Solution · Business outcomes
FastAPI microservices: turn the AI demo into a dependable service
Most AI products don't die from a bad model — they die from everything around it. FastAPI microservices with async workers, proper validation and real failure handling are how a notebook demo becomes something other systems can depend on.
What this gets you
The service stops timing out under real traffic
Slow work — inference, LLM calls, heavy writes — moves off the request thread to Celery workers. The API validates, enqueues and returns in milliseconds.
Failures degrade instead of cascading
Retries with backoff, idempotent tasks, and job state in PostgreSQL mean a crashed worker or a flaky third-party API loses no data and blocks no one.
The team can ship without fear
Pydantic contracts at every boundary, pytest coverage, Docker images and a GitHub Actions pipeline — changes are reviewable and rollbacks are boring.
Service boundaries that fit the product
Not everything needs to be a microservice. Where one deployable unit is honest, I'll say so; where a boundary is real — heavy GPU work vs. light API work — it gets one.
How it works
1. Map the request paths
What must be synchronous (validation, reads) and what can be asynchronous (inference, notifications, heavy writes). That split drives the architecture.
2. FastAPI at the edge
Typed request/response models, documented OpenAPI, dependency injection for auth and rate limiting — the API layer stays thin and strict.
3. Celery + Redis for the slow work
Late acks, idempotent tasks, exponential backoff, and recovery jobs for events missed during downtime — the patterns written up in my architecture guide.
4. Shipped with the boring parts done
Docker, CI, tests, deployment notes and a handover walkthrough. The code lives in your repository from day one.
Frequently asked questions
FastAPI or Django for my product?
FastAPI suits async, API-only services and model endpoints. Django is better when you need its admin, ORM and auth out of the box. I don't rewrite a working Django app just to switch.
Do I really need Celery and Redis?
Not always. For a few jobs a minute, FastAPI's BackgroundTasks or a scheduled job is often enough — and I'll say so. The queue earns its keep when work is slow, spiky, or must survive worker restarts.
Can you work with our existing codebase?
Yes. Whether it's a monolith needing a boundary or a service that needs hardening, work happens in your repository with small, reviewable milestones.
How do we track what the async jobs are doing?
Job state lives in PostgreSQL with an ID the client can poll or get notified about — the database is the source of truth, not Redis.
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Want this built for your business?
Book a free 15-minute call below — I'll tell you honestly whether this fits your situation, and what it would take.
How I work with clients