Agentic Unconference 2026 - September 19, Chennai
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Entrans builds FastAPI services, AI backends and full stack web apps with Pydantic v2 models and OpenAPI contracts. Senior Python engineers also fix the most common bug in enterprise FastAPI code: blocking calls in async routes that stall every request on a worker.
FastAPI development services cover building and running APIs and web backends on FastAPI. FastAPI is the open-source Python framework created by Sebastián Ramírez. It uses Python type hints and Pydantic to check every request, and it writes OpenAPI docs for you. It runs on ASGI servers such as Uvicorn and supports async code natively.
✓ Other teams build against your API. Mobile, partner and front-end teams need a contract they can trust. FastAPI turns your Pydantic models into OpenAPI docs and schemas, so the docs never drift from the code.
✓ The backend spends most of its time waiting. Calls to LLMs, payment providers and other APIs are network waits, not CPU work. FastAPI's async routes let one worker keep many of those calls in flight instead of blocking on each.
✓ You're shipping AI features in Python. Model code, LLM SDKs and vector search libