FastAPI AI Backend Stack

Persistent storage

Stores conversation history, user profiles, and AI output logs — the pgvector extension adds similarity search for RAG pipelines without a separate vector database.

Task queue

Celery broker for long-running inference jobs like document parsing and batch generation — keeps the API response time fast by pushing heavy work to background workers.

Container packaging

Pin exact Python and package versions in a container — AI dependencies are notoriously fragile and reproducible builds prevent the worked-in-dev-failed-in-prod incidents.

Deployment

Deploy containers close to users globally with persistent volumes for model weights — simpler than EKS for AI workloads that don't need full Kubernetes orchestration.

Error monitoring

Full Python async tracebacks for FastAPI — captures coroutine failures, LLM timeout errors, and Celery task exceptions with the full request context attached.