Monolith vs Microservices for Startups

We get asked about monolith vs microservices by founders who have not shipped a first version yet, and the question usually arrives with a reference to how Netflix or Amazon build software. A monolith is an application built and deployed as one unit, with its interface, business logic and data access running in a single process. Microservices split the same application into small services that each run, deploy and often store data on their own.

The choice matters because it decides how much of your early engineering time goes into features and how much goes into plumbing between services. For a product still looking for its market, that split is the whole game.

This post covers why we start most products as a monolith, how the two options compare for a small team, a step-by-step way to decide, how to build a monolith that can split cleanly later and the signals that a service is ready to leave.

Why Most Startups Should Begin With a Monolith

Most startups should begin with a monolith because the first job of an early product is to learn what users want, and a single codebase lets a small team change direction in hours rather than weeks. Every hour spent on service boundaries, network calls and deployment pipelines is an hour not spent on the features that prove the product works.

Martin Fowler made the same observation in 2015, writing that "almost all the successful microservice stories have started with a monolith that got too big and was broken up". In the same piece he notes that systems built as microservices from scratch have usually ended up in serious trouble, and that even experienced architects find it hard to draw service boundaries correctly at the start.

That last point is the one founders underestimate. Boundaries between services are expensive to move, because moving them means changing APIs, data ownership and deployment at the same time. Inside a monolith the same change is an ordinary refactor, and early products refactor constantly as the domain becomes clear.

The examples people cite for microservices also started elsewhere. Netflix describes how it migrated from a monolithic app to hundreds of microservices over a cloud migration that began in 2008 and finished in 2016. That was a response to scale the company already had, not a starting architecture for a product with no users.

We weigh the cost of the opposite choice in plain terms. A team of three running many services would spend much of its week on service discovery, retries and debugging requests that cross several logs, and the product roadmap would slip while the infrastructure grows.

Monolith vs Microservices Compared for an Early Team

For an early team, the monolith vs microservices comparison comes down to speed and simplicity today against independent scaling and team autonomy later. AWS describes a monolithic architecture as "a traditional software development model that uses one code base to perform multiple business functions", while microservices compose software into small independent components.

Atlassian, which moved its own products to microservices, lists the costs plainly. Its guide names development sprawl, exponential infrastructure costs, added organizational overhead and debugging challenges as the main drawbacks, and notes that each microservice has its own logs.

The same guide records that Atlassian had about 15 microservices in January 2016 and more than 1,300 later, which tells you microservices are a tool for an organization of that size. We read the table as a sequence, with the monolith column describing the right starting point and the microservices column describing a destination some products reach.

How to Decide Between a Monolith and Microservices

Decide between a monolith and microservices by checking six conditions in order, and start with a monolith unless several of them clearly point the other way. We run through these with founders before any architecture work, because the answers are usually visible from the team plan and the roadmap.

1. Count the Teams That Will Ship Independently

Microservices solve a coordination problem between teams. If one small team builds the whole product, there is nobody to coordinate with, and splitting the code adds the overhead without the benefit.

A useful test is to ask who would own each proposed service. If the same few engineers own all of them, a monolith with clear modules gives the same separation at a fraction of the cost.

2. Check How Stable Your Domain Boundaries Are

Service boundaries should follow stable parts of the business, often called bounded contexts. Early in a product, those parts are still moving as you learn which workflows users care about.

If you cannot describe your main domains and how they hand data to each other without hedging, the boundaries are not ready. Drawing them into separate services now means redrawing them later at a much higher price than moving code between modules.

3. Look for a Component With Its Own Scaling Profile

A real reason to extract a service is a component whose load or resource needs differ sharply from the rest, such as video processing, a search index or a heavy reporting job. That component may need different hardware or many more instances than the core app.

Even then, the first step is often a background worker or a queue inside the monolith rather than a full service. Our guide on when to introduce a queue system into your architecture covers that middle ground.

4. Price the Operational Work Honestly

Each service needs its own build, deployment, monitoring, alerting and on-call attention. Multiply that by the number of services and compare it with the size of the team that will carry it.

If nobody on the team has run distributed systems in production, the learning curve lands at the worst moment, while the product is trying to find users. A monolith keeps that work to one pipeline and one set of dashboards.

