FastAPI development company
We design and ship FastAPI backends where request validation, documentation and client types come from the same Pydantic models: product APIs, microservices, webhook processors and inference endpoints for scikit-learn, PyTorch or LLM workloads, deployed with sensible concurrency settings.
- Senior engineers · IST + EST overlap
- NDA on day 1
- 24-hour reply
- Projects shipped
- 50+
- Code ownership
- 100%
- Client rating
- 4.8/5
- First reply
- 24h
FastAPI development company for typed Python backends
Krapton is a FastAPI development company that builds Python APIs for products, data platforms and machine learning teams. FastAPI sits on Starlette and Pydantic, so request bodies, query parameters and responses are declared once as typed models and the framework handles validation, serialization and the OpenAPI schema behind Swagger UI. That design rewards discipline and punishes shortcuts. The classic failure is a blocking call, such as the requests library, a synchronous database driver or a heavy model prediction, placed inside an async def endpoint, where it stalls the event loop and every other request with it. Our engineers choose deliberately between async def with asyncpg or httpx and plain def handlers that FastAPI runs in a threadpool, and they move CPU-heavy inference to worker processes. We structure larger services with routers and Annotated dependencies, manage schema changes through SQLAlchemy 2.0 and Alembic, and replace in-process BackgroundTasks with Celery, ARQ or Dramatiq whenever a job must survive a restart.
FastAPI development services
Product and SaaS APIs
REST backends for web and mobile apps with versioned routers, OAuth2 and JWT authentication through dependencies, role checks, pagination, and an OpenAPI schema your frontend generates typed clients from.
ML and LLM inference endpoints
Serving scikit-learn, XGBoost or PyTorch models loaded once in the lifespan handler, batched where useful, with LLM responses streamed to the browser through StreamingResponse or server-sent events.
Flask and Django migrations
Moving Flask or Django REST Framework endpoints to FastAPI route by route behind the same gateway, translating marshmallow or DRF serializers into Pydantic models and keeping response contracts unchanged.
Pydantic v2 upgrades
Upgrading older FastAPI services from Pydantic v1 to v2: validator to field_validator, Config classes to model_config, .dict() to model_dump(), with contract tests proving responses did not change.
Webhooks and integrations
Webhook receivers for Stripe, GitHub or Shopify that verify signatures, acknowledge quickly, and hand work to a durable queue, plus typed clients for the third-party APIs you depend on.
Performance and reliability fixes
Profiling slow endpoints with py-spy and OpenTelemetry traces, fixing event-loop blocking, N+1 queries and connection-pool exhaustion, and tuning Uvicorn worker counts for the CPU and memory you have.
Why teams choose Krapton for FastAPI
Senior engineers, fixed scope, and code you own — the things agencies promise and rarely deliver.
Contracts your frontend trusts
The OpenAPI schema is checked in CI, breaking changes flagged by oasdiff fail the build, and openapi-typescript or similar generators give your web and mobile teams typed clients that match production.
Tests through dependency overrides
Every route is tested with httpx and pytest, using app.dependency_overrides to swap databases, auth and external clients, so tests run fast and never call production services.
Security in the dependency chain
Authentication, tenant scoping and permission checks live in reusable dependencies, input limits are set on models, and CORS, rate limiting and secret handling are reviewed before launch.
Observable from the first deploy
Krapton wires structured logging, OpenTelemetry tracing and Prometheus metrics into each service, so latency spikes, slow queries and error bursts are visible before customers report them.
How a FastAPI project runs with Krapton
01
API contract first
We draft Pydantic models and route signatures with your frontend or partner teams, publish the OpenAPI document early, and agree error formats before writing business logic.
02
Build in vertical slices
Each feature ships end to end: route, dependency, service layer, Alembic migration and tests, merged behind review so a working API exists from the earliest sprint.
03
Load and security testing
Locust or k6 load tests find the concurrency limits, and we check auth bypasses, oversized payloads and injection paths before traffic arrives.
04
Containerized release
Services ship as slim Docker images running Uvicorn or Gunicorn with Uvicorn workers, with health checks, graceful shutdown and rollback configured on your platform.
The FastAPI stack we build with
FastAPI development: questions answered
Why hire a FastAPI development company instead of building in-house?
Hire one when you need production-grade Python APIs faster than you can recruit for them, or when an existing service is struggling. A specialist team brings settled patterns for project layout, async database access, auth dependencies, testing and deployment, so time goes to your domain logic. Krapton can also work beside your developers and leave those patterns documented in your codebase.
When is FastAPI a better choice than Django?
FastAPI is the better choice for API-only services, ML inference, high-concurrency I/O and teams that want typed request models without a large framework. Django wins when you need an admin panel, built-in auth, server-rendered pages and a mature ORM in one package. Many companies run both: Django for the back office and FastAPI for public or model-serving APIs. Django Ninja is a middle ground if you want FastAPI-style typing inside Django.
How do you scale a FastAPI application?
Scale it horizontally once each instance is efficient. We first remove event-loop blocking, size database connection pools so workers do not exhaust Postgres, and cache hot reads in Redis. Then we run Uvicorn workers sized to the available cores and add replicas behind a load balancer or Kubernetes HPA. CPU-heavy work moves to task queues or separate inference services so API latency stays predictable.
Is FastAPI stable enough for long-lived production systems?
Yes, provided you pin versions and upgrade on purpose. FastAPI moves quickly, and its releases occasionally change behavior in ways that affect validation or dependency handling. We pin FastAPI, Starlette and Pydantic together, read release notes before bumping, and rely on a contract test suite to catch changes in validation or serialization before they reach clients.
What determines the cost of FastAPI development services?
The number of endpoints matters less than what sits behind them. Cost is driven by data model complexity, integrations with payment, CRM or identity providers, migration from an existing Flask or Django codebase, ML model serving requirements, compliance needs such as audit logging, and the expected traffic profile. Seniority mix and how much of the work your own team takes on also shape it.
Ready to build? Tell us what you have in mind.
Send a short brief and a senior engineer will reply within one business day with next steps — no sales fluff.