FastAPI's usage jumped five points in a single year. In the Stack Overflow 2025 survey, 14.8% of respondents said they use it, and the survey called the increase "one of the most significant shifts in the web framework space." It is now more widely used than Django among those respondents. That growth has a hiring side effect: a lot of developers have built a FastAPI app over a weekend, and far fewer have run one in production. The framework makes the first 200 lines easy, and the rest is on your engineer.
Key Takeaways
- FastAPI is small on purpose. Your developer has to supply structure, auth, background jobs, and database patterns that Django would give them.
- Screen for async understanding. Blocking calls inside async endpoints are the most common production mistake.
- Strong FastAPI developers are strong at data modeling with Pydantic and type hints, since the whole framework runs on them.
- Many FastAPI roles sit next to ML or AI services. Decide whether you need a backend engineer who knows ML, or an ML engineer who can ship an API.
What a FastAPI Developer Does
FastAPI shows up at startups in two places. The first is a plain product backend: a typed JSON API behind a React or mobile front end. The second is serving models and AI features, where a Python API sits between your product and a model. The framework's own site quotes engineers at Microsoft describing ML services integrated into Windows and Office, and a team at Uber using it to serve predictions (FastAPI). Those are individual engineers' testimonials, not company statements, but they show the pattern.
In either case, the developer designs Pydantic models for requests and responses, writes endpoints, wires dependencies for auth and database sessions, sets up SQLAlchemy or another data layer, handles migrations, runs background work, and deploys. FastAPI itself covers routing, validation, and generated OpenAPI docs. Everything else is a choice your developer makes, and those choices are what you're hiring for.
One more thing to know: FastAPI is still pre-1.0. The latest release is 0.141.1. It's stable in practice, but a senior developer pins versions and reads release notes.
What to Screen For
| Signal | Green flag | Red flag |
|---|---|---|
| Async | Knows which of their calls block and what to do about it | Marks every endpoint async by default and calls a sync database driver inside |
| Data modeling | Separate input, output, and database models, with clear validation | One giant model reused everywhere |
| Structure | Organizes a growing app into routers, services, and dependencies | Everything in main.py |
| Database | Comfortable with SQLAlchemy sessions, migrations, and connection pools | Hasn't thought about session lifetime |
| Testing | Uses dependency overrides and a real test database | Tests only by clicking in the docs page |
| Operations | Has configured workers, timeouts, and logging for production | Has only run the dev server |
The async row catches more candidates than any other. Ask them what happens to a FastAPI server when an async endpoint calls a slow synchronous library. A strong answer mentions the event loop being blocked and offers ways around it: declare the endpoint with plain def so FastAPI runs it in a threadpool, push the call to a thread with run_in_threadpool, switch to an async client, or move the work to a job queue.
A Practical Exercise
Give candidates a two-hour task with a real shape: an API with two related resources, token auth, pagination, and one endpoint that calls a slow external service. Ask them to write tests. Then review it together.
In the follow-up, ask:
- Where would this app block under load, and how would you find out?
- How would you run the slow external call without holding the request open?
- What would you change before putting this behind 50 requests per second?
You'll learn more from their answers than from the code itself. How to design a coding challenge has more on keeping exercises short and fair.
FastAPI or Django?
This decides who you hire, so decide it first. Django gives you an admin, an ORM, auth, and conventions out of the box, which is why a new Django developer can find their way around any Django app. FastAPI gives you speed, types, and freedom, which means every FastAPI codebase is organized a little differently.
For an API-first service, especially one close to ML, FastAPI is a good fit. For a data-heavy product with lots of internal tooling, Django usually gets you there faster. Our Django developer guide covers that side.
A Concrete Version
A 7-person AI startup ships a document-analysis feature. Their FastAPI service takes an upload, calls a language model, and stores the results. It works in demos. In production, with 30 customers uploading at once, response times climb to 40 seconds and requests start timing out.
They bring on a senior FastAPI developer. On day three he finds the cause: the endpoints are async, but the database driver and the PDF parser are both synchronous, so a handful of uploads blocks the whole server. He moves parsing and the model call into a background job queue, returns a job ID right away, switches to an async database driver, and adds a status endpoint the front end polls. Upload responses drop under 300 milliseconds and the timeouts stop. The fix took two weeks. Finding it took someone who had seen the pattern before.
The Honest Counterpoint
Not every Python API needs a FastAPI specialist. The framework is small, and a strong senior Python engineer who has built APIs in Flask or Django can learn it in a week or two. If your pool is thin, widen it to senior Python developers and test them on the async and data-modeling points above.
The reverse is also true. If your team keeps rebuilding things Django includes, such as admin screens, permissions, and user management, FastAPI may have been the wrong choice for that service, and a better hire won't fix that.
Frequently Asked Questions
Does a FastAPI developer need machine learning experience?
Only if the service serves models. For a model-serving API, look for someone who has handled model loading, batching, and long-running requests. Otherwise, hire a backend engineer.
What should they know besides FastAPI?
Pydantic, SQLAlchemy or a similar data layer, a migration tool, a job queue, Docker, and whatever cloud you deploy to.
Is FastAPI mature enough for a production startup?
Yes, many teams run it in production. Pin your versions and read the release notes when you upgrade, since it hasn't reached 1.0.
The Bottom Line
Hire FastAPI developers who understand async, model data carefully, and have run a service under real load. Ruzora sends a vetted shortlist of senior Python engineers within 72 hours. Hire FastAPI developers in LATAM, or see how to hire a backend developer for the broader role.
Roberto Espinoza is CEO of Ruzora, which helps US startups hire pre-vetted senior LATAM engineers, with a vetted shortlist in 72 hours. See available engineers.
