Why pipeops

Ilori Joshua — Thu, 06 Aug 2026 07:00:20 +0000

What Changed my mind

I originally sat down to write an article titled "What is PipeOps?" I had been playing around with the platform, testing its features, and figured a standard overview would be useful. But then this past Saturday, while out for a run, I decided to check the billing dashboard for a test database environment I had set up and left running idle for 14 days.

The total compute usage for the infrastructure was just $0.19. Because of this, my basic $10 PipeOps subscription covered the whole expense with absolutely no additional cost.

I was legitimately shocked. That realization completely changed my perspective, and it changed the topic of this article. We don't just need to know what PipeOps is; system architects and startup founders need to know why they should be using it to drastically cut costs while gaining a crazy amount of control over their infrastructure.

I've sat in enough boardrooms with exhausted CTOs and bewildered CFOs to know one thing: the modern cloud deployment model is quietly draining our engineering teams. We all bought into the promise of hyper-scale providers like AWS, GCP, and Azure. The pitch was great: abstract away the physical data center so software engineers can just focus on building the product. But let's be honest about what actually happened. We didn't eliminate the complexity; we just traded racking physical servers for untangling Kubernetes YAML, fighting with Terraform state files, and babysitting CI/CD pipelines.

The default industry reflex—deploying directly to AWS or Azure and manually wiring together EC2, RDS, and VPCs—has become a massive tax on engineering velocity. But recently, I've been shifting my focus toward a different paradigm: PipeOps.

PipeOps is a No-Code deployment and infrastructure management platform that completely abstracts the nightmare of Kubernetes clusters and CI/CD pipelines, while—crucially—letting us keep our hands on the granular resource dials. Let's break down exactly why I recommend PipeOps over raw AWS or Azure, focusing on the real-world balance of control, cost, and uptime.

The Cloud Provider Tax: Surviving the "Always-On" Trap

To understand why PipeOps is a breath of fresh air, we have to look at the pain of a standard AWS deployment. AWS gives you Lego bricks. That's great for flexibility, but building a production-ready app means you're manually provisioning compute, databases, caching, networking, and IAM permissions.

The worst part? We end up over-provisioning out of fear. We put the database on a dedicated RDS instance, the cache on ElastiCache, and the app on EC2. It's safe, but it is financially devastating—especially for development, staging, or low-traffic production environments where those servers sit idle 90% of the time.

The Silent Budget Killer: The "Always-On" EC2 and RDS Trap

Whenever I audit a company's cloud architecture, the first thing I check is their baseline compute bill. Most developers default to setting up an EC2 instance for their application code and a separate RDS instance for their relational database. It is what we are all taught to do, and it is highly relatable for any general developer setting up a basic stack.

But this separation is essentially a trap. AWS bills these instances by the hour, 24/7, regardless of whether a single user is actually hitting your application.

AWS Compute Component	Pricing Structure (Estimated)	Monthly Financial Impact
EC2 t3.small Instance	$0.0208 per hour	~$15.18 per instance
RDS db.t3.micro Instance	~$0.018 per hour	~$13.14 per instance
EBS Storage (App + DB)	~$0.08 - $0.10 per GB	~$10.00+ minimum
Public IPv4 Address Charge	$0.005 per hour per Elastic IP	~$3.60 per IP address
Total Baseline Compute	Standard Decoupled Setup	$41.92+ per month

For a simple, standard decoupled setup, you are paying over $40 a month before a single byte of actual user data is transmitted. Factor in the new $0.005/hour charge AWS added for public IPv4 addresses, and it gets worse. If you scale this across multiple microservices, staging environments, or developer testing branches, this "always-on" idle compute bill can easily eclipse the cost of your actual production servers.

The PipeOps Paradigm: Abstraction Without Compromise

PipeOps fixes this by acting as an intelligent translation layer. You still use the cloud, but PipeOps handles the grunt work, getting your app from code to production in minutes.

