Microservices vs Modular Monolith: How to Choose

Teams building a new backend, or untangling an old one, often frame the choice as monolith versus microservices. That framing skips the option that suits many growing products best: a modular monolith, deployed as one application but organised internally into well-separated modules with clear boundaries. The microservices vs modular monolith decision is really about where you pay for complexity: inside one codebase, through discipline about boundaries, or across a network, through separate deployments, data stores and operational tooling. This guide compares the two honestly, explains the signals that justify splitting into services, shows how to build a modular monolith that can be split later if needed, and covers the mistakes that make either approach painful.

What each approach means

A modular monolith is one deployable application, usually with one database, divided into modules such as billing, customers, orders and reporting. Each module owns its data and logic, exposes a defined interface to the others, and is not allowed to reach into another module's internals. It is deployed, scaled and monitored as one unit.

Microservices split the system into separately deployed services, each owning its own data store and communicating over the network through APIs or messages. Each can be built, deployed and scaled independently, often by a separate team.

Both are different from a tangled monolith, where any code can call any other code and every table is shared. Many monoliths that teams want to escape are tangled rather than inherently monolithic.

Comparing microservices vs modular monolith

FactorModular monolithMicroservicesDeploymentOne deployment for the whole systemIndependent deployments per serviceOperational overheadOne application to run and monitorMany services, plus service discovery, tracing and networkingData consistencyTransactions across modules within one databaseConsistency across services needs events, retries and reconciliationTeam independenceShared release cycle; boundaries enforced in codeTeams can release independentlyScalingScale the whole applicationScale busy services individuallyFailure isolationA severe bug can affect the whole processFailures can be contained to one service if designed wellRefactoring boundariesMoving code between modules is relatively cheapMoving responsibilities between services is expensive

Neither column is better in general. The question is which costs your team and product can carry, today and over the next few years as the team grows.

When a modular monolith fits

A modular monolith is often the better starting point when:

• One team, or a few closely working teams, owns the system
• The domain is still being learned and boundaries may move
• Strong consistency matters, such as orders and stock updated together
• Operational capacity is limited, with no dedicated platform team

Martin Fowler's article Monolith First observes that almost all the successful microservice stories he heard of started with a monolith that got too big and was broken up, while systems built as microservices from scratch often ended up in serious trouble. That is one practitioner's observation rather than a rule, but it matches the logic: boundaries are easier to get right once you understand the domain.

Signals that justify splitting into services

Consider extracting a service when you see concrete pressures, not fashion:

• Teams block each other's releases, and release coordination has become the bottleneck
• One part of the system has very different scaling needs, such as heavy background processing
• A component needs a different technology or runtime for a good reason
• A part of the system needs stronger isolation, for security or reliability
• The module boundary is stable and well understood

Extract one service at a time, starting with the module whose boundary is cleanest and whose pressure is greatest. Our guide to monolith to microservices migration covers how to do that step by step without a risky rewrite.

Building a modular monolith that can split later

Good internal boundaries give you most of the benefits now and keep future options open:

• Organise code by business capability, such as billing or orders, not by technical layer
• Give each module its own tables or schema, and forbid other modules from querying them directly
• Communicate between modules through defined interfaces or internal events
• Enforce boundaries with tooling, such as architecture tests that fail the build when a module imports another module's internals
• Keep shared code small and generic

If a module later becomes a service, its interface becomes an API and its internal events become messages, with far less rework than untangling shared tables. Our guide to event-driven ERP shows how internal events can later carry across system boundaries.

Worked example: deciding for a growing SaaS backend

Suppose a SaaS company with eight engineers in two teams runs a tangled monolith for its scheduling product. Releases are slow and changes in billing break scheduling. It weighs the options and decides:

• Restructure into a modular monolith with modules for accounts, scheduling, billing, notifications and reporting, each owning its tables
• Add architecture tests to block cross-module access to internals
• Move notifications to internal events, because they are triggered from many places
• Revisit extraction in a year, starting with notifications if their volume grows faster than the rest, because their boundary is already event-based

The plan, module list and review point are the company's own. It chose not to split services now because two teams can coordinate releases, and its strongest pain, tangled code, is solved by boundaries rather than by distribution.

Operational realities of microservices

Teams sometimes underestimate what microservices require: automated deployment pipelines per service, centralised logging, distributed tracing, service-to-service authentication, versioned APIs, handling of partial failures and data consistency across services. Without these, debugging a request that crosses five services becomes guesswork. If you go this way, invest in the platform first, and budget for the people to run it. A small team can spend much of its time maintaining infrastructure rather than building the product, which is a poor trade unless the benefits are clear.

Common mistakes

• Treating the choice as tangled monolith versus microservices, ignoring the modular option
• Splitting into services before the domain boundaries are understood
• Microservices that share one database, combining the costs of both approaches
• A modular monolith whose boundaries are not enforced, so it becomes tangled again
• Extracting services for fashion rather than a concrete pressure
• Underinvesting in deployment, logging and tracing for distributed systems

Frequently Asked Questions

What is a modular monolith?

It is one deployable application divided into modules with clear boundaries, each owning its data and logic and communicating with others through defined interfaces.

When should I choose microservices over a modular monolith?

When independent team releases, very different scaling needs or strong isolation are concrete problems, and the boundaries between parts are stable and well understood.

Can a modular monolith be split into microservices later?

Yes. Well-enforced module boundaries, separate data ownership and internal events make extracting a module as a service much easier than untangling shared code.

Do microservices improve performance?

Not automatically. They let busy parts scale independently, but network calls add latency and complexity. Performance depends on design, not on architecture labels.

Choose the right backend structure

We help teams restructure backends into maintainable modules, and extract services when there is a clear reason. See our custom app development services, or talk to us about your architecture.

About the Author

Manav Sharma is the founder of Insyrge and a CRM solutions architect. He works across Zoho, Salesforce, HubSpot, Odoo and more, designing the CRM setups, integrations and automations that keep sales and operations teams working from the same data.