Microservices Are a Tax Your Startup Probably Can’t Afford

Why splitting your codebase too early can quietly destroy your team’s velocity — and what to do instead.

In a startup, your survival depends on how quickly you can iterate, ship features, and deliver value to end-users. This is where the foundational architecture of your startup plays a big role; additionally, things like your tech stack and choice of programming language directly affect your team’s velocity. The wrong architecture, especially premature microservices, can substantially hurt productivity and contribute to missed goals in delivering software.

I've had this experience when working on greenfield projects for early-stage startups, where questionable decisions were made in terms of software architecture that led to half-finished services and brittle, over-engineered and broken local setups, and demoralized teams who struggle maintaining unnecessary complexity.

Before diving into specific pitfalls, here’s what you’re actually signing up for when introducing microservices prematurely:

Microservices Early On: What You’re Paying For

Let's unpack why microservices often backfire early on, where they genuinely help, and how to structure your startup's systems for speed and survival.

Monoliths Are Not the Enemy

If you're building some SaaS product, even a simple SQL database wrapper eventually may bring a lot of internal complexity in the way your business logic works; additionally, you can get to various integrations and background tasks that let transform one set of data to another.

With time, sometimes unnecessary features, it's inevitable that your app may grow messy. The great thing about monoliths is: they still work. Monoliths, even when messy, keep your team focused on what matters most:

• Staying alive
• Delivering customer value

The biggest advantage of monoliths is their simplicity in deployment. Generally, such projects are built around existing frameworks — it could be Django for Python, ASP.Net for C#, Nest.js for Node.js apps, etc. When sticking to monolithic architecture, you get the biggest advantage over fancy microservices — a wide support of the open source community and project maintainers who primarily designed those frameworks to work as a single process, monolithic app.

At one real-estate startup where I led the front-end team, and occasionally consulted the backend team on technology choices, we had an interesting evolution of a Laravel-based app. What started as a small dashboard for real-estate agents to manage deals gradually grew into a much larger system.

Over time, it evolved into a feature-rich suite that handled hundreds of gigabytes of documents and integrated with dozens of third-party services. Yet, it remained built on a fairly basic PHP stack running on Apache.

The team leaned heavily on best practices recommended by the Laravel community. That discipline paid off, we were able to scale the application’s capabilities significantly while still meeting the business’s needs and expectations.

Interestingly, we never needed to decouple the system into microservices or adopt more complex infrastructure patterns. We avoided a lot of accidental complexity that way. The simplicity of the architecture gave us leverage. This echoes what others have written — like Basecamp’s take on the “Majestic Monolith”, which lays out why simplicity is a superpower early on.

People often point out that it's hard to make monoliths scalable, but it's bad modularization inside the monolith that may bring such problems.

Takeaway: A well-structured monolith keeps your team focused on shipping, not firefighting.

But Isn’t Microservices “Best Practice”?

A lot of engineers reach for microservices early, thinking they’re “the right way.” And sure — at scale, they can help. But in a startup, that same complexity turns into drag.

Microservices only pay off when you have real scaling bottlenecks, large teams, or independently evolving domains. Before that? You’re paying the price without getting the benefit: duplicated infra, fragile local setups, and slow iteration. For example, Segment eventually reversed their microservice split for this exact reason — too much cost, not enough value.

Takeaway: Microservices are a scaling tool — not a starting template.

Where Microservices Go Wrong (Especially Early On)

In one early-stage team I advised, the decision to split services created more PM-engineering coordination overhead than technical gain. Architecture shaped not just code, but how we planned, estimated, and shipped. That organizational tax is easy to miss — until it’s too late.

Diagram: Coordination overhead grows linearly with services — and exponentially when you add product managers, deadlines, and misaligned timelines.

Here are the most common anti-patterns that creep in early.

1. Arbitrary Service Boundaries

In theory, you often see suggestions on splitting your applications by business logic domain — users service, products service, orders service, and so on. This often borrows from Domain-Driven Design or Clean Architecture concepts — which make sense at scale, but in early-stage products, they can ossify structure prematurely, before the product itself is stable or validated. You end up with:

• Shared databases
• Cross-service calls for simple workflows
• Coupling disguised as "separation"

At one project, I watched a team separating user, authentication, and authorization into separate services, which led to deployment complexity and difficulties in service coordination for any API operation they were building.

In reality, business logic doesn't directly map to service boundaries. Premature separation can make the system more fragile and often times difficult to introduce changes quickly.

Instead, isolate bottlenecks surgically — based on real scaling pain, not theoretical elegance.

When I’ve coached early-stage teams, we’ve sometimes used internal flags or deployment toggles to simulate future service splits — without the immediate operational burden. This gave product and engineering room to explore boundaries organically, before locking in premature infrastructure.

Takeaway: Don’t split by theory — split by actual bottlenecks.

