Full-Stack Python & FastAPI Development

When Your Product Lives at the Intersection of API and Data Science

If your product involves data - analysis, machine learning, natural language processing, recommendations - Python is the obvious choice. The entire data science ecosystem is at your disposal: pandas, scikit-learn, PyTorch, transformers. Building your API in Python means your data pipeline and your API share the same runtime.

FastAPI adds what Python web frameworks traditionally lacked: speed and type safety. Async request handling, automatic validation, and OpenAPI documentation make it a production-grade API framework, not just a scripting language with routes bolted on.

Dependency Injection, Layered Services, and Typed API Clients

We build APIs with clear boundaries between HTTP handling, business logic, and data access. FastAPI’s dependency injection system makes this natural - services are injected into routes, repositories are injected into services. Testing any layer independently is straightforward.

For the frontend, we pair FastAPI with React, Vue, or a server-rendered solution depending on your product’s needs. The OpenAPI schema generated by FastAPI drives client SDK generation, so the frontend always uses typed API clients that match the actual backend implementation.

Async SQLAlchemy, Celery Workers, and ML Model Serving

Our FastAPI architecture uses SQLAlchemy 2.0 with async sessions for database access. Alembic handles schema migrations with proper rollback support. Background tasks use Celery or arq depending on the complexity - simple jobs stay in-process with FastAPI’s background tasks, complex workflows get proper job queues.

For products with ML components, we separate model training from model serving. Models are versioned artifacts loaded at startup or swapped at runtime. The API layer handles feature extraction, model invocation, and response formatting without coupling to specific model implementations.

Connection Pooling, Circuit Breakers, and Real-Time with Redis Pub/Sub

FastAPI’s native support for async/await pairs naturally with async database drivers like asyncpg for PostgreSQL. We configure connection pooling through SQLAlchemy’s async engine, tuning pool sizes based on your expected concurrency. Read replicas are straightforward to add - a secondary bind in SQLAlchemy routes read-heavy queries away from the primary without changing application code.

For external service integration, we build typed HTTP clients using httpx with retry logic, circuit breakers, and structured logging. Third-party API failures don’t cascade into your application. Each integration is wrapped in an adapter that can be swapped or mocked during testing. Rate limiting, both inbound and outbound, is handled through middleware rather than scattered throughout route handlers.

When your product requires real-time features, FastAPI’s WebSocket support handles persistent connections natively. We combine WebSocket endpoints with Redis pub/sub for broadcasting updates across multiple application instances, ensuring horizontal scaling doesn’t break real-time functionality.

Continuous Delivery from Docker Build to Production Metrics

We deploy using Docker with multi-stage builds that keep images small. Kubernetes or simple container hosting - we match the infrastructure to your scale and budget. CI/CD pipelines run tests, type checks, and linting on every push.

Feature development follows a steady cadence. We plan in two-week sprints, deploy continuously, and monitor production metrics. When your product needs new ML capabilities, data pipeline changes, or API expansions, we handle it within the same stack and the same team. No coordination overhead between separate backend, data, and ML teams.