How Your Infrastructure Is Burning Money: 8 Insidious Mistakes CTOs Make

My name is Peter Stukalov. With 20 years in the infrastructure game, I’ve seen it all. I’ve built projects and worked for everyone from five-person startups to the world's largest corporations. In this article, I want to share the costly mistakes I’ve been called in to fix.

Mistake #1: Treating Infrastructure as an Afterthought—Your Most Expensive Tech Debt

At the start of any project, the CTO is obsessed with the product. Every resource, every ounce of focus, is on shipping features to the market as fast as possible. In this race, infrastructure feels like a secondary concern, a tedious necessity. The logic is simple: "Let's just click some stuff together in the AWS console for now. Once we raise a round, we'll hire a DevOps person to clean it up."

This is the single most dangerous delusion that plants a financial and technological time bomb under your company.

At first, everything works. A few clicks, and you have a server, a database, and things are running. The project grows. But under the hood, chaos is breeding. No one remembers who opened which port, when, or why. The dev, staging, and production environments start to drift apart, spawning bugs that are impossible to reproduce.

Then comes the moment of truth. A major client demands a SOC 2 compliance audit. Or the first serious outage hits, and you realize no one knows how to spin up an exact replica of production from scratch.

That’s when the CTO frantically hires that Senior DevOps engineer, who delivers a devastating verdict. It's not a simple "we'll fix it." It's a choice between two catastrophic scenarios:

1. Technical Archeology: A six-month-long, painstaking process of reverse-engineering the chaos you clicked into existence. An engineer will manually write code (Terraform) to define every resource, every IAM role, and every security group, praying they don’t miss anything and bring down production. It's slow, expensive, and paralyzes all forward momentum.

2. Surgical Amputation: Burn it all to the ground and build it again from scratch. This is faster—maybe a month—but it requires pulling in the product team, painful data and service migrations, and almost guaranteed downtime. It's a self-inflicted gunshot wound to the business.

   
   

The question isn't whether you saved $15,000 on an engineer's salary in the first six months. The question is whether you're prepared to pay ten times that a year later in money, time, and reputation to fix a problem you could have avoided.

Infrastructure as Code (IaC) isn't a luxury; it's the foundation. Without it, you're building a skyscraper on sand.

Mistake #2: The Serverless Trap—A Cheap Start, an Expensive Dead End

The marketing pitch for Serverless is music to a startup CTO's ears: fast, cheap, pay only for what you use. For an MVP with a dozen functions, it works perfectly. But this initial simplicity hides an architectural snare known as the "distributed monolith."

The problem with Serverless isn't the technology itself; it's that it silently and tightly couples your business logic to a specific cloud provider's infrastructure. Your functions aren't just pieces of code; they become links in an invisible chain of AWS Lambda triggers, SQS queues, and DynamoDB streams.

As the project grows, these invisible connections become a tangled mess. Ten functions turn into hundreds, then thousands. I've personally seen a project with 9,000 functions where no one could answer simple questions: Which function calls which? Where is the source of truth for configuration? How do you debug a request that hops through a dozen of these functions?

This becomes a nightmare for several reasons:

• Manageability drops to zero. Without iron-clad IaC discipline from day one, the system becomes an unmanageable "big ball of mud."

• Cognitive load on developers skyrockets. Instead of thinking about business logic, they spend hours trying to untangle a web of implicit dependencies.

• The cost of debugging and maintenance dwarfs any initial savings. You save money on idle servers but burn multiples of that on developers idling in debug sessions.

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Eventually, you hit a wall where further development is impossible. The company arrives at the same conclusion as the one with 9,000 functions: years of work and millions of dollars invested are thrown away because the only way forward is to completely rewrite the project on a sane architecture.

Serverless is a powerful tactical tool for specific, isolated tasks (image processing, ETL pipelines). But using it as the foundation for your entire product is a bet that your business will never get complex. It's a bet that almost always loses.

Mistake #3: The "Simple Start" Trap—From EC2 to a Technological Cul-de-Sac

The road to tech hell often begins with the simplest, most logical step: launching a service on a single EC2 instance. It’s cheap, understandable, and fast. The project grows, and soon you have ten instances spread across different Availability Zones, wrapped in load balancers, and managing this menagerie by hand becomes impossible.

The time comes to hire the first DevOps engineer. And here, the CTO makes a critical error in defining the task. Instead of asking, "How do we build a scalable and efficient platform?" they ask:

"Automate what we already have."

A recruiter finds someone with experience in EC2, Terraform, and Ansible. This person diligently does exactly what they were asked: they automate an outdated approach. They write Terraform modules to create instances and Ansible playbooks to configure them. The task is formally complete. The CTO is happy.

A few years later, the company finds itself in a technological dead end:

• Engineering efficiency is 10x lower. A task that takes an hour in a Kubernetes world takes an entire day for the Ansible team. You're not paying for innovation; you're paying to fight legacy automation.

