All use casesUse Case
FastAPI AI Kit as a general-purpose AI backend boilerplate.
If you're evaluating boilerplates for an AI product broadly — not tied to one specific application — FastAPI AI Kit's core layer (auth, LLM abstraction, RAG, billing, background jobs) is the general-purpose starting point most AI backends need, regardless of the specific product built on top.
FastAPIOpenAI / Anthropicpgvector / QdrantPostgreSQLRedisStripe
The usual pain points
- ✕Rebuilding the same auth/billing/rate-limiting layer for every new AI product idea
- ✕Choosing and wiring an LLM abstraction before writing any product logic
- ✕Standing up async infrastructure (Celery, Redis) before you can process anything in the background
- ✕Deciding on a vector store and RAG pattern before you've validated the product idea
How the kit solves them
- One backend covers auth, billing, and rate limiting regardless of the AI product built on top
- Unified LLM layer means switching providers later doesn't touch business logic
- RAG pipeline is available from day one if the product needs it, unused if it doesn't
- Background job infrastructure is already wired for whatever async workload comes up
Example implementation
# The same core stack, regardless of what you build on top
@router.post("/v1/{your_feature}")
@require_api_key(tier=["basic", "pro"])
@rate_limit(per_minute=60)
async def your_endpoint(body: YourRequest, key: APIKey = Depends(get_api_key)):
response = await llm.chat(messages=build_prompt(body), track_tokens=True)
await meter.record(key.id, response.tokens)
return {"result": response.content}Ready to build your ai backend boilerplate?
FastAPI AI Kit ships with everything shown above, pre-configured and production-ready. Clone the repo and start building in minutes.
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