When I sit down with a team, I show them the two ways they can run this:

Nova Servers (Managed Cloud): You tell PipeOps to spin up a fully managed VM directly on major global providers or, crucially, utilizing MTN Cloud for fully Nigerian-based infrastructure. This recent integration with MTN Cloud allows African businesses to deploy and scale faster using highly affordable, local cloud infrastructure tailored to the continent's realities. You never even log into a complex cloud console. You just use visual sliders to allocate CPU and memory, and PipeOps provisions it.
Bring Your Own Server (BYOS): Got a reserved instance you already paid for? Or a bare-metal box sitting in a closet? Install the pipeops-k8-agent, and PipeOps instantly turns it into a modern, container-orchestrated cluster.

Under the hood, PipeOps runs a highly resilient Kubernetes engine. But here is the magic: you don't have to manage it. No writing Helm charts, no kubectl nightmares, no cluster upgrades. You get the self-healing power of enterprise Kubernetes through an interface junior devs can use on day one.

Deep Dive into Control: Segregating the Server Without the Overhead

The biggest pushback I get from senior engineers when suggesting an abstracted platform is, "But I need control! If I don't manage the primitives, I can't guarantee performance."

This is where PipeOps truly shines, and it is a point I want to emphasize heavily. PipeOps doesn't just put your apps on a server; it gives you precise control over the server's resources. When setting up a server, you can allocate the exact amount of memory, storage, and vCPU that a specific service will use. This is highly efficient usage of the server, allowing you to dedicate exactly what you need without wastage.

A single server can contain multiple instances with highly efficient storage. You can put your PostgreSQL database, your Redis cache, and your application code all on the exact same machine. Now, for each of these, you can set strict boundaries.

This works by leveraging Linux cgroups and Kubernetes resource isolation—specifically the difference between requests (the minimum needed) and limits (the absolute maximum ceiling).

CPU Limits: If a service hits its limit, it gets safely throttled.
Memory Limits: If a service hits a memory limit, it is killed and restarted before it can crash the whole server.

In PipeOps, this control is visual and immediate. Imagine you provision a 4 vCPU, 8GB RAM server.

Redis: You can cap your Redis add-on to exactly 0.25 vCPU, 256 MB of RAM, and 1 GB of storage.
PostgreSQL: You can guarantee your relational database gets a solid 0.5 to 1 vCPU, 512 MB to 2 GB of RAM, and 5 to 10 GB of dedicated block storage.
Your App: You let your custom code consume whatever is left.

That sounds pretty incredible to me. You are segregating the server with absolute precision, but with none of the setup overhead and management burden typically required to pull this off. A memory leak in your Node app cannot starve your database. You get the operational stability of a highly decoupled architecture with the massive economic benefits of a single server.

The Economics: The $22.14 AWS Bill vs. The Covered PipeOps Subscription

This level of control ties directly back to my Saturday run experience. When I carried out a test with a relational database, I created a small server (2GB, roughly equivalent to a t3.small on AWS) and only put a Postgres DB on it.

If you are familiar with AWS RDS or EC2, you know that a t3.small costs anywhere from $13 to $50 a month depending on the server load. But for a fair comparison, let's leave both idle just running a staging environment.

In a direct AWS deployment, an idle t3.small EC2 instance and a db.t3.micro RDS instance are billed by the hour, 24/7. For my 14-day test period, the EC2 instance cost $15.46, and the RDS instance cost $5.92. That is a combined total of $22.14 on AWS just for an idle, sitting environment, entirely ignoring the added costs of EBS volumes, Elastic IPs, and data transfer fees.

Because PipeOps lets you densely pack these services and utilizes a hyper-accurate Pay-As-You-Go (PAYG) model, you are only billed for actual compute utilized. At the end of 14 days on PipeOps, the actual usage generated for my bill was a mere $0.19.

