastructure as the Security Boundary

Platform engineers sit between development velocity and operational stability. Their infrastructure decisions directly shape security posture.

What platform engineers can focus on:

Enforce security through automation

Policies should not rely on manual checks.
Maintain least-privilege access

Regularly audit permissions and rotate credentials.
Manage configuration drift

Use infrastructure-as-code and policy enforcement to prevent unsupervised changes.
Build observability into security

Integrate security metrics into daily dashboards and workflows.

Platform engineers either make security scalable or create gaps attackers exploit.

Leadership’s Lens: Culture and Accountability

Leadership determines whether security is a real priority or a checkbox exercise.

What leaders can focus on:

Allocate time for security

Dedicate sprint capacity to security improvements.
Tie security to customer trust

Security incidents impact users, retention, and revenue.
Celebrate proactive security

Reward teams who prevent issues early.
Make security visible

Review security metrics alongside business metrics.
Foster psychological safety

Encourage reporting issues without blame.

Leadership creates the conditions where security can thrive.

Building a Security-first Culture

Understanding individual perspectives is only the beginning. The real work is weaving them into a shared culture.

Educate and empower

Make security training part of onboarding and continuous learning.
Normalize ownership

Encourage every role to think like a security advocate.
Create feedback loops

Use post-incident reviews as learning tools, not blame sessions.
Make security visible

Integrate security metrics into everyday workflows.
Focus on adaptability

Treat security culture as a strategic asset that evolves with new threats.

A multi-layered defense complements this culture, protecting applications, infrastructure, and organizational boundaries.

Next Step: The Cultural Transformation

The threat landscape continues to evolve. AI-driven attacks, supply chain vulnerabilities, and configuration exploits are becoming more sophisticated. Organizations can only keep up through cultural transformation.

Security must be embedded into daily workflows and maintained through transparency. When security becomes a shared conversation rather than a compliance checkbox, true organizational maturity begins.

Each issue becomes an opportunity to strengthen systems and prevent recurrence. This mindset builds stronger systems and more resilient organizations.

At Improving, trust is at the core of everything we do. Keeping software secure is essential to maintaining that trust. Our consistent focus on security and privacy is why enterprises continue to trust us as one of the leading software consulting providers.

How To Choose the Best Offshore Software Development Partner?

Improving — Fri, 26 Dec 2025 09:22:05 +0000

The global offshore software development market has grown into a massive industry, valued at over $150 billion and continues to expand. For many organizations, it offers a vital pathway to scale engineering teams and access specialized talent. However, the sheer volume of vendors creates a somewhat chaotic landscape where quality varies drastically. Thousands of firms offer offshore services, but not all are up to par.

Reports of scams and failures are unfortunately common. Companies have encountered "ghost firms" or "shell companies" that vanish after receiving deposits, vendors that hold Intellectual Property (IP) hostage, or teams that secretly outsource work to unqualified third parties. Beyond outright scams, many projects fail due to poor code quality, missed deadlines, or a lack of transparency. Selecting the right partner is a critical business decision that requires a rigorous vetting process.

In this blog post, we will explore how to validate and select the best offshore software development partner for your project.

#1 Validate Technological Depth and Domain Fit

You need a partner who understands your specific technical landscape. Verify that the service provider has proven experience and capabilities that align with your roadmap – whether in cloud-native development, data engineering, cybersecurity, QA automation, enterprise platforms, AI development, database management, etc.

WifiTalents reports that around 70% of companies cite access to specialized skills as a top reason for offshoring. To ensure you are getting this expertise, ask for case studies that mirror your technical stack. For example, if you are building a fintech application, a general web development firm may not understand the nuances of PCI-DSS compliance or high-frequency transaction processing. Technical assessments and code reviews during the selection process can confirm if their depth matches their marketing claims.[attached_file:1]

#2 Assess Delivery Maturity and Governance

A partner's ability to write code is secondary to their ability to deliver software predictably. Look for partners that follow disciplined Agile practices, maintain transparent reporting, manage backlogs effectively, and keep consistent communication.

Ask about their approach to risk management, documentation, and knowledge transfer. A mature partner will have established protocols and frameworks for sprint planning, daily stand-ups, and retrospectives. They should provide you with direct access to project tracking tools (like Jira or Azure DevOps) so you always know the real-time status of your deliverables.[attached_file:1]

#3 Evaluate Hybrid or Onshore Leadership Models

Purely offshore models can sometimes suffer from a "thrown over the wall" mentality where requirements are misunderstood. Partners that combine offshore engineering talent with nearshore or onshore leadership typically provide better alignment, faster feedback loops, and stronger delivery oversight.

In this model, an onshore delivery lead or product manager acts as a bridge. They understand your business context, time zone, and culture, effectively translating your vision to the engineering team. It reduces the friction often caused by language barriers or time zone gaps, ensuring that the offshore team is always building the right thing.[attached_file:1]

#4 Prioritize Security and Compliance Readiness

Data breaches can be catastrophic. Make sure the provider complies with standards such as SOC 2, ISO 27001, GDPR, or HIPAA when relevant. About 53% of companies identify data security as a top concern in offshore contracts, according to the Zipdo education report.

