Python gives API teams an unusual amount of choice. Flask, FastAPI, and Bottle can all expose an HTTP endpoint in a few lines, but they optimise for different kinds of simplicity. Choosing well means looking beyond the first route and asking how the service will be validated, tested, deployed, and changed.

Flask: flexible and proven#

Flask is the dependable general-purpose option. Its request model is easy to understand, its extension ecosystem is mature, and experienced Python developers can recognise a Flask application quickly. It works especially well for internal tools, integration services, conventional REST APIs, and products that need freedom without inventing the entire HTTP layer.

That flexibility requires team conventions. Flask does not prescribe validation, dependency injection, database access, or project structure. Decide those early. Separate route handling from business logic, use an explicit validation library, create an application factory, and keep configuration outside code. A small Flask service stays pleasant when its boundaries are deliberate.

Choose Flask when ecosystem maturity, compatibility, and incremental design matter more than automatic typing or maximum asynchronous throughput.

FastAPI: typed contracts and async I/O#

FastAPI is a strong fit for typed APIs, data products, model-serving endpoints, and services that benefit from generated OpenAPI documentation. Request and response models make contracts visible, validation happens consistently, and editor support improves because types are central rather than optional decoration.

Its ASGI foundation is valuable for workloads that spend time waiting on databases, queues, or other services. It does not make blocking code asynchronous, however. A synchronous database driver or CPU-heavy model inside an async route can still block useful work. Profile the complete request path and move expensive computation to an appropriate worker.

FastAPI’s dependency system can elegantly express authentication and shared request concerns, but deeply nested dependencies become difficult to trace. Keep business rules in ordinary Python objects that can be tested without constructing a web request.

Choose FastAPI when contract clarity, automatic documentation, async integrations, or proximity to data and AI workloads are central.

Bottle: minimal means genuinely minimal#

Bottle fits utilities, prototypes, embedded tools, educational projects, and tiny services where a single-file deployment is a real advantage. It has no required external dependencies and very little framework machinery.

That economy becomes a trade-off as operational requirements grow. Authentication, structured validation, migrations, telemetry, and team-wide conventions must come from elsewhere. Bottle is successful when the problem remains intentionally small—not when a growing platform is forced to pretend it is small.

Use the deployment test#

Before deciding, sketch the production service. How will it authenticate callers, validate input, access data, report errors, emit traces, run background work, and publish an API contract? How many developers will maintain it? Will it primarily wait on I/O or perform CPU-intensive work?

Build one representative endpoint rather than a hello-world route. Include validation, a database query, an external failure, a test, and telemetry. The framework that makes this path clearest is usually the right choice.

Microframeworks reduce ceremony, not engineering responsibility. Flask offers mature flexibility, FastAPI offers typed modernity, and Bottle offers radical minimalism. Select the smallest foundation that still makes the service’s real operational needs explicit.