Microservices patterns are tools for specific problems, not a checklist for every system. Start by deciding whether independent deployment and ownership are worth the added system-wide complexity; then choose service boundaries around business capabilities and select communication, data, resilience, and operational patterns to fit the work. For many teams, a well-structured monolith is still the simpler and better starting point.
What microservices patterns solve—and what they cost
A microservices architecture divides an application into loosely coupled services that can be deployed independently. Patterns help teams manage the consequences of that choice: services need to find and communicate with one another, coordinate work without relying on a single database transaction, and remain observable and resilient when parts of the system fail.
They do not make a distributed application simple. Microsoft’s architecture guidance identifies added concerns such as service discovery, consistency, transactions, and interservice communication. AWS’s Implementing Microservices on AWS whitepaper puts the architecture decision plainly: “Deciding between microservices or monoliths should be made on a case-by-case basis, considering factors like scale, complexity, and specific use cases.”
When a monolith may be the better choice
Prefer a monolith, or begin with one, when the application is not yet complex enough to justify distributed services, when a small team can work effectively in one deployable unit, or when splitting the system would create more coordination and operational work than it removes. A monolith can still have clear internal modules and boundaries; those boundaries can make a later extraction more deliberate if the need becomes clear.
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What should justify the split
Look for concrete needs such as independently scaling distinct workloads, releasing parts of the system on different schedules, or giving teams clearer ownership of separate business capabilities. Consider the cost alongside the benefit: service deployment, monitoring, discovery, security, testing, and cross-service data consistency all require deliberate design.
Choose service boundaries around business capabilities
Begin decomposition with the business, not with technical layers or a desire to create many small services. Business capabilities and domain subdomains provide useful starting points because they group related responsibilities and help make ownership explicit. The pattern catalog also identifies self-contained services and service-per-team as possible approaches; neither is a universal rule.
Define ownership before extracting a service
For each proposed service, identify the business responsibility it owns, the team responsible for it, the interface other services use, and the data it controls. Microsoft’s guidance notes that when each service owns its data and schema, cross-service dependencies can be reduced and services can evolve independently. If a proposed boundary still requires frequent direct access to another service’s tables or coordinated releases, the boundary may not be independent enough—or the interaction may need to be redesigned.
Use the Strangler Fig pattern for gradual modernization
The Strangler Fig pattern is a migration strategy for replacing selected parts of a legacy system incrementally. Consumers continue to use an existing interface while a controlled boundary routes the functionality being replaced to the old implementation or a new service. Move one well-understood capability at a time, keep behavior consistent at that boundary, and retire the old implementation only after its responsibility has been transferred. It is not a one-step rewrite and does not remove the need to manage the transition between old and new behavior.
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API gateway
An API gateway gives clients a unified endpoint and can route requests, aggregate calls to multiple services, and centralize concerns such as authentication, SSL termination, and rate limiting. It is useful when clients should not need to know the internal service topology or when common client-facing policies belong at one entry point.
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A gateway also becomes an operational component of the system. Decide which responsibilities belong there and which belong in services, and avoid turning the gateway into a place where core business logic accumulates.
Backend for Frontend
A Backend for Frontend (BFF) creates a client-specific backend—for example, separate backends for mobile and desktop experiences—when those clients have meaningfully different requirements. It can tailor responses and aggregation to each client rather than forcing every client through one shared API shape. The trade-off is more backend components to build and operate.
| Decision | API gateway | Backend for Frontend |
|---|---|---|
| Best fit | A common entry point and shared routing or client-facing concerns | Distinct client types with different API or aggregation needs |
| Aggregation | Can aggregate multiple service requests | Can tailor aggregation to a particular client |
| Operational trade-off | Centralizes responsibility in a shared gateway | Adds client-specific backends to maintain |
These approaches can coexist: a system may use an edge gateway for shared routing and BFFs for client-specific behavior. Choose based on the clients and responsibilities you actually have rather than adopting both by default.
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Keep service data independent without ignoring consistency
Database per service
With database per service, each service controls its own storage and data management. This supports service autonomy and lets teams choose data technologies appropriate to their responsibilities. Other services interact through the owning service’s interface rather than treating its tables as shared infrastructure.
The cost is that a workflow spanning services no longer gets a single shared database transaction by default. Teams must decide how updates become consistent across service boundaries and what users should see while that work is still in progress. Microsoft notes that distributed transactions are often impractical in microservices, which makes application-level coordination an important design choice.
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Saga for a workflow across services
A saga sequences local transactions owned by different services. If a later step fails, compensating transactions can undo or counteract earlier steps. Use a saga when a business operation must progress across independently controlled stores and a single distributed transaction is not a practical fit.
A saga is not the same as a database transaction spanning the whole workflow: its steps and compensations are application behavior. Specify what happens when each step succeeds or fails, what compensation means for the business, and how the workflow’s progress is observed. The exact implementation depends on the workflow and should be designed and checked for that system.
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Related data patterns have different jobs
- API Composition: Combines query results from services that own their data. It is a way to answer a cross-service read without making one service the owner of all underlying records.
- CQRS: Separates read and write models when the two sides have different needs. It changes how queries and updates are represented; it is not itself a service communication protocol or a consistency guarantee.
- Domain events: Represent business events that other parts of the system may need to react to. Teams must still decide how events are published, consumed, and handled when processing is repeated or delayed.