Security validation should go beyond checking for a certificate. Inquire about their internal security practices: Do developers work on secure, managed devices? Is there physical security at their office? How do they handle data at rest and in transit? A partner who cannot answer these questions confidently is a significant risk liability.[attached_file:1]

#5 Check Cultural and Communication Fit

Technical skills are easier to screen than soft skills, yet the latter often determines project success. Teams that communicate clearly, collaborate effectively across time zones (sync as well as async), and demonstrate responsiveness are easier to integrate. Approximately 54% of offshore projects experience cultural or communication challenges, states the Zipdo education report.

Cultural fit includes work ethics, proactive problem solving, and the ability to say "no" or offer better alternatives. You want a team that feels like an extension of your own, not a silent order-taker. During the interview process, observe if they ask clarifying questions or simply nod. Active engagement is a strong indicator of future communication health.[attached_file:1]

#6 Focus on Long-term Strategic Value

The best partners act as strategic contributors. They help plan future investments, optimize engineering processes, and scale programs responsibly to ensure sustainable growth and product stability. A strategic partner will care about the lifecycle of the product. They will suggest architectural improvements, warn against technical debt, and help you innovate.

When a vendor is only focused on billable hours, they may build efficient code that is ineffective for your long-term goals. Look for a partner interested in your business outcomes, not just your ticket backlog.[attached_file:1]

#7 Awards, Certifications, and Partners

Credibility often leaves a trail. Look for objective third-party recognition.

Partner Status: Are they a Microsoft Gold Partner, AWS Advanced Partner, or Google Cloud Partner? These tiers require verified expertise and client success stories.
Industry Awards: Accolades like the Inc. 5000, "Best Places to Work," or recognition from Clutch and Gartner can indicate operational excellence and stability.
Certifications: ISO 9001 (Quality) and CMMI (Capability Maturity Model Integration) levels show a commitment to process improvement.[attached_file:1]

#8 Community Involvement

Active participation in the tech community is a hallmark of a passionate engineering culture. Check if the company hosts local user groups, sponsors tech conferences, or contributes to open source projects. Extensive contributions to open source libraries suggest their developers are leaders and understand complex code created collaboratively. Organizing hackathons or community learning sessions indicates they invest in their team's growth and the broader ecosystem.[attached_file:1]

#9 Check out the Legality

Legal recourse is often overlooked until it is too late. In case of disputes, you need to know which government will handle the situation.

Jurisdiction: Does the contract state that disputes are resolved in your home country's courts, or theirs?
IP Protection: Ensure the contract explicitly assigns all Intellectual Property rights to you upon creation.
Indemnification: The agreement should protect you against claims if the vendor accidentally infringes on third-party IP.[attached_file:1]

#10 Political Stability

Global events impact local operations. Research if the country has stable governance, or how the change in regulations may affect your project. Hyperinflation can lead to sudden rate hikes or staff turnover.

Political unrest can sometimes lead to government-mandated internet blackouts. The emerging laws in some nations may restrict how data can be stored or accessed across borders.[attached_file:1]

#11 Assess Talent Retention and Team Stability

If your offshore team changes every three months, you lose institutional knowledge and waste time onboarding new developers.

Retention Rates: Ask for their annual employee turnover rate. The turnover rate below 15% is excellent in the IT sector.
Employee Satisfaction: Look for "Best Place to Work" awards. Happy developers stay longer, write better code, and care more about the product.[attached_file:1]

#12 Scalability and Flexibility

Your needs will change. A good partner must be able to scale up quickly when you have a deadline and scale down when you are in maintenance mode.

Bench Strength: Do they have a "bench" of developers ready to join?
Ramp-up Time: How long does it take them to staff a new senior engineer?
Contract Flexibility: Avoid long-term lock-ins that prevent you from adjusting team size based on business value.[attached_file:1]

Offshore Partner Evaluation Questionnaire

Company & Culture

What percentage of your workforce is full-time vs. contractors?
How do you maintain engineering culture across distributed or hybrid teams?
What is your ratio of senior-to-junior engineers?
What learning and development programs do your engineers participate in?
How do you ensure English proficiency across all team members?
Can we meet the proposed team members before engagement?
What is your bench strength (available talent pool) for rapid scaling?[attached_file:1]

Delivery, Process & Collaboration

Who will be our primary point of contact (role, seniority, experience)?
How do you measure developer productivity and team performance?
What is your escalation path for risks, blockers, or underperformance?
How do you ensure continuity when a team member leaves unexpectedly?
How do you handle knowledge transfer and documentation throughout the project?
What practices do you use to maintain code quality (reviews, CI/CD, linters, etc.)?
How many hours of time-zone overlap can we expect daily?
How will you align your team with our product roadmap and OKRs?[attached_file:1]

Technical Expertise & Quality Assurance

How do you assess technical proficiency during hiring?
What percentage of your team has experience with our tech stack?
Can you show examples of architecture documents or code standards from past work?
How do you handle DevOps, CI/CD, and release management?
What testing approach do you follow (unit, integration, automation, load testing)?
Do you provide dedicated QA engineers or expect developers to self-test?
What is your process for managing pull requests and code reviews?[attached_file:1]

Security, Compliance & Risk

How often do you conduct security audits or penetration testing?
Are your developers required to work from secure office networks or can they work remotely?
What endpoint protection, MDM, and monitoring systems do you use?
How do you manage privileged access for developers (production, staging, repositories)?
Do you have a documented incident response plan?
How do you ensure compliance when your team members work remotely?[attached_file:1]

