API framework · in regular use
FastAPI agency in Nice
The Python framework we choose for a pure API, or for a project touching data and AI.
What we do with it
FastAPI at our clients, concretely
It is our stated rule, written on the software development pillar page: FastAPI for a pure API or a data-oriented project, Laravel as soon as there is a back-office to maintain.
The limits
When we recommend something else
If the application needs a full admin interface, the advantage disappears.
Our work
Projects we delivered with FastAPI
Paradigm
Platform for a Nice business centre: registered address, digitised mail, messaging and assistant.
FastAPI: API of the OCR service, called by Laravel for each incoming letter.
Read the Paradigm case studySilversun — prospection
B2B prospecting tool for renewable energy: geocoded projects and enriched prospects.
FastAPI: Asynchronous API and background jobs.
Silversun — suivi de chantier
Solar project progress timeline for the end customer, fed by the CRM.
FastAPI: API and secure customer sign-in.
LLM Monitor
SaaS that measures how visible brands are in AI answers.
FastAPI: Platform API and processing workers.
Read the LLM Monitor case studyOrdelya
Restaurant management platform: back office, tablets, kiosks, screens and website.
FastAPI: API feeding the back office, tablets and screens.
Method
How we bring FastAPI into a project
We begin by writing the API contract with the teams who will consume it: endpoints, input and output formats, error cases. Those formats become Pydantic models, and an early skeleton is deployed so the client application can connect to it before the logic is finished. The repository is created in your name at this stage.
The architecture keeps three layers apart: routes, which only receive and respond; services, which hold the logic; and data access through SQLAlchemy with Alembic migrations. FastAPI's dependency system supplies the database session, the authenticated user and settings, so each can be swapped for a test double. pytest and the HTTPX test client cover every endpoint, error paths included. Long calculations or calls to an AI model go onto a queue, so the API responds without waiting for them to finish.
In production the application runs under Uvicorn with several worker processes, in a container behind a reverse proxy, on hosting set up under your own credentials. The version number is part of the API paths, so the contract can evolve without breaking software that is already connected. We hand over an operations note and the procedure for updating dependencies.
For your business
What it changes in practice
For people using the applications that rely on the API, the visible effect is responsiveness: a call waiting on an external service or an AI model does not hold up the others, and a long job carries on in the background while the screen confirms the request has been received. Badly formed data is rejected with a precise message, instead of causing an error further down the line, sometimes days later.
For your business, the API becomes a single entry point to your data or calculations: the website, a mobile app, an internal tool or a partner all use it in the same way, without the logic being duplicated in each. The service stays small, with little code to review during an upgrade or a security audit, which keeps maintenance time down. And you can evolve the API at your own pace, without forcing every connected system to update on the same day.
Our reading of it
What we take away from FastAPI
FastAPI settles a very concrete problem: the boundary between your interface and your business code. Incoming data is validated before it reaches the logic, and the interactive API documentation is generated from the Python signatures, so it does not go stale. For your developers as much as for an outside provider, that readable contract shortens the conversation.
We mostly use it to expose processing already written in Python — reading documents, computing, calling a model — without rebuilding a full stack around it. Persistence sits on PostgreSQL most of the time, and the service ships in a Docker image so that the version running in production is the one the tests passed against. It is not a choice for a content site, where FastAPI brings nothing.
Decision guide
Should you choose FastAPI?
FastAPI or Django for a Python back end?
We choose FastAPI when the project is a pure API or a data-oriented service: it exposes typed endpoints, validates input automatically and generates interactive documentation. Django adds an admin interface and page templates, which help if your staff manage content on screen. For a service consumed by another application, a mobile tool or an AI process, FastAPI spares you from shipping and maintaining that unused layer.
FastAPI or Laravel: where is the dividing line?
Our rule, set out on our software development page: FastAPI for a pure API or a data-oriented project, Laravel as soon as there is a back-office to maintain. If the application needs a full admin interface with user management, permissions and forms, the lightweight advantage disappears, because you would be rebuilding what Laravel already provides. For exposing an AI model or calculations to other software, FastAPI remains the more direct route.
Can FastAPI keep up with an API written in Go or Node.js?
For the vast majority of SME APIs, yes. FastAPI handles requests asynchronously, so it waits on a database or external service without holding up other calls. Response times then depend far more on database queries and the services being called than on the language. Go is justified for very high volumes with heavy computation on each request; Node.js when the team already writes the entire product in JavaScript.
Client questions
What clients ask us about FastAPI
Is FastAPI mature enough for a production application?
FastAPI is younger than Django or Flask, but it is built on proven components: Starlette for the web layer and Pydantic for data validation. It is widely adopted, particularly for serving AI services. We back it with automated tests, an established application server and regular version tracking, so that the framework's relative youth does not turn into a weak point.
How do other systems or suppliers connect to our API?
FastAPI automatically generates an OpenAPI specification and an interactive documentation page, refreshed with every deployment. A third-party supplier finds the list of endpoints, expected formats and possible responses there, and can try calls directly. There is no separately written document drifting out of date. Access to that page can be restricted if the API is not meant to be public.
Who takes care of security and authentication on a FastAPI API?
FastAPI provides the mechanisms but does not switch them on for you. We set up token-based authentication, permission checks on every endpoint, rate limiting and strict validation, with Pydantic rejecting any data that does not match the expected format. Secrets are kept out of the code, and dependencies are scanned regularly for published vulnerabilities.
Can a FastAPI service be added alongside an existing application without replacing it?
Yes, and it is often the safest way to introduce it. The FastAPI service sits next to your current application and takes on one specific function, such as a calculation or document analysis. It reads the same database or receives calls from the existing application. Your users keep their familiar screens, and the new function can be switched off without touching anything else.
How is a FastAPI API monitored in production?
We expose a health-check endpoint polled by the monitoring system, which also confirms access to the database and to the services the API depends on. Every request is logged with its duration and a correlation ID, without personal data. Errors are reported to a tool such as Sentry, and an alert fires when the error rate or response time crosses a threshold agreed with you.
Going further
FastAPI sits within our Software development
A technology does not make a project. The method around it decides the outcome: scoping, prototypes, short cycles, and a clean way out.
Neighbouring technologies
A FastAPI project in mind?
The first conversation is free. We will tell you plainly whether this technology is the right one, and if not, which is.
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