• You're paying for air. EC2 instances are almost always underutilized. You can't pick the perfect size, so you pay for unused capacity. Scaling is slow and inefficient, forcing you to keep a "buffer" of resources just in case of a traffic spike.

• You're building on a slow foundation. Attempts to run stateful services on EC2 inevitably lead to kludges like EFS (NFS), which kills performance.

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The result is a modern product running on a 15-year-old infrastructure paradigm. You hired a team of engineers not to build the future, but to pour concrete over the past. It's expensive, slow, and completely uncompetitive.

Mistake #4: The "DIY Tool" Trap—When Infrastructure Becomes a Product for Itself

The moment you hear your infrastructure team say, "we're writing our own tool for...," it should be a deafening alarm bell. I'm not talking about small helper scripts, but full-fledged internal products: a custom CI/CD engine, a bespoke templating system, or, God forbid, a proprietary orchestrator.

This is a symptom of a fundamental problem: your infrastructure team has lost its connection to business goals.

Instead of solving problems for developers and the business, they start solving engineering problems that are interesting to them. They begin building a product not for the company, but for themselves.

Why does this happen? Often, it's due to a flawed incentive system or a lack of clear objectives. Engineers want to grow and show results. If they don't see a path to do that by improving the platform for developers, they start "innovating" in a vacuum.

The consequences are always catastrophic:

• You pay twice: first for development, and then endlessly for maintenance, documentation, and training new hires on this unique tool nobody on the market knows.

• You create "indispensable" people: Critical system knowledge gets locked in the heads of one or two individuals. Their departure becomes a disaster.

• You lose the speed race: Your internal tool, built by one and a half engineers, can never compete on features and reliability with an open-source project maintained by thousands worldwide.

• You burn money on zero-value work: Every hour spent reinventing the wheel is an hour stolen from developing a real platform that could actually accelerate your business.

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In 20 years, I have never had to write my own complex tooling. The problem you're trying to solve has almost certainly been solved by someone else—and probably better. The CTO's job is not to encourage a zoo of homegrown solutions, but to channel the team's energy into intelligently integrating existing, battle-tested tools into a unified, user-friendly platform.

Mistake #5: The "Skills Mismatch" Trap—When Carpenters Pour the Foundation

This mistake is a direct result of not having dedicated expertise. In a young company without a DevOps engineer, developers show commendable initiative and build the CI/CD pipeline themselves. Naturally, they use the tools they know best—like JavaScript. On the surface, it’s a win: fast, cheap, and it works.

In reality, it's a time bomb that detonates the moment you hire your first infrastructure engineer.

You post a job opening that says, "Seeking an expert in Kubernetes, Terraform, AWS, and also, mastery of JS." 99% of strong infrastructure engineers will close that tab in five seconds. Why? Because professionals stick to their craft. Their job is to build reliable, scalable systems, not to debug the nuances of a front-end technology that has been shoehorned into the wrong domain.

You end up in a hiring dead end. You're forced to hire a junior engineer or an intern who has "a little Kubernetes" and some JS on their resume. This person is now responsible for a critical part of your business for years to come. Do I need to explain what your infrastructure will become under their stewardship? It will be fragile, undocumented, and completely unmaintainable.

The true error of the CTO here isn't choosing JS for CI/CD. It's the approach.

It's like letting carpenters pour the building's foundation because they happen to have planks of wood, not concrete.

Infrastructure is a separate, complex engineering discipline. Trying to cut corners by offloading it to non-specialists always results in exponentially higher costs down the road.

Mistake #6: The "Culture of Busyness"—When Metrics Kill Common Sense

This is one of the most insidious mistakes because it masquerades as "data-driven management." Most CTOs understand that you can't manage a product development team and an infrastructure team with the same yardstick. Product teams create new features, which are easy to measure. Infrastructure, in a perfect world, is invisible. Its main job is to ensure stability (operations) and provide resources to developers (platform development).

The ideal state of infrastructure is when it just works, and the team is in a "passive" mode because everything is stable and developers have what they need. That’s what you should be giving out bonuses for.

But if a CTO doesn't get this and demands constant "activity" from the infra team, a toxic cycle begins:

1. "Finding" Work. Engineers see they are valued not for stability, but for the number of tickets closed and commits made. They start artificially inflating task times and creating work out of thin air: endless meetings, refactoring for the sake of refactoring, and, of course, writing their own "brilliant" tools (see Mistake #4).

2. Complexity Creep. To justify their "busyness," the team starts to monstrously over-engineer the system. Why have three Kubernetes clusters when you can have a hundred? Why have one CI pipeline when you can write a hundred helper pipelines for it?

3. Headcount Bloat. The overly complex system requires more people to maintain it. New engineers join the same broken culture and start imitating the same busyness, making the system even more complex.

