A small restaurant runs perfectly well from one kitchen. Then it grows. The bakery needs its ovens at 4am, the grill is busiest at 8pm, and every new dish means someone waits for space at the same counter. Eventually the owner gives the bakery its own room, but not before working out exactly what it needs and how orders will flow between the two. Python microservices follow the same logic, and teams that bring in experienced Python development services usually start by asking which part of the kitchen truly needs a room of its own.
Python is a strong choice for microservices, especially with FastAPI, but splitting a monolith only pays off when separate parts of the system need to deploy, scale or evolve independently. For most early products, a well-structured modular monolith is faster and cheaper. This guide explains when to split, which framework to use, how to extract services safely, and what production really requires.
The guidance reflects how Acquaint Softtech builds Python backends as an ISO 27001-certified technology partner, drawing on 1,300+ projects and 70+ in-house engineers serving clients in the USA, UK, Europe, and Australia.
Is Python good for microservices?
Yes. Python is well suited to microservices that are I/O-bound, such as APIs, integrations and data pipelines, and it is the natural choice for services that run machine learning models. Async frameworks like FastAPI handle many concurrent requests efficiently.
The honest limitation
CPU-heavy work in a single Python process is limited by the Global Interpreter Lock. In practice, teams run multiple worker processes, push heavy jobs to task queues, or write hot paths in faster languages. Newer Python releases also offer an optional free-threaded build, though library support is still catching up.
When to split a monolith
Split when the monolith is slowing the business down in ways a better internal structure cannot fix.
| Signal | What you notice | Why a service helps |
| Deploy contention | Teams queue to release | Independent deployments |
| Uneven load | One feature needs 10x capacity | Scale that part alone |
| Different runtime needs | ML models need GPUs | Separate infrastructure |
| Fault spread | One bug takes everything down | Failures stay contained |
| Team growth | Many teams in one codebase | Clear ownership |
When not to split yet
Microservices add network calls, distributed data, more deployments and harder debugging. If you have one small team, an unclear domain or no automated deployments, splitting usually creates more problems than it solves.
Try a modular monolith first
Organise the existing codebase into clear modules with defined interfaces and no shared database tables between them. If those modules stay cleanly separated for months, they become easy, low-risk candidates to extract later.
Which Python framework is best for microservices?
FastAPI is the best default for most new Python microservices because it is async, fast, uses type hints with Pydantic validation and generates OpenAPI documentation automatically.
| Framework | Best for | Trade-off |
| FastAPI | APIs, async I/O, ML serving | Fewer built-ins than Django |
| Flask | Small, simple services | You assemble most pieces |
| Django | Large core services, admin needs | Heavier for tiny services |
Mixing frameworks is fine
Many teams keep a Django core for accounts and admin, and add FastAPI services around it for high-traffic or ML workloads.
How to split a monolith step by step
The safest approach is the strangler fig pattern: extract one service at a time while the monolith keeps running, and route traffic to the new service gradually.
1. Map the boundaries
Identify business capabilities, such as billing, notifications or search, that have clear inputs, outputs and data ownership.
2. Extract the easiest valuable service first
Choose something low-risk with real benefit, such as notifications or a recommendation engine, to prove your tooling and process.
3. Give each service its own data
A service should own its database. Sharing tables between services recreates the monolith, only slower.
4. Route traffic gradually
Put an API gateway or proxy in front, send a small share of traffic to the new service, compare results, then switch over fully.
Each extraction needs people who understand both the old code and the new architecture. Many teams hire Python developers with FastAPI and Docker experience to run extractions without pausing feature work.
Communication between services
Use synchronous calls when the caller needs an immediate answer, and asynchronous messaging when it does not.
- REST or gRPC: for request-response calls, with timeouts and retries.
- Message brokers: RabbitMQ, Kafka or Redis Streams for events between services.
- Task queues: Celery or similar for background work inside a service.
| @app.get(“/orders/{order_id}”)async def get_order(order_id: int) -> Order: return await orders.fetch(order_id) |
Design for failure
Every network call can fail. Add timeouts, retries with backoff, circuit breakers and idempotent handlers so a slow service cannot drag down the rest.
Running microservices in production
Microservices move complexity from code into operations. Plan for it before the first extraction.
- Containers: package each service with Docker for consistent builds.
- Orchestration: run them on Kubernetes or a managed container platform.
- CI/CD: automated tests and deployments for every service.
- Observability: structured logs, metrics and distributed tracing with OpenTelemetry.
- Contracts: versioned APIs so services can change without breaking each other.
Do not skip the platform work
Without container orchestration and monitoring in place, microservices become hard to run safely. Teams without that experience in-house often bring in hired DevOps developers to set up pipelines, clusters and alerting first.
Making the call
The restaurant did not build a second kitchen because it was fashionable. It did so because the bakery’s needs had become genuinely different. Treat your monolith the same way. Start modular, split only where deployment, scaling or team pressure demands it, extract one service at a time, and put the operational foundations in place before you need them.
FAQs
Is Python good for microservices?
Yes, especially for I/O-bound APIs, integrations and ML services. FastAPI makes Python microservices fast and well documented.
When should you split a monolith into microservices?
When parts of the system need to deploy, scale or evolve independently, and a modular structure inside the monolith is no longer enough.
Which Python framework is best for microservices?
FastAPI for most new services, Flask for very small ones, and Django for larger core services with admin needs.
Should every service have its own database?
Yes. Each service should own its data and share it through APIs or events, not shared tables.