2. Repository and Infrastructure Sprawl

When working on the application, typically a next set of things is important:

• Code style consistency (linting)
• Testing infrastructure, including integration testing
• Local environment configuration
• Documentation
• CI/CD configuration

When dealing with microservices, you need to multiply those requirements by the number of services. If your project is structured as a monorepo, you can simplify your life by having a central CI/CD configuration (when working with GitHub Actions or GitLab CI). Some teams separate microservices into separate repositories, which makes it way harder to maintain the code consistency and the same set of configurations without extra effort or tools.

For a three-person team, this is brutal. Context switching across repositories and tooling adds up to the development time of every feature that is shipped.

Mitigating issues by using monorepos and a single programming language

There are various ways to mitigate this problem. For early projects, the single most important thing is — keeping your code in a monorepo. This ensures that there's a single version of code that exists on prod, and it's much easier to coordinate code reviews and collaborate for smaller teams.

For Node.js projects, I strongly recommend using a monorepo tool like nx or turborepo. Both:

• Simplify CI/CD config across subprojects
• Support dependency graph-based build caching
• Let you treat internal services as TypeScript libraries (via ES6 imports)

These tools save time otherwise spent writing glue code or reinventing orchestration. That said, they come with real tradeoffs:

• Complex dependency trees can grow fast
• CI performance tuning is non-trivial
• You may need faster tooling (like bun) to keep build times down

To summarize: Tooling like nx or turborepo gives small teams monorepo velocity — if you’re willing to invest in keeping them clean.

When developing go-based microservices, a good idea early in the development is to use a single go workspace with the replace directive in go.mod. Eventually, as the software scales, it's possible to effortlessly separate go modules into separate repositories.

Takeaway: A monorepo with shared infra buys you time, consistency, and sanity.

3. Broken Local Dev = Broken Velocity

If it takes three hours, a custom shell script, and a Docker marathon just to run your app locally, you've already lost velocity.

Early projects often suffer from:

• Missing documentation
• Obsolete dependencies
• OS-specific hacks (hello, Linux-only setups)

In my experience, when I received projects from past development teams, they were often developed for a single operating system. Some devs preferred building on macOS and never bothered supporting their shell scripts on Windows. In my past teams, I had engineers working on Windows machines, and often it required rewriting shell scripts or fully understanding and reverse engineering the process of getting the local environment running. With time, we standardized environment setup across dev OSes to reduce onboarding friction — a small investment that saved hours per new engineer. It was frustrating — but it taught a lasting lesson on how important it is to get the code running on any laptop your new developer may be using.

At another project, a solo dev had created a fragile microservice setup, that the workflow of running Docker containers mounted to a local file system. Of course, you pay a little price for running processes as containers when your computer runs Linux.

But onboarding a new front-end dev with an older Windows laptop turned into a nightmare. They had to spin up ten containers just to view the UI. Everything broke — volumes, networking, container compatibility — and the setup very poorly documented. This created a major friction point during onboarding.

We ended up hacking together a Node.js proxy that mimicked the nginx/Docker configuration without containers. It wasn’t elegant, but it let the dev get unblocked and start contributing.

Takeaway: If your app only runs on one OS, your team’s productivity is one laptop away from disaster.

Tip: Ideally, aim for git clone && make up to have the project running locally. If it's not possible, then maintaining an up-to-date README file with instructions for Windows/macOS/Linux is a must. Nowadays, there are some programming languages and toolchains that don't work well on Windows (like OCaml), but the modern widely popular stack runs just fine on every widely used operating system; by limiting your local setup to a single operating system, it's often a symptom of under-investment in DX.

4. Technology Mismatch

Beyond architecture, your tech stack also shapes how painful microservices become — not every language shines in a microservice architecture.

• Node.js and Python: Great for rapid iteration, but managing build artifacts, dependency versions, and runtime consistency across services gets painful fast.
• Go: Compiles to static binaries, fast build times, and low operational overhead. More natural fit when splitting is truly needed.

It's very important to pick the right technical stack early on. If you look for performance, maybe look for the JVM and its ecosystem and ability to deploy artifacts at scale and run them in microservice-based architectures. If you do very fast iterations and prototype quickly without worrying about scaling your deployment infrastructure — you're good with something like Python.

It's quite often for teams to realise that there are big issues with their choice of technology that wasn't apparent initially, and they had to pay the price of rebuilding the back-end in a different programming language (like those guys were forced to do something about legacy Python 2 codebase and migrated to Go).

But on the contrary, if you really need to, you can bridge multiple programming languages with protocols like gRPC or async message communication. And it's often the way to go about things. When you get to the point that you want to enrich your feature set with Machine Learning functionality or ETL-based jobs, you would just separately build your ML-based infrastructure in Python, due to its rich ecosystem of domain-specific libraries, that naturally any other programming language lacks. But such decisions should be made when there's enough head count to justify this venture; otherwise, the small team will be eternally drawn into the endless complexity of bridging multiple software stacks together.

Takeaway: Match the tech to your constraints, not your ambition.

5. Hidden Complexity: Communication and Monitoring

Microservices introduce an invisible web of needs:

• Service discovery
• API versioning
• Retries, circuit breakers, fallbacks
• Distributed tracing
• Centralized logging and alerting