Legal, Commercial & Strategic

Will our contract include SLAs for uptime, delivery quality, and responsiveness?
Can you provide a sample MSA and SOW for review?
How do you structure pricing (hourly, fixed sprint, retainer, dedicated team)?
What is included and not included in your pricing model?
Do you charge extra for project managers, QA, DevOps, or after-hours support?
What is your policy for replacing underperforming team members?
Do you subcontract any part of the work? If yes, under what terms?
What is your minimum engagement length?[attached_file:1]

Financial Stability & Business Continuity

Can you share proof of financial stability (years of operation, financial reports, etc.)?
What is your business continuity plan in case of political, economic, or natural disruptions?
How do you ensure uninterrupted service if a developer becomes unavailable suddenly?[attached_file:1]

Team Fit, Soft Skills & Work Style

How do you evaluate communication skills during hiring?
How do your teams handle conflict resolution?
Do you provide cultural alignment or communication training for engineers?
How do you manage cross-functional collaboration with designers, PMs, and QA?

Final Words

The best offshore development companies focus on quality engineering, transparent communication, and long-term outcomes. Choosing the wrong partner can cost you more than just money; it can cost you your reputation and months of lost time. Be wary of vendors who say "yes" to everything or offer rates that seem too good to be true.

Improving understands the offshore software development challenges deeply. With over a decade of experience, we have earned our spot on the Inc. 5000 list of private companies. We combine deep technical expertise with a hybrid delivery model that ensures security, clarity, and results. We can help you build a sustainable, high-performing offshore strategy that truly works.

Best MCP Servers for Software Developers and Engineers

Improving — Fri, 26 Dec 2025 09:12:17 +0000

AI assistants are getting smarter, but most of them still cannot directly act in real systems. They can explain or suggest, but they cannot execute it. MCP servers solve that gap.

MCP servers give AI a safe way to call real tools, APIs, workflows, and systems. In this blog post, we will explore the MCP servers that are actually helping developers, SREs, and automation engineers.

What is MCP Server?

MCP server is a small service that exposes actions, resources, or queries to AI using the Model Context Protocol. MCP is an open standard that defines how AI agents can connect to external systems in a consistent and permission-controlled way. It is the "bridge" between AI and real infrastructure.

In theory, an MCP server can be built for any platform. The building blocks include connection lifecycle, schema definition, authorization, resource enumeration, streaming data, clear error design, and mapping real system capabilities into MCP actions. In practice, it is not trivial.

Teams must think about versioning, capability boundaries, idempotent behaviors, safe scoping of high-impact operations, and returning meaningful errors that AI models can interpret reliably. Building an MCP server takes time and careful design. Using existing MCP servers can significantly speed up development and let the focus directly on value.

List of Best MCP Servers for Software Engineers

The software developer team at Improving stays at the forefront of innovation, constantly testing and refining the latest tools, frameworks, and protocols shaping the AI ecosystem. Our software engineers actively explore how MCP servers can bridge AI systems with real-world platforms and workflows, making automation and integration more seamless. Based on hands-on testing and real project experience, here is a curated list of MCP servers our experts recommend.

DevOps & Infrastructure Management Servers

Kubernetes MCP Server: Allows AI assistants to connect with Kubernetes/OpenShift clusters to perform CRUD operations, manage pods, deployments, services, and logs. Flux159's mcp-server-kubernetes also connects to existing kubectl contexts.
GitHub MCP Server: Facilitates automating and managing GitHub repositories, issues, pull requests (PRs), branches, and releases via AI agents.
AWS MCP Server: Enables AI assistants to manage AWS resources such as S3, DynamoDB, VPC configurations, EC2, and IAM.
Azure DevOps MCP Server: Integrates with Azure DevOps for managing work items, pipelines, repositories, and pull requests.
Terraform MCP Server: Integrates with the Terraform ecosystem for Infrastructure as Code (IaC) development.
Jenkins MCP Server: Enables LLMs to interact with Jenkins for listing jobs, triggering builds, and retrieving logs.
Argo CD MCP Server: Allows AI assistants to interact with Argo CD deployments and applications using natural language commands.
Docker Hub MCP Server: Connects Docker Hub APIs to LLMs for intelligent image discovery and repository management.
Cyclops MCP Server: Enables AI agents to manage Kubernetes resources through the Cyclops abstraction layer.
Alibaba Cloud MCP Server: Official server for managing Alibaba Cloud resources including ECS instances and Cloud Monitor metrics.

Testing and Validation Servers

Postman MCP Server: Curated catalog of MCP servers for interacting with external services via defined endpoints.
Testkube MCP Server: Enables AI assistants to interact with testing workflows, executions, and artifacts on Testkube.
Playwright MCP Server: Official Microsoft implementation for browser automation through structured accessibility snapshots.

Monitoring & Observability Servers

Prometheus MCP Server: Enables LLMs to run PromQL queries and analyze Prometheus metrics.
Grafana MCP Server: Allows programmatic interaction with Grafana dashboards and data sources.
Datadog MCP Server: Enables operations like retrieving monitors, logs, metrics, and incidents.
Comet Opik MCP: Natural language exploration of LLM observability data and monitoring metrics.
Influx DB MCP Server: Official server for InfluxDB time-series data management.
Hydrolix MCP: Time-series datalake schema exploration and query capabilities.