   
   

This cycle never breaks on its own.

It devours millions of dollars and leads to absurd outcomes. I saw a company where 10 engineers were maintaining ~100 Kubernetes clusters for a workload that could have easily run on three. The system was so complex it didn't work, and nobody could figure it out. The only solution was to tear it all down and start over.

This is a direct management failure by the CTO. Their job is to define the right metrics for infrastructure: SLIs/SLOs for stability (uptime, success rate) and Developer Experience metrics for the platform (commit-to-deploy time, time to spin up a test environment). The right metrics incentivize reliability and simplicity. The wrong ones breed monsters.

Mistake #7: The Golden Cage of SLAs—When Stability Kills Progress

This is the flip side of the previous mistake. Let's say you, as the CTO, did everything right: you implemented SLIs/SLOs, and your dashboards are a beautiful, reassuring green. Uptime is 99.99%. It looks like victory.

But this "green zone" can be a trap. When the only measurable success for the team is "nothing broke," engineers develop a powerful disincentive: don't touch what's working. Any change, any new tool, any upgrade is a risk to those precious nines of uptime. To protect their metrics, the team begins to sabotage all progress.

As a result, your infrastructure becomes a museum.

It's stable, but it's years behind technologically. You're still running old versions of Kubernetes, outdated CI/CD practices, and inefficient data storage solutions. Meanwhile, your competitors are adopting tools that let them do the same work five times faster and twice as cheap.

Your team aren't slackers; they're hostages of the system you built. They are heroically maintaining legacy instead of building the future.

The CTO's job isn't just to demand stability, but to manage the balance between stability and evolution. The platform must not only work, it must improve. This requires:

1. Allocating time for innovation. Mandate that 20% of the team's time must be spent not on maintenance, but on research, prototyping, and modernization.

2. Measuring more than just uptime. Introduce metrics that reflect developer speed and convenience (Developer Experience). How quickly can a test environment be created? How long does it take to get a commit to production? Improving these is just as important as maintaining an SLA.

3. Conducting regular audits. Bring in external consultants every year or two. A fresh perspective will spot the technological lag that has become invisible from the inside.

   
   

Blind faith in SLAs turns infrastructure from a business driver into an anchor. Stability without progress is just deferred failure.

Mistake #8: Betting on Humans—The Most Unreliable System Component

Cloud providers spend billions of dollars to achieve cosmic levels of infrastructure reliability. Here are the official stats from AWS:

• Availability Zone (AZ) Failure: 99.95% SLA, which equates to ~4.38 hours of downtime per year.

• Full Region Failure: 99.99% to 99.999% SLA, which is between 5 and 52 minutes of downtime per year.

  
  

The probability of a region failing today is about 0.000027%. That’s nearly zero.

Now compare that number to the probability that your key DevOps engineer celebrated a birthday too hard last night, is sleep-deprived, or just got distracted by a Slack message while manually deploying to production. That probability approaches 100%.

When you allow manual processes, you are deliberately tying your business, your revenue, and your reputation to the mood and condition of a single person.

You are nullifying every effort to build a fault-tolerant system by introducing the most unpredictable and error-prone element into its most critical point: a human. The primary source of failures in modern systems isn't hardware or software. It's human error.

Therefore, any process performed by hand isn't just tech debt; it's a ticking time bomb. Manual deployments, manual configuration changes, manual secret management—all are just waiting for the right moment to explode at the worst possible time.

The solution can only be one thing, and it must be absolute: total automation of all critical processes. Any change to the system must happen only through code, reviews, and automated pipelines. Manual intervention in production should be an exceptional event requiring special approval, not a routine practice.

Remember this simple rule: partial automation is no automation. If an engineer has the ability to "quickly fix" something by hand, they eventually will. And that will be the cause of your next major outage.

The Solution: From Reactive Infrastructure to a Proactive Platform

All the mistakes listed above are symptoms of one overarching disease: an outdated, reactive approach to infrastructure. When infrastructure is a black box that responds to tickets, it inevitably becomes a bottleneck for the business.

The modern, proactive approach is to stop treating infrastructure as a service department and start treating it as an internal product. Your developers are your internal customers. And to be productive, they need an Internal Developer Platform (IDP).

What is an IDP in plain English?

Imagine that instead of giving developers access to a warehouse full of raw building materials (the raw AWS/GCP infrastructure), you give them a set of pre-fabricated, secure, and standardized "Lego bricks." Need a database? Here's a database "brick." Just specify in your config whether you want a 'small', 'medium', or 'large' one. Need to spin up a new service? Here's a service "brick" that already has all the standards for logging, monitoring, and security built-in.

This platform is the "Golden Path." It's not a set of rigid constraints but a paved and well-lit road that is the easiest, fastest, and safest route for a developer to take.

