Short answer: most startups should begin with a monolith, ideally a well-structured, modular one, and move to microservices only when specific, measurable problems justify the cost. Microservices solve organizational and scaling problems that early-stage companies usually don't have yet, while adding complexity that early-stage companies can least afford.
This isn't an argument against microservices. They are a legitimate tool, and large companies use them successfully. The point is about timing. Architecture decisions made before you understand your product, your users, and your team tend to be expensive guesses.
A monolith is a single deployable application. The code for user accounts, billing, notifications, and everything else lives in one codebase and runs as one unit, usually with one database.
A microservices architecture splits the application into many small, independently deployable services. Each service owns a specific capability and communicates with others over the network, through APIs or message queues. Teams can, in theory, build, deploy, and scale each service on its own.
The trade-off is simple to state. Microservices exchange code complexity for operational and coordination complexity. Calls that were function calls inside one process become network calls that can fail, time out, or return stale data. That exchange pays off when you have many teams and clear boundaries. It rarely pays off for a team of three to fifteen engineers.
Microservices only work well when service boundaries match real boundaries in the business. Early in a startup, those boundaries are guesses. Your product pivots, features merge, and the concept you called "orders" turns out to overlap heavily with "subscriptions."
In a monolith, fixing a wrong boundary means moving code and running tests. In a microservices system, it can mean rewriting APIs, migrating data between databases, coordinating deployments, and handling backward compatibility. Software architect Martin Fowler made this argument in his "MonolithFirst" essay: almost all successful microservice systems he had seen started as a monolith that was later broken up, while systems built as microservices from scratch often ran into serious trouble.
Once functionality is spread across services, you inherit problems a monolith avoids:
Network failure: every service call can be slow, fail, or time out, so you need retries, timeouts, and fallbacks.
Data consistency: a single database transaction becomes a multi-service workflow that needs patterns like sagas or eventual consistency.
Debugging: tracing one user request across many services requires distributed tracing and centralized logging.
Testing: integration testing across services is harder than running one application's test suite.
Versioning: services must stay compatible with each other as they change.
None of these are unsolvable, but each requires tooling, expertise, and ongoing attention. A startup pays that cost before it has earned any of the benefits.
Microservices usually bring along container orchestration, service discovery, API gateways, per-service CI/CD pipelines, monitoring, and secrets management. Someone has to build and maintain all of that.
In a small team, that "someone" is an engineer who would otherwise be building product features. Every hour spent on infrastructure plumbing is an hour not spent on what customers actually pay for. A monolith can run on a simple setup, such as a managed platform, a single database, and a straightforward deployment pipeline, and still serve a surprising amount of traffic.
The most common justification for early microservices is scale: "We'll need to handle millions of users." But most startups fail from lack of product-market fit, not from an inability to scale. Optimizing for a problem you may never have is a poor trade against shipping faster today.
A monolith keeps feedback loops short. One repository, one deployment, one place to search for bugs, and refactoring tools that work across the whole codebase. When you're running experiments weekly, that simplicity is a real competitive advantage.
Initial development speed
Monolith: Fast. One codebase and one deployment.
Microservices: Slower. Infrastructure and service contracts must be set up first.
Operational complexity
Monolith: Low.
Microservices: High. Requires orchestration, tracing, and per-service pipelines.
Changing boundaries
Monolith: Easy refactoring within one codebase.
Microservices: Costly, because changes span services, APIs, and data stores.
Debugging
Monolith: A single stack trace and a single log stream.
Microservices: Requires distributed tracing across services.
Data consistency
Monolith: Straightforward database transactions.
Microservices: Eventual consistency and coordination patterns.
Independent scaling
Monolith: Scale the whole application together.
Microservices: Scale only the services that need it.
Team autonomy at scale
Monolith: Can cause merge conflicts and coordination friction in large organizations.
Microservices: Lets many teams deploy independently.
Best fit
Monolith: Early-stage products, small teams, evolving requirements.
Microservices: Large organizations, stable domains, clear scaling bottlenecks.
"Monolith" doesn't mean "tangled mess." A modular monolith is a single deployable application organized into well-defined modules with explicit boundaries and limited, deliberate dependencies between them.
In practice, that means:
Grouping code by business capability (billing, accounts, notifications) rather than by technical layer alone
Having modules talk through defined interfaces instead of reaching into each other's internals
Keeping each module's data access private to that module, even if everything shares one database
Enforcing boundaries with code structure, linters, or dependency rules
This approach gives you most of the organizational clarity people want from microservices without the network and operational costs. It also keeps your options open: a well-bounded module is far easier to extract into a separate service later than logic scattered across the codebase. Shopify, for example, has publicly described using a large modular monolith rather than a fully distributed architecture.