Daily Task Automation & Productivity Servers

Slack MCP Server: Connects AI models to Slack for channel management and messaging.
Filesystem MCP Server: Secure file system activities including read/write and directory management.
Notion MCP Server: Bridge between AI agents and Notion workspace for pages and databases.

Database & Other Specific Use Cases

ClickHouse MCP Server: Safe query execution and read-only enforcement for ClickHouse.
MCP Server Replicate: Python-based MCP for AI model inference and image generation.
La-rebellion MCP Server: TypeScript implementation with simplified facade pattern.
Chroma MCP Server: Access to local and cloud Chroma vector database instances.
Couchbase MCP Server: Manage Couchbase Capella and self-managed clusters.
Redis MCP: Official Redis implementation for key-value operations and search.
Snowflake MCP Server: Official server with full RBAC and comprehensive authentication support.

Security Domain

MCP-Scan: Scans MCP servers for vulnerabilities like prompt-injection and over-permissive tools.
MCP Gateway: Security proxy with reputation scoring and real-time risk alerts.
MCP for Security: Exposes security tools like Nmap and SQLMap to AI assistants.
Contrast MCP Server: Automated vulnerability detection and AI-guided remediation.
Panther MCP Server: Connects Panther Labs' SIEM for alert investigation.

Code-Writing & Revision Domain

Serena: Developer-assistant MCP for project search, editing, and symbol lookup.
Coding-agent-mcp: File I/O, terminal, and repository operations with sandboxed environments.
Next-devtools-mcp: Tailored for Next.js apps with project structure exploration.
VS Code MCP Server: Direct integration with VS Code for reading, editing, and linting code.
Code-to-tree: Parses source code into language-agnostic ASTs for semantic reasoning.

General MCPs

1mcp/agent: Aggregates multiple MCP servers under one unified endpoint.
Mcp-server-browserbase: Browser automation for browsing, scraping, and form filling.
Mcp-server-cloudflare: Integrates Cloudflare Workers, KV, R2, and D1 APIs.
Supabase-mcp-server: Query, manage, and configure Supabase

Best Practices to Use MCP’s in Production

While MCPs are helpful, you should avoid connecting them to your production without taking proper precautions. Here are several ways to use MCP servers securely:
Use clear access controls: Define which users or systems can access each MCP server and what operations they can perform. Use scoped API keys or tokens, rotate them regularly, and store them securely in a secret manager.
Keep servers isolated: Deploy each MCP server in its own environment or container to prevent one from affecting another. Use network segmentation or firewalls to limit communication to only what’s necessary.
Monitor logs and performance: Collect logs for every request and response to help with troubleshooting and audits. Track performance metrics like latency, error rates, and uptime, and set alerts for unusual behavior.
Validate inputs and outputs: Sanitize all incoming data and carefully review what your MCP servers return. Avoid exposing sensitive information and set sensible limits on data size to prevent overload or data leaks.
Test before deployment: Always test in a staging or pre-production environment using realistic workloads. Include security checks, load testing, and compatibility verification with your AI assistant.
Maintain consistent versions: Keep all MCP servers and clients on compatible versions. Apply updates promptly to fix bugs, security issues, or protocol mismatches, and document any configuration changes.
Plan for failure: Set up retries and timeouts for network calls, and ensure services shut down gracefully. Back up configurations and important data regularly so you can recover quickly from incidents.

Final Words

Software developers, engineers and users are using MCP servers to talk to the product directly from an AI app. If you are planning to develop the MCP servers for your product and encounter challenges, our AI engineering team can provide expert support and end-to-end development assistance.

As our engineering team continues to discover and experiment with new MCP servers, we will keep updating the list. Contributions and recommendations from the community are always welcome. If you believe that we missed any MCP servers that deserve to be featured in this list, share it with me on LinkedIn.

Best Places to Outsource Software Teams

Improving — Fri, 26 Dec 2025 07:30:10 +0000

A dedicated outsourcing development center can give you access to global talent, lower costs, and scalability. But not all outsourcing hubs are equal, each region and country comes with its own mix of strengths and trade-offs. Some countries are very inexpensive but lack experienced engineers; others offer high technical skills but at premium rates. Some have strong English communication but may pose cultural or time zone challenges; others are cost-effective but may suffer from unstable regulatory or political conditions. Choosing the right outsourcing location, whether for a long-term overseas outsourcing arrangement or a short-term project, requires a careful look at cost, talent quality, communication, time zones, and stability. In this blog post, we will cover the best regions and countries for outsourcing software teams, highlight what makes them attractive (and what to watch out for), and provide a practical checklist to help you evaluate potential offshore and nearshore partners with confidence.

Best Countries to Outsource Software Teams

1. Asia Pacific

Top Countries: India, Philippines

Best For: Large-scale enterprise projects, QA/Testing, Cloud-native development, and AI/Machine Learning

For decades, Asia has been the go-to destination for outsourcing software development. APAC is ideal for organizations seeking massive scalability. The sheer volume of engineering graduates produced annually in Indian region allows companies to scale teams from 5 to 50 in a matter of weeks. It is particularly strong for enterprise-level support, cloud migration, and legacy modernization.