- Event sourcing: Stores changes as a sequence of events rather than treating only the latest state as the record. It affects how state is reconstructed and managed, so it is a significant data-model choice rather than a default companion to microservices.
- Transactional outbox: Addresses the problem of atomically recording a database change and the message that announces it. It pairs a database transaction with an outbox record; message delivery and consumer behavior still need an implementation-specific design.
These patterns are related but not interchangeable. Select one only when it addresses a requirement in the system, and account for the extra models, coordination, and operational work it introduces.
Select communication and service discovery deliberately
Request-response calls
Remote procedure invocation supports synchronous request-response interactions. Use it when the caller needs a result to continue its own request and the dependency’s response time is part of the caller’s experience. A direct call also means the caller depends on the callee being reachable and responsive at that moment, so define timeouts and failure behavior.
Asynchronous messaging
Asynchronous messaging lets one service send a message without requiring the consumer to be online at the moment of sending; AWS describes a message broker between services as one way to do this. Messaging can reduce temporal coupling, but moves complexity into message handling and operations.
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Decide how consumers handle duplicate processing, whether message ordering matters, how delays affect the workflow, and what latency the business can tolerate. Do not assume a particular delivery guarantee without verifying the broker, configuration, and application implementation.
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|---|---|---|
| Interaction | Caller requests a result from a service | Sender publishes work or an event for a consumer |
| Availability coupling | Caller needs the callee to respond during the interaction | Consumer need not be online when a message is sent |
| Key design concerns | Latency, timeout, and failure response | Duplicate handling, ordering, delay, and message operations |
Service discovery
Service discovery answers how a caller or router finds a service instance when instance locations change. A service registry records service-instance locations. With client-side discovery, the client consults discovery information and chooses an instance; with server-side discovery, a router or load balancer performs that lookup and routing on the client’s behalf.
Choose based on where your platform places routing responsibility and what clients can reasonably know. Client-side discovery puts more of that work in clients; server-side discovery centralizes it in the routing layer. Neither changes the need to manage service availability and failures.
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Circuit breaker
A circuit breaker sits between a caller and a callee, tracks failures, and stops routing calls after a configured threshold is exceeded. When open, it returns an immediate failure rather than continuing to send requests to a service that appears unavailable. It can periodically check for recovery and allow calls again when conditions improve.
Set the threshold and recovery behavior to fit the service and its callers. Include useful logging and consider administrative control and multithreaded calls, as AWS circuit-breaker guidance highlights. A breaker changes how failures propagate; it does not repair the unavailable dependency.
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Coordinate retries with timeouts
Retries can help with transient failures, but unbounded or poorly timed retries can add load during an outage. Define a timeout and a failure policy for each call, and make retry behavior consistent with that policy. A circuit breaker, timeout, and retry are complementary controls, not substitutes: the timeout bounds waiting, the retry policy governs another attempt, and the breaker can stop further calls when failures pass a threshold.
Pick a deployment model that matches the workload
Deployment patterns include running multiple service instances per host, using a host or container per service instance, and serverless deployment. Compare them by isolation, density, workload needs, platform capabilities, and the operating burden your team can support.
| Deployment approach | Decision considerations |
|---|---|
| Multiple instances per host | Consider how host sharing affects density, isolation, and operating responsibilities. |
| Host or container per service instance | Consider the isolation required and the infrastructure needed to run and manage instances. |
| Serverless deployment | Consider workload fit and the capabilities and constraints of the platform you use. |
| Container orchestration | Container orchestration can handle scheduling, deployment, failure recovery, and autoscaling; Kubernetes is one example. |
Container orchestration is a platform approach, not a requirement for every microservices system. Microsoft’s guidance describes the operational tasks an orchestrator can handle; assess whether those capabilities fit your needs and whether the team can operate the platform.
Make a distributed system observable and testable
Observability across service boundaries
Plan for centralized logs, metrics, application performance monitoring, distributed tracing, exception tracking, and health checks. Each shows a different aspect of system behavior; no single signal explains every failure.
Distributed tracing follows a request across service boundaries and can help locate bottlenecks when one user request touches multiple services. Microsoft names OpenTelemetry as one framework for visibility into application health and performance. Connect the trace to useful logs and metrics so an operator can move from a failing request to the relevant service behavior.
Test interactions as well as individual services
Use service-component tests to exercise a service in its own context and consumer-driven contract tests to check expectations between a consumer and provider. End-to-end tests can cover important user journeys, but they should not be the only way you test service interactions. Microsoft cautions that testing dependencies and refactoring across service boundaries can be challenging; component and contract tests help address those risks at a narrower scope.
A practical way to choose and introduce patterns
- State the problem in business terms. Identify the capability, client requirement, workflow, failure mode, or operational constraint that needs attention.
- Check whether a service boundary is justified. Name the owner, interface, and data responsibility. If the change does not need independent deployment or ownership, an internal module may be enough.
- Choose only the pattern that addresses the problem. For example, a gateway shapes client access, a saga coordinates a multi-service workflow, discovery locates instances, and a circuit breaker changes failure behavior.
- Write down the trade-off and failure behavior. Specify what happens on a timeout, partial workflow failure, duplicate message, unavailable dependency, or client-specific API change where relevant.
- Plan deployment, observability, and tests before rollout. Define how the service will be discovered, monitored, and tested across its boundaries, and how the team will operate its deployment model.
- Introduce changes incrementally. For legacy systems, route selected functionality through a controlled boundary and replace it in manageable pieces rather than assuming a full rewrite is safer.
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