Software architecture: Monolith vs Microservices

If you've learned to code with AI in the last two years, there's a decent chance you've never consciously thought about software architecture: you've prompted the AI assistant, which has then scaffolded the project for you. That's not a critique: in 2026, this is the reality of how software gets built.

At some point, your app grows. As a result, things get slow or fragile, and you start wondering how to improve your software. You quickly find blogs explaining "monolithic vs microservices" architectures and how they impacts software performance… And so, you try to figure out what kind of architecture you’ve actually built. 

Here's the uncomfortable part: the standard advice you'll find is mostly from 2015 to 2020, and the ground has shifted. Teams have spent years breaking their systems into microservices, and are now putting them back together. AI-assisted development is one of the reasons why.

This post covers what both architectures actually are, why the industry swung from one to the other, and the question nobody had to ask before: what happens to this debate when a large share of your code is written and maintained by AI?

The two architectures explained

A monolith is one application. One codebase, one build, and one deployment. It includes everything from user accounts, to billing, product logic, and the admin panel. All of these features live in the same process and can talk to each other through function calls. 

A microservices architecture splits that system into isolated and independently deployed services. The billing and user service both run on their own and they communicate over the network through APIs or message queues. This allows each service to have its own databases, programming language, and even a team that maintains it. 

If you've been vibe coding, it almost certainly has resulted in monolithic code. You might have a Next.js app with API routes and a Postgres database, and that’s fine. It's arguably the correct starting point, and it always was, even during the years of microservice popularity when it became unfashionable.

Why everyone left the monolith

The classic case is Netflix. Their monolith worked well until it didn't: as the platform and the engineering organization grew, a single giant codebase became the bottleneck. A small change in one function meant rebuilding, retesting, and redeploying the whole platform. So around 2009 they began splitting it apart, and their migration became the template a generation of companies copied.

The complaints that fueled that migration are worth listing, because we're going to revisit them:

• Slower development. A large monolith is complex, and complexity slows every change.

• Scaling. You can't scale one hot component. If the video encoder component needs to scale, you need to scale the whole application, which is not so practical.

• Reliability. A bug in one module can take down the entire application.

• Technology lock-in. The whole app shares one framework and one language. Upgrading either is expensive, and you can't adopt a better tool for just one part.

• Deployment. A one-line fix requires redeploying everything, which makes teams deploy less often, which makes each deployment riskier.

Every problem on this list is valid. But notice something: most of them are about humans. Slower development is about how much complexity fits in a developer's head. Deployment fear is about human coordination across teams. Even the technology argument is partly about hiring and team autonomy. Microservices were, to a large extent, an organizational solution wearing a technical costume. You split the system so that thirty teams could work simultaneously without stepping on each other.

This human element matters a lot as you’ll see how the AI narrative is going to change this.

2026: the pendulum swings back

The swing back started before AI entered the argument. In 2023, Amazon's own Prime Video team published a case study describing how they moved a specific workload from microservices back to a monolith and cut its infrastructure costs by around 90%. 

This retreat to monolithic software design has become a pattern. According to the 2025 CNCF survey, 42% of organizations that initially adopted microservices have consolidated at least some services back into larger deployable units. As so many organizations are considering this change, playbooks have been created to guide engineering organizations on how to migrate back to a monolithic architecture.

Why do so many companies regret their change to microservices? Because microservices trade code complexity for operational complexity, and that trade is brutal when you don't have the team size to justify it. Instead of debugging a function call, you're debugging a network. You need distributed tracing to debug a single request. Additionally, you need to handle partial failures, retries, eventual consistency, and version skew between services. A bug that would have been a stack trace in a monolith becomes an afternoon of correlating logs across five dashboards. 

Many teams also discovered they hadn't built microservices at all, but fifty deployables that all have to change together: a distributed monolith, which combines the drawbacks of both approaches.

The landing spot for most of these teams is not the old-school big ball of spaghetti. It's the modular monolith: a single deployment unit organized internally into clear modules with strong boundaries. One deploy, one process, but the billing code cannot casually reach into the user module's database tables. You get most of the organizational discipline of microservices without paying network tax on every function call.

So where does AI come in?

Now the interesting part. Take that list of monolith disadvantages and re-read it in a world where an AI agent writes or reviews a large share of your code.

"Slower development speed." → Mostly changed

This was the strongest argument for splitting, and it's the one AI erodes most. The reason big codebases slowed humans down is cognitive: no one can hold a million lines in their head. However, AI agents don't get tired of reading code and they're good at navigating large codebases.

"Technology lock-in" and "lack of flexibility." → Weakened argument

One appeal of microservices was rewriting a single service in a better language. But large mechanical migrations, the kind teams avoided for years, are now dramatically cheaper when an AI does the bulk of the transformation and humans review. The monolith's technology choices are less of a life sentence than they used to be.

"Reliability." → Unchanged. 

If your monolith's payment module throws an unhandled exception that crashes the process, your whole app is down, no matter who wrote the code. 

"Scalability." → Mostly unchanged.

AI can't make one process scale its components independently. What has changed is how often this matters: most applications never reach the scale where per-component scaling beats simply running more copies of the monolith. Netflix problems remain real for Netflix but they are not a real problem for most organizations.

"Deployment." → Partially changed. 

Redeploying everything for a one-line fix is still the deal you sign with a monolith. But the fear behind that complaint came from slow builds and manual testing, and AI-generated test suites plus modern CI pipelines have shrunk both. A monolith can now deploy in a matter of minutes whereas a decade ago it could take 2 hours. 

In summary, AI weakens the disadvantages of monoliths that were about human cognitive limits, and leaves untouched the ones that are about physics and process isolation. Since the cognitive arguments were the main reason mid-sized teams adopted microservices, the case for defaulting to microservices has genuinely gotten weaker. It only really exists for large organizations that run high-demand services.

What should you actually do: monolith or microservices?

If you're a vibe coder leveling up, here's the honest 2026 playbook.

1. Start with a monolith

One repo, one deployment process, and one database. This was always the right call for small teams, and AI assistance has extended how far it carries you, because your agent works best when it can see everything.

2. Add modularity before you add services

When the codebase starts feeling tangled, don't reach for Kubernetes. Reach for folder structure and boundaries: a billing module, a users module, and each with a clear public interface. Ask your AI assistant to help enforce this during code reviews. By doing this, you’re building a solid software application while also preparing for a potential future split because you’ve set well-drawn module boundaries that can become services.

3. Extract a service only for a concrete, named reason

Good reasons exist: a component with genuinely different scaling needs like video processing requires isolation. But most other components do not require extensive scaling needs and can be embedded in your monolith.

4. Learn the concepts even if you never split

Understanding bounded contexts, API contracts, and failure isolation makes you better at designing monoliths and writing software. The microservices era wasn't wasted, it taught the industry to think in boundaries. We're just learning that boundaries don't require a network between them.