Top countries for outsourcing in Asia

India: India offers unmatched scalability and a mature ecosystem of Service Integrators. From legacy system maintenance to cutting-edge AI and Cloud DevOps, talent exists here.
Philippines: Known for exceptional English proficiency and Western cultural compatibility, making them ideal for frontend and QA roles that require heavy communication.

Strengths & key advantages

Deep Talent Pool: India alone produces over 1.5 million engineering graduates per year.
24/7 Development Cycle: The time zone difference (10–12 hours from the US) allows for a "follow the sun" model where work continues while the onshore team sleeps.
English Proficiency: India and the Philippines have high English fluency, making them distinct from other low-cost regions.
Cost-Efficiency: It remains one of the most cost-effective regions for high-volume staffing.

Tradeoffs

Time Zone Gaps: While good for 24/7 cycles, real-time collaboration can be difficult without flexible working hours.
High Attrition: The market is competitive; retention can be a struggle if you don't partner with a top-tier firm.

2. Latin America (LATAM)

Top Countries: Mexico, Brazil, Argentina, Costa Rica, Guatemala

Best For: Agile product development, Staff Augmentation, Mobile App Development, and UX/UI design.

For North American companies, LATAM has become the premier destination for nearshoring. The primary advantage here is not just cost, but synchronization. When your engineering team is online at the same time as your product team, iteration cycles speed up dramatically. LATAM offers the perfect balance of cost and convenience. It is the premier choice for Agile teams that require real-time collaboration. Most major LATAM hubs offer a 4-8 hour overlap with US and Canada working hours, meaning your outsourcing software development company is essentially working the same hours you are.

Top countries for nearshoring in LATAM:

Mexico: With the USMCA trade agreement and physical proximity to the US, Mexico boasts a massive talent pool familiar with US business culture. Often called the "Silicon Valley of Mexico," Guadalajara is a leading tech hub with strong engineering talent.
Guatemala: With its growing pool of bilingual tech talent, competitive costs, and increasing investment in digital infrastructure, Guatemala is emerging as a promising nearshore destination for software outsourcing.
Argentina: A rapidly growing hub with strong government support for IT and high cultural affinity with the US.

Strengths & key advantages

Time Zone Alignment: Teams work in CST/EST/PST, enabling instant feedback loops and simultaneous Scrum ceremonies.
Cultural Affinity: There is a strong cultural overlap with Western business practices, leading to smoother integration with internal teams.
Tech Savviness: Strong focus on modern stacks (JavaScript frameworks, Mobile development, UX/UI).
English Proficiency: High proficiency, particularly in Mexico and Costa Rica, reducing the barrier to entry.

As Guillermo Ortega, President of Improving Mexico, states:

"Outsourcing in LATAM and India is more than only cost-effectiveness. Our clients have found a true innovation partner that makes their business goals possible through collaboration, convenience, and technology savviness."

Tradeoffs

Cost: Due to high demand from the US, rates in LATAM are generally higher than in Asia or Africa, though considerably lower than onshore.
Economic Volatility: Certain countries (like Argentina) face economic fluctuations, though established tech partners usually insulate clients from this.

3. Eastern Europe

Top Countries: Poland, Romania, Czech Republic, Ukraine

Best For: Complex R&D, FinTech, Cybersecurity, and automotive software

Eastern Europe has built a reputation for high-end engineering, rooted in a strong educational history of mathematics and science. If your project involves complex algorithms, security protocols, or deep R&D, Eastern Europe is a strong contender. The developers here are often viewed not just as coders, but as product engineers who challenge specifications to improve the final outcome.

Top countries for outsourcing in Eastern Europe:

Poland: The central hub. Highly stable, EU member (GDPR compliant), and home to developers who consistently rank high in global coding competitions (HackerRank, TopCoder).
Romania: Excellent balance of cost and quality, with widely spoken English and French.
Ukraine: Despite geopolitical challenges, Ukrainian developers remain renowned for their resilience and high-level technical output.

Strengths & key advantages

High-Quality Code: A strong emphasis on architectural integrity and mathematical precision.
Direct Communication: The work culture is direct and efficient; developers are known for their candid feedback and problem-solving mindset.
GDPR Compliance: Being part of or near the EU means strong adherence to data security and privacy standards.

Tradeoffs

Geopolitical Risk: The ongoing conflict in Ukraine has created hesitation in the region, though countries like Poland and Romania remain stable and secure NATO members.
Rising Costs: As the region integrates more with the Western European economy, rates are climbing steadily.

4. Middle East & Africa (MEA)

Top Countries: Egypt, Nigeria, South Africa, UAE

Best For: Web development, mobile apps, and organizations prioritizing value-based scaling.

While less saturated than Asia or Europe, the MEA region is rapidly digitizing and becoming a hub for value-based scaling. This region is attractive for companies looking to diversify their talent supply chain. With a young, tech-hungry population and increasing government investment in technical education, it offers a "ground floor" opportunity for hiring.

Top Countries for outsourcing in MEA & African region:

Egypt: A government-backed tech hub with excellent time zone alignment for Europe and competitive costs.
Nigeria: Popularly known as "Silicon Lagoon" of Lagos, it is producing a generation of self-taught and bootcamp-trained developers eager for international work.
South Africa: A more mature market with native English speakers and high cultural compatibility with the UK/Australia.