How does this solve the CTO's dilemma?

1. Speed + Control. You no longer have to choose between "give developers freedom and get chaos" and "impose total control and kill velocity." Developers get self-service freedom within the platform's guardrails, and the CTO gets full control because the "bricks" themselves are designed by the infrastructure team according to your standards. A developer can't click together a database with a security hole, because the database "brick" already has the correct settings baked in.

2. Safe Deployments to Production. Developers still don't need direct access to production. Their interaction with the platform happens through code. Want to change a service version or add a database? Great, describe that change in a config file and open a Pull Request. The CTO or team lead can see all planned changes, review them, and trigger a safe, automated rollout with a single "Approve" button. The entire process is transparent and auditable through Git history.

3. Reduced Cognitive Load. Developers no longer need to be experts in Kubernetes, Terraform, and the arcane details of IAM policies. They can focus on business logic, using the simple, clear abstractions the platform provides.

   
   

This isn't science fiction. This is the reality built on GitOps principles, the technical implementation of which you can read about in my in-depth article. Shifting from reactive "infrastructure-by-ticket" to a proactive "infrastructure-as-a-platform" is a strategic move that directly impacts how quickly and reliably your company delivers value.

Accelerating Debugging: Ephemeral Environments on Demand

One of the biggest black holes for developer time is debugging one microservice that interacts with a dozen others. The classic docker-compose on a local machine is an endless adventure of dependency hell, memory shortages, and the infamous "but it works on my machine." The alternative—shared, static dev environments—creates its own problems: developers stepping on each other's toes, configuration drift from production, and paying for idle resources 24/7.

The platform approach (IDP) solves this problem at its root. Instead of trying to replicate production on a laptop, we give the developer the power to spin up a complete, isolated, and temporary copy of the entire application in the cloud with a single command.

This is another "Lego brick" the platform provides. A developer doesn't need to wait for a DevOps engineer or fight for a shared server. They just tell the platform, "create an environment for ticket X." The platform spins up all the necessary services, databases, and queues, configured exactly like production.

Using tools like Telepresence, the developer can then connect this remote environment directly to their local IDE and debug their code as if it were running on their laptop, but with the full context of a live, realistic environment.

The Result:

• The "works on my machine" problem vanishes. Debugging happens in an environment that is 100% identical to production.

• You save money. You pay only for the resources actively being used for debugging, not a penny more. The environment is destroyed when it's no longer needed.

• You gain speed. Hours spent setting up an environment turn into minutes. Developers code instead of waiting. This is a direct acceleration of your time-to-market.

Instant Rollbacks: A Business Safety Net

A critical bug in production is every CTO's nightmare. In a traditional system, it means a midnight panic, a frantic search for who to blame, attempts to apply hotfixes on top of hotfixes, and manual tweaks to the database. It's chaos that costs money, reputation, and sanity.

In the world of GitOps and IDP, this problem doesn't exist. Because Git is the single source of truth for the entire state of the system—not just code, but versions, configs, secrets—rolling back to the last known stable state is a trivial operation.

This isn't just a git revert on a single repository. It's an atomic rollback of the entire application to its last known good configuration. It takes minutes, with a single command, without panic or late-night calls. The platform itself ensures the real world matches the state described in Git.

This fundamentally changes the culture of development. The fear of deployment disappears. Instead of rare, monolithic releases that everyone is afraid to touch, teams shift to frequent, small, and safe changes. And the bug gets analyzed calmly in a test environment, not in a production fire.

Transparency as a Service: The Platform UI

Infrastructure should not be a "black box" understood only by a couple of DevOps engineers. That kills collaboration and fosters a culture of "not my problem."

A modern IDP provides a unified web interface—a mission control center for the entire application. This is the face of the platform that everyone interacts with: developers, testers, managers, and the CTO.

What does it provide?

• A shared view of reality. Everyone can see in real-time what version of which service is deployed to which environment, what its status is, and how it's performing.

• Developer self-service. Reading logs, checking basic metrics, restarting their own service in a dev environment—a developer can do all of this themselves without creating a ticket or distracting the infrastructure team.

• An end to the blame game. When a developer and a DevOps engineer are looking at the same dashboard with the same data, arguments over "whose fault is it?" stop. The conversation shifts to finding a solution together.

  
  

This transparency radically lowers the barriers between teams and transforms infrastructure from a source of friction into a shared enabler.

What to Do Next?

If you recognized your company in these mistakes, it's not a reason to panic. It's a reason to act. Building an internal platform isn't magic; it's an engineering discipline that saves millions and dramatically accelerates development.

If you're ready to move from firefighting to strategic growth, email me at peter.stukalov01@gmail.com to schedule a free, 30-minute strategy session. We'll diagnose where to start in your specific case and how to turn your infrastructure from a cost center into your greatest competitive advantage.