Python API Specialists

FastAPI Experts —
High-Performance APIs for AI and Fintech

FastAPI is our primary framework for Python-based APIs — particularly when serving AI models, orchestrating LLM chains, or building high-throughput microservices. Its async-first architecture, automatic OpenAPI documentation, and native Pydantic validation make it the most productive and performant Python API framework available.

Build an API with FastAPI → AI Automation Service

Async

Native async/await support

Auto

OpenAPI / Swagger docs generated

Pydantic

Type-safe request validation

Near-Go

Performance benchmarks

Why FastAPI for AI and Fintech APIs

When teams need to expose an LLM pipeline, a fraud detection model, or a document processing endpoint as an authenticated REST API, FastAPI is the fastest path from prototype to production. Its async support means a single FastAPI instance can handle hundreds of concurrent AI inference requests without blocking — critical when LLM calls can take 3–10 seconds each.

We containerize FastAPI services and deploy them to Cloud Run — giving you auto-scaling from zero instances (zero idle cost) to hundreds in seconds when traffic spikes. The automatic OpenAPI spec generation means your frontend team and integration partners always have accurate, up-to-date API documentation without a separate documentation effort.

✓ Streaming responses with Server-Sent Events for real-time LLM output

✓ Background tasks for async processing without blocking response

✓ Dependency injection for clean, testable auth and database patterns

✓ Middleware for JWT validation, rate limiting, and request logging

✓ WebSocket support for bi-directional agent communication

✓ SQLAlchemy async ORM integration for high-throughput database access

AI Model Serving

Wrap LangChain chains, CrewAI agents, or Vertex AI model endpoints in a production FastAPI service with auth, rate limiting, streaming, and usage logging. Your AI features become a stable, versioned API.

Fintech Microservices

Build transaction processing, KYC verification, and scoring microservices as independent FastAPI apps — each with its own Pydantic schema, unit tests, and Cloud Run deployment.

Webhook Receivers

Handle high-volume webhooks from payment processors, banks, and third parties with async FastAPI receivers that validate signatures, queue events to Pub/Sub, and return fast 200 responses to prevent retries.

Internal Platform APIs

Replace shared spaghetti scripts with well-documented FastAPI services that your team can call consistently. Automatic OpenAPI docs mean less time answering 'what does this endpoint do'.

FastAPI FAQ

FastAPI vs Django vs Flask — when do you choose FastAPI?+

FastAPI is our first choice for API-only services, especially when performance, async support, or AI integration matters. Django is better when you need a full monolith with admin panels, ORM migrations, and batteries-included features. Flask is a reasonable choice for very small APIs but lacks FastAPI's type safety and performance. For new API microservices, FastAPI is almost always the right choice.

Can FastAPI handle streaming LLM responses?+

Yes — and it's one of FastAPI's strengths. We implement streaming responses using `StreamingResponse` with async generators that yield tokens from OpenAI, Anthropic, or Vertex AI as they arrive. Your frontend receives a text/event-stream response and renders tokens in real time, creating the familiar 'typewriter' LLM experience. We've built this pattern for both chat interfaces and document generation features.

How do you handle authentication in FastAPI?+

We implement JWT authentication using FastAPI's dependency injection system. The `Depends()` mechanism lets you declare auth requirements per endpoint, which shows up correctly in the OpenAPI docs. For service-to-service calls within GCP, we use Cloud Run's identity tokens (OIDC). For OAuth2 flows (user-facing APIs), we implement PKCE-compliant flows with token refresh.

How do you deploy and scale FastAPI services?+

Our standard deployment is: Dockerfile → Cloud Build CI/CD → Cloud Run. Cloud Run scales from zero to N instances automatically based on request concurrency. We configure minimum instances for low-latency requirements (avoiding cold starts) and maximum instances for cost control. For very high write-throughput, we use Cloud Run Jobs for batch processing alongside the main API.

Can you take over an existing FastAPI codebase?+

Yes. Our typical engagement for inherited FastAPI services starts with a code audit: reviewing the dependency injection patterns, error handling, logging, Pydantic model completeness, and test coverage. We then prioritize technical debt items by risk — security gaps first (auth, validation bypass, SQL injection vectors), followed by reliability issues (missing error handling, no health checks), then maintainability improvements.

FastAPI Experts — High-Performance APIs for AI and Fintech