Strengths & key advantages

Growing Talent Ecosystem: Massive youth population eager to upskill in modern technologies.
Cost Advantage: Very competitive pricing compared to Eastern Europe and LATAM.
Language: South Africa and Nigeria have strong English capabilities.

Trade offs

Infrastructure: Power and internet stability can vary significantly by country (though established tech hubs usually have redundancies).
Maturity: The project management maturity may vary compared to the established processes found in India or Eastern Europe.

Comparison Table

(Note: Original table data not provided in text)

Hybrid Model

A hybrid outsourcing model combines teams from two different regions to leverage their respective strengths. One location may handle tasks that require real-time communication, tight feedback loops, or deep product involvement, while another location focuses on high-volume development, backend processing, testing, or long-running tasks. Instead of placing all your development resources in a single country or time zone, you distribute work strategically across multiple geographies based on capability, availability, and collaboration needs.

Best For: Companies that want to balance cost, speed, talent depth, and risk diversification without relying on just one region.

Why choose a hybrid approach?

Optimized Talent Mix: Access different skill strengths from different regions.
Better Coverage: Near-24-hour development cycles without overworking any single team.
Cost Balancing: Mix premium talent with more cost-efficient resources.
Risk Diversification: Reduced dependency on a single economy, political environment, or labor market.
Scalability: Easier to expand or contract teams based on regional availability.

Important Things to Look at When Considering Location for Outsourcing

Before signing a contract with an outsourcing software development company, explore the location and the partner against these criteria:

Security & IP Laws: Does the country have legal frameworks that protect your Intellectual Property?
English & Communication: Don't just check for "fluent" on paper. Interview the lead engineers to assess conversational ability and cultural nuance.
Attrition Rates: Ask the vendor about their employee retention. High turnover in the offshore and nearshore teams kills momentum.
Infrastructure Redundancy: Does the dedicated development center have backup power and diverse internet connections?
Overlap Hours: Keep at least 3-4 hours of working overlap for meetings and unblocking issues.

Final Words

Choosing the best place to outsource software teams is about aligning a region's strengths with your business goals. Asia offers unmatched scale; LATAM offers unmatched collaboration; Eastern Europe offers deep technical expertise. However, the most successful companies don't just pick the location, they pick an outsourcing partner with global reach.

Improving has strategically positioned itself to offer the best of both worlds. Through strategic acquisitions, we have established world-class delivery centers in both LATAM (Nearshore) and India (Offshore). Our outsourcing centers allow us to offer a hybrid model that provides the cost-efficiency and scale of India, combined with the real-time collaboration and cultural alignment of the Americas. Contact us today to learn why Fortune 500 and global enterprises trust Improving to help outsourcing software development.

Top 15 Offshore Software Development Companies

Improving — Fri, 26 Dec 2025 07:01:25 +0000

Over the past decade, offshore software development has evolved from a cost-arbitrage tactic to a cornerstone of global digital strategy. Outsourcing is enabling organizations to gain access to world-class talent, accelerate delivery through distributed time zones, and improve speed to market.

But not all offshore talent is good enough to match your vision and advance your product development. The distance between your organization and offshore site creates latency and miscommunication creeps in due to cultural differences. Working directly with offshore developers often means that you will be subject to the legal jurisdiction of their countries. The best approach to leverage offshore talent without risk is working with companies that shoulder the risk upon themselves, while connecting you to the best offshore engineers.

There are many US companies that are doing it globally. In this blog post, we will cover the best offshore delivery partners and explore how you can pick the one that suits your project.

Executive Summary

Offshore software development gives organizations access to skilled engineering talent from cost-advantaged regions while supporting predictable delivery across cloud, DevOps, data, security, product development, and AI. As talent shortages rise, offshore models help teams scale faster, tap niche expertise, and extend development cycles through distributed time zones.

This guide profiles 15 leading offshore development companies and outlines how to assess partners based on technical depth, delivery maturity, security, compliance, and cultural alignment. We also compare major offshore hubs across Asia-Pacific, Eastern Europe, and the Middle East and Africa, providing a clear framework to evaluate vendors, choose the right geography, and design a hybrid delivery model that fits your business goals.

Who Is This Guide For?

This guide is built for leaders responsible for digital delivery outcomes, including:

CTOs, CIOs & CDOs evaluating global delivery and engineering scale
VPs of Engineering & Product Leaders expanding development capacity
IT Sourcing & Procurement teams assessing offshore partner ecosystems

If your focus is on scaling engineering teams, accelerating product delivery, modernizing tech stacks, or achieving cost-efficient velocity, this guide is for you.

What Is Offshore Software Development & How Does It Work?

Offshore software development refers to partnering with engineering teams located in countries outside your own, typically in regions such as Asia-Pacific, Eastern Europe, and Africa, to deliver software applications, platforms, and managed services.

Most organizations engage offshore partners to either:

Expand product development capacity
Gain specialized tech talent
Reduce delivery costs
Support follow-the-sun operations

Common Offshore Engagement Models

Key models include staff augmentation, dedicated teams/PODs, managed services, and end-to-end delivery.