Why does this matter so much? Because PipeOps' subscription tiers actually include base server credits:

Subscription Plan	Monthly Price	Included Nova Credit	Target Audience
Growth Plan	~$10 (₦15,500)	~$5 (₦7,750)	Solo Devs & Startups
Platform Plan	~$35 (₦54,250)	~$10 (₦15,500)	Mid-Sized Agile Teams
Scale Plan	~$100 (₦155,000)	~$50 (₦77,500)	Expanding Orgs

Because the $10 PipeOps subscription comes with $5 in Nova server credits, my $0.19 usage was completely absorbed. The $10 subscription covered the whole expense with absolutely no additional infrastructure cost.

Why consider both? Because PipeOps' Postgres is a managed service just like RDS, and you are comparing it against an EC2 which is a server you have to set up and manage yourself. The main concept centers around cost and control. How much control do you have on the server? How much does that control cost? And how much is that control generating for your business? Getting hit with $22.14 in extra AWS charges versus zero additional cost on PipeOps is a wake-up call.

Getting Your Life Back (Zero Overhead)

Finally, let's talk about the human cost. I don't want my senior engineers spending three days writing GitHub Actions, provisioning load balancers, and fighting Let's Encrypt certificates.

With PipeOps, you connect your GitHub, and it automatically sets up the CI/CD pipeline. You run a git push, and it builds and deploys with zero downtime. SSL certificates are provisioned and renewed autonomously.

Even deploying complex infrastructure is a one-click affair. Need an open-source observability stack? One click drops Victoria Metrics (1 vCPU, 1 GB RAM, 30 GB storage) or Sentry (2 vCPU, 4 GB RAM, 30 GB storage) right onto your server with their boundaries strictly enforced.

The Bottom Line

At the end of the day, why you do anything in software is to serve a business purpose driving towards company goals and objectives. What did you promise as an organization or a freelancer to your users or business? We have to consider this when weighing the pros and cons of our infrastructure choices.

When I advise system architects today, my message is simple: stop treating raw AWS primitives as a badge of honor. It is a massive drain on your company's wallet and your team's sanity.

PipeOps gives us the ability to densely pack databases and application code onto single machines with strict, mathematical resource boundaries. We dodge the always-on EC2 and RDS trap, we turn our $22.14 idle instances into zero-cost line items completely covered by a standard $10 subscription, and we reclaim our engineering hours.

Join me in exploring PipeOps. Over the next few weeks, I'll be posting more deep dives and tutorials on exactly how to set up your infrastructure on this platform compared to the rest of the traditional cloud providers. Let's start building smarter, not harder.

Check out PipeOps with my referral link:

You're invited to PipeOps

Join me on PipeOps and ship apps to any cloud in minutes. Free to start — claim this invite, connect Git, and deploy without the DevOps headache.

console.pipeops.io

Carbon successor for c++

Ilori Joshua — Sat, 23 Jul 2022 00:47:00 +0000

Carbon by Google is said to be the successor to c++
C++ upgrading have been in play since but too much dept to pay , there was so much work that as been done but after much try making a new programming language seems to be a more realistic and attainable goal
So has of this week Google engineer Chandler Carruth introduced Carbon to be the successor to c++
So like every programmer I wanted to see the documentation of carbon to see if it will be a more efficient way to get project done
A big question popped up in my head after seeing it

is it just another system language
It solves the problem that the aging c++ has
But there are programming language that do the same thing it is talking about
I personally use rust on alot of project so am not seeing the major difference from carbon and rust from the syntax to the logic , there are so much similarities

So I want to get comments on this
Reference article link

https://www.google.com/url?sa=t&source=web&rct=j&url=https://thenewstack.io/google-launches-carbon-an-experimental-replacement-for-c/&ved=2ahUKEwiYnpmV3435AhWRXsAKHYVcATwQ0PADKAB6BAgEEAE&usg=AOvVaw1HjhOEbZeuUj32vMcIiNac

DEV Community: Ilori Joshua