5. Decide How Often Parts of the Product Must Release Separately

Independent deployment matters when one part of the product must ship on a different rhythm from the rest, for example because of separate customers, regulators or release windows. Most early products do not have that constraint, so one deploy for everything is simpler and safer.

If the need appears later, it usually appears for one area first, which points to extracting that area alone rather than splitting the whole system.

6. Ask What Happens If the Product Pivots

Early products pivot, and a pivot changes which parts of the system matter. In a monolith a pivot means rewriting some modules and deleting others in one repository.

With microservices the same pivot can strand whole services, contracts and data stores that no longer match the business. If a pivot is a real possibility in the next year, that alone is a strong argument for one deployable application.

When most of these six answers point to a monolith, which they do for nearly every product before market fit, we build one and revisit the decision when the team or the load changes. The point is not loyalty to an architecture but spending effort where the product needs it now.

How to Build a Modular Monolith That Can Split Later

A modular monolith is a single deployable application whose internal modules have clear boundaries, so any of them can become a separate service later without a rewrite. We treat this as the default shape for new products, because it keeps the speed of a monolith and the option value of microservices.

The practical rules are simple and they compound over time:

• Organize code by business domain, such as billing, scheduling or reporting, rather than by technical layer alone.
• Let modules talk to each other only through explicit interfaces, never by reaching into another module's tables.
• Give each module ownership of its own data, even if every module lives in the same database for now.
• Keep shared code small and boring, limited to logging, configuration and similar utilities.
• Add automated checks that fail the build when one module imports another module's internals.

When one module does need to leave, Fowler's strangler fig approach describes the route. New functionality grows around the old system, traffic moves over step by step and the old code is retired once nothing calls it, so investment and returns arrive gradually.

These habits are part of the engineering principles we work by on every product engineering engagement, because they cost little on day one and save a rewrite in year two. Our guide on how to structure your codebase so it survives scaling goes further into module layout.

Signals That a Service Is Ready to Leave the Monolith

A service is ready to leave the monolith when a specific, measured problem keeps recurring in one module and splitting it would remove that problem. We look for evidence, not a general feeling that the codebase has become large.

• One module needs far more compute or memory than the rest, and scaling the whole application to serve it is wasting money.
• A separate team now owns one domain and keeps waiting on other teams' releases to ship its work.
• A module's failures keep taking down unrelated parts of the product, and isolation would contain them.
• The module's interface has been stable for months, so its boundary is unlikely to move again soon.

If none of these is true, the better investment is usually performance work inside the monolith. Our founders' guide to software scalability at ten times the traffic covers the database, caching and infrastructure steps that come first.

Start With One Deployable and Earn Each Split

The monolith vs microservices decision for an early-stage product is rarely close. Build one well-structured monolith, keep its modules honest, measure where it strains and extract a service only when a team or a workload clearly needs one. We would rather a product reach real users on a simple architecture than stall on infrastructure it does not yet need.

If you are weighing an architecture decision for a new product, the team at Better Software can talk it through with you.

Frequently Asked Questions

Are Monoliths Better Than Microservices?

Neither is better in general. We find a monolith better for small teams and products still changing quickly, because it is faster to build and easier to debug. Microservices become the better fit when several teams need independent releases or parts of the system need separate scaling.

Is Netflix Monolithic or Microservices?

Netflix runs on microservices today, but it started as a monolith. We point founders to its own account, which describes moving from a monolithic app to hundreds of microservices during a long cloud migration, a change driven by scale it already had.

Are Microservices Still a Thing?

Yes, microservices are still widely used, mostly by larger organizations with many teams and heavy, uneven workloads. We see more early teams choosing modular monoliths instead, because they keep clean boundaries without the operational cost of running many services before the product needs them.

When to Use Monolith vs Microservices?

We use a monolith when one small team is building a product whose domain is still changing. We move to microservices when separate teams own stable domains, need independent releases or have components with very different scaling needs that one application cannot serve well.

What Is an Example of a Monolith?

A typical example is a web application where the user interface, business logic and database access live in one codebase and deploy together. We build many early products this way, often as a single application with modules for accounts, billing and core workflows sharing one database.