Key Advantages of Offshore Development

Offshore development offers meaningful strategic and operational benefits that help organizations accelerate delivery and optimize budgets while building scalable engineering capacity. Some of the most relevant advantages include:

Cost efficiency through access to high-quality engineering talent at globally competitive rates, further strengthened by purchasing power parity.
Faster scaling through access to large and highly specialized offshore talent pools.
Time-zone alignment that enables near-round-the-clock development cycles.
Access to niche skills across AI, cloud, data, cybersecurity, and emerging technologies.
Ability to refocus internal teams on strategy and core innovation.
Cultural exposure that strengthens global perspective.

How To Choose the Best Offshore Software Development Partner?

Validate technology depth and domain fit: Verify experience in cloud native development, data engineering, cybersecurity, QA automation, enterprise platforms, AI. Around 70% of companies cite access to specialized skills as a top reason for offshoring.
Assess delivery maturity and governance: Look for disciplined Agile practices, transparent reporting, risk management.
Evaluate hybrid or onshore leadership models: Better alignment and feedback loops.
Prioritize security and compliance: SOC 2, ISO 27001, GDPR, HIPAA. 53% of companies identify data security as a top concern.
Check cultural and communication fit: 54% of offshore projects experience cultural challenges.
Focus on long-term strategic value: Partners who contribute to planning and optimization.

Top 15 Offshore Software Development Companies

1. Improving

Improving is a global technology consulting and software engineering firm helping enterprises build modern, secure, scalable digital platforms. They combine consulting-led strategy with deep engineering expertise across AWS, Azure, Google Cloud.

Why Improving? Hybrid sourcing model with onshore leadership and offshore delivery. Strengthened by InfraCloud acquisition.

"This acquisition enhances our cloud-native solutions..."

— Girish Shilamkar, President, Improving Pune

Key Stats:

100+ clients (CNCF, Fortune 500)
170+ engineers (4 CKS, 51 CKA, 19 CKAD certified)
2–4 week team launch
Top 1% talent at offshore rates

Case Studies:

Mercedes-Benz: Consolidated 1,000+ Azure/400 AWS accounts, 80% faster deployments.
J.P. Morgan: Reduced deployment from 20min to 5min.

Connect with Improving offshore experts.

2. HCL Global Systems

Offers digital engineering, enterprise applications, infrastructure services across SAP, Oracle, Microsoft, cloud ecosystems.

Why HCL? Large-scale delivery capacity, flexible models for ERP modernization.

Talent: India, Middle East engineers in app dev, ERP, cloud migration.

Track Record: Manufacturing, healthcare, consumer services transformations.

3. Tata Consultancy Services (TCS)

World's largest IT services company with full lifecycle capabilities in consulting, digital transformation, product engineering.

Why TCS? Mission-critical, multi-year engagements with strategy + execution.

Talent: Massive India-based pool + LATAM/Europe in software eng, data, cybersecurity.

Track Record: BFSI, telecom, public sector, manufacturing.

4. L&T Technology Services (LTTS)

Leader in ER&D, embedded systems, digital industrial transformation, IoT platforms.

Why LTTS? Hardware-software convergence for automotive, aerospace, industrial.

Talent: India-based in embedded systems, digital manufacturing, IoT.

Track Record: Connected vehicles, digital twins, smart factories.

5. Accenture

Global consulting with enterprise modernization, digital product engineering, cloud transformation.

Why Accenture? End-to-end digital transformation, global governance.

Talent: India, Philippines, Eastern Europe, LATAM.

Track Record: Financial services, healthcare, retail, telecom.

6. Wipro

Software development, cybersecurity, digital transformation, legacy modernization.

Why Wipro? Cost-effective delivery, extensive domain coverage.

Talent: India primary, + LATAM/MEA/APAC.

Track Record: BFSI, retail, manufacturing, telecom, energy.

7. Persistent Systems

Strong in product development, platform integration, digital modernization.

Why Persistent? Scalable architecture for digital platforms, customer products.

Talent: India, Eastern Europe in cloud, data, enterprise platforms.

Track Record: BFSI, healthcare, manufacturing, ISVs.

8. Thoughtworks

Technology consultancy specializing in digital product engineering, agile transformation.

Why Thoughtworks? Architectural transformation, rapid iteration, design-led thinking.

Talent: Global network (APAC, LATAM, Europe, NA) in custom dev, DevOps.

Track Record: Retail, travel, technology sectors.

9. EPAM Systems

Digital platforms, product development, enterprise modernization.

Why EPAM? Engineering scale across cloud, digital platforms, complex integrations.

Talent: Strong Eastern Europe (Poland, Ukraine, Georgia) + APAC/LATAM.

Track Record: BFSI, media, healthcare, travel, consumer tech.

10. Nagarro

Digital product engineering, data modernization, agile delivery.

Why Nagarro? Flexible engineering-first delivery, legacy modernization.

Talent: India, Eastern Europe, LATAM in cloud, DevOps.

Track Record: Healthcare, logistics, manufacturing, technology.

11. Endava

Digital engineering, product development, platform modernization.

Why Endava? Design-focused product dev, co-creation.

Talent: Eastern Europe (Romania, Moldova, Serbia).

Track Record: Financial services, retail, logistics, media.

12. Globant

Digitally native engineering with UX, AI, product development.

Why Globant? Product innovation, customer experience redesign.

Talent: LATAM hubs (Argentina, Colombia, Mexico, Brazil).

Track Record: Consumer tech, retail, media, travel.

13. SoftServe

Digital modernization, data transformation, software innovation.