"Almost always" leaves room for exceptions. Consider splitting out services when you see real signals, not hypothetical ones:
Organizational friction: many teams are blocking each other in one codebase or release process, and coordination costs are visibly slowing delivery.
Uneven scaling needs: one component (such as video processing or search) needs very different resources from the rest, and scaling the whole application for it is wasteful.
Different technical requirements: a component genuinely needs a different language, runtime, or isolation level.
Fault isolation needs: a failure in one area must not take down the whole product, and modular boundaries inside the monolith aren't enough.
Compliance or security isolation: a sensitive component needs to be separated for regulatory or risk reasons.
Stable, well-understood boundaries: you've run the product long enough to know where the seams really are.
A useful test: can you name the specific problem splitting would solve, and measure it? If the answer is "it's the modern way" or "we might need it," it's too early.
Splitting incrementally, extracting one service at a time while the monolith keeps running, is generally safer than a big-bang rewrite. This is often called the strangler fig approach.
Choosing microservices for résumé or fashion reasons. Architecture should serve the product and team, not trends.
Building a "distributed monolith." This happens when services are split but remain tightly coupled, so they must be deployed together and fail together. You get the costs of both worlds and the benefits of neither.
Treating "monolith" as a license for messy code. Without internal boundaries, the codebase becomes hard to change, and later extraction becomes painful. Structure matters even when everything deploys together.
Splitting by technical layer instead of business capability. Separate "database service" or "API service" boundaries create dependencies on every feature. Boundaries should follow business domains.
Sharing one database across "separate" services. If multiple services read and write the same tables, they are not really independent.
Ignoring the real bottleneck. Teams sometimes reach for microservices to fix slow queries, poor caching, or unoptimized code, which are usually cheaper to fix directly.
Rewriting everything at once. Big-bang migrations are risky and often stall feature development for months.
You can keep your future options open with a few habits:
Organize by domain. Structure code around business capabilities, not just controllers, models, and utilities.
Define module interfaces. Let modules call each other through explicit public interfaces rather than internal functions or tables.
Keep data ownership clear. Even in a shared database, treat each module's tables as private to it.
Use asynchronous patterns where natural. Background jobs and internal events decouple modules and map well to message queues later.
Invest in the basics. Automated tests, continuous integration, logging, and monitoring help whether you ever split or not.
Measure before you split. Track performance, deployment frequency, and team bottlenecks so any future decision rests on evidence.
Not inherently. A well-built monolith can scale vertically and horizontally by running multiple identical instances behind a load balancer. Many successful products serve large audiences this way. Independent scaling of components is a genuine advantage of microservices, but it matters only once specific parts have clearly different demands.
It is a single deployable application with strongly defined internal modules and controlled dependencies between them. It offers much of the organizational clarity of microservices while avoiding network and operational overhead, and it makes later extraction easier.
There's no universal number. The signal is coordination pain: teams stepping on each other, slow or risky releases, and merge conflicts that can't be solved with better module boundaries. A team of a few engineers rarely has this problem, while dozens of engineers across several teams often do.
Yes, and it's the commonly recommended path. Extract services gradually, starting with a well-bounded module that has a clear reason to be separate. A modular design makes this considerably easier.
Yes, and they did so to address problems of very large scale and many teams. Their needs are different from an early-stage startup's. Notably, Amazon's Prime Video team published a case study describing how consolidating one monitoring workload from distributed components into a single process reduced its infrastructure costs, a reminder that the right architecture depends on the workload.
Not automatically. Network calls between services add latency compared with in-process calls. Microservices can improve team speed at large scale, and allow targeted scaling, but they don't make an application faster by default.
Serverless is a deployment and operating model, not an architecture style. You can run a monolith on serverless infrastructure, and you can build microservices with it. It can reduce operations work, but it doesn't remove the design questions about boundaries and data.
Occasionally. If your team has deep prior experience with the domain and the tooling, the boundaries are already well understood, and hard requirements (such as strict isolation) exist from day one, it can be reasonable. For most startups, those conditions don't hold.
The case for starting monolithic comes down to uncertainty and constraints. Early on, you don't know where your boundaries belong, your team is small, and your biggest risk is building something nobody wants, not failing under load. A monolith, built with clear internal modules, lets you move quickly now and leaves the door open to split later, when real evidence tells you where and why.
Choose the simplest architecture that works today, structure it so it can evolve, and let measurable problems, not trends, drive the move to microservices.
Next step: Before your next architecture discussion, write down the specific problem you expect microservices to solve and how you'd measure it. If you can't, a modular monolith is probably the right call for now. For deeper reading, explore Martin Fowler's writing on monolith-first design and Sam Newman's work on migrating from monoliths to microservices.
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