Why SoftServe? Strategic consulting + large-scale engineering.

Talent: Eastern Europe (Ukraine, Poland) + LATAM/Asia.

Track Record: Healthcare, retail, fintech, energy.

14. Slalom

Business/technology consulting with global delivery centers.

Why Slalom? Advisory depth + implementation support.

Talent: Blended global model with US leadership.

Track Record: Consumer, healthcare, financial services.

15. ScienceSoft

Software development, testing, cybersecurity, modernization.

Why ScienceSoft? Predictable execution, cost efficiency.

Talent: Eastern Europe in enterprise dev, QA, DevOps.

Track Record: Healthcare, retail, professional services.

When To Hire Offshore Developers

Consider offshore when:

In-house talent insufficient for timelines
Need niche skills (AI, security, data)
Cost optimization required
Parallel workstreams across time zones
Flexible scaling needed

Choosing the Right Offshore Hub

Region	Key Advantages	Best For
Asia Pacific	Deep talent, scalability, English proficiency	Cloud, QA, enterprise platforms
Eastern Europe	Advanced R&D, cultural compatibility, time-zone overlap	Complex product engineering
Middle East & Africa	Cost advantage, growing talent	Value-based scaling

Improving Advantage: Pune, India center with AI/cloud-native expertise.

FAQs

1. What outcomes can offshore achieve?

Scale capacity, accelerate delivery, reduce costs, access specialists.

2. Right engagement model?

Staff Augmentation: Individual engineers
Dedicated Teams: Cross-functional PODs
Managed Services: SLAs/operations
End-to-End: Full lifecycle ownership

3. How quickly can teams start? 2–6 weeks typically.

4. Time-zone collaboration? Overlapping hours, Agile rituals, Slack/Jira/Teams.

5. Security/compliance? SOC 2, ISO 27001, GDPR, NDAs/IP protection.

6. Need domain/tech experience? Yes, accelerates onboarding, reduces risk.

7. Hidden costs? Onboarding, knowledge transfer—mature partners minimize.

Ready to scale with top 1% offshore talent? Connect with Improving for hybrid delivery models achieving up to 50% cost savings.

DEV Community: Improving

Cost Optimization in Amazon ECS: Leveraging Spot Instances the Right Way

Understanding ECS Launch Types

Running Tasks on EC2 Launch Type

Running Tasks on Fargate Launch Type

Fargate vs. EC2

Why Cost Optimization Matters in ECS

Where Spot Instances Fit in the Cost Optimization

Optimizing Cost with ECS on Spot

Fargate Spot

EC2 Spot Capacity Providers

Choosing the Right Spot Instance: Manual Data vs. Automated Selection

1. Manual Selection and Diversification using Spot Capacity Advisor

2. Automated Selection with Attribute-Based Selection (ABS)

Understanding Spot Allocation Strategies

Capacity Provider Strategies

Baseline Reliability Strategy

Base and Weight Explained

Cost vs. Reliability Tradeoff

Step-by-Step: Running ECS Workloads on Spot

Final Words

Backup and Restore Kubernetes Resources Across vCluster using Velero

What is Velero?

What is vCluster?

Why Backup and Migrate Workloads Using vCluster?

Why Use Velero with vCluster?

How to Backup and Migrate Workloads Between vClusters

Prerequisites

Step-by-step Guide

1. Create Azure Resources

2. Create a Service Principal with Contributor Privileges

3. Prepare Credentials

4. Log in to vCluster and Create Velero Namespace

5. Create BSL and Cloud Credentials

6. Install Velero Using Helm

Backup and Restore a Sample MySQL Pod

Deploy MySQL in Source vCluster

Add Test Data

Take a Backup

Restore in Destination vCluster

Update values.yaml for Destination

Create a Restore

Troubleshooting Tips

Issue 1: Backup status is PartiallyFailed or FailedValidation

Issue 2: Node Agent Pod is Not Running

Issue 3: Restore Fails Without Specific Errors

Conclusion

When MCP Is Not The Right Choice

What is MCP and an MCP Server?

Benefits of Adding MCP Servers to Your Software

Standardization and Interoperability

Developer Velocity and Resource Efficiency

Centralized Control and Governance

Architectural Flexibility for Growth

Hidden Costs: What MCP Adoption Really Means

Operational Burden and Complexity Tax

Performance Trade-offs

Security Risks if Misconfigured

A Nascent Ecosystem

Debugging and Observability Challenges

When MCP Is the Wrong Choice: Critical Red Flags

Customer-Facing Latency Sensitivity

Minimal Tool or Static Integrations

Regulated or Enterprise Security Gaps

Immature Teams or Shadow Deployments

AI as a Peripheral Feature

Decision Framework for Adopting MCP Servers

Complexity Assessment

Team Capability Audit

Total Cost of Ownership (TCO) Calculation

Strategic Alignment

Pilot Before Commitment

Common Pitfalls Organizations Fall Into

Exposing Overly Powerful Tools

Treating MCP As a Security Boundary By Itself

Skipping Monitoring and Logging

Allowing Unrestricted Model Access to Production Systems

Conclusion

End-to-End Observability with Prometheus, Grafana, Loki, OpenTelemetry and Tempo

Why Observability is Important

Issue 1: Backup status is `PartiallyFailed` or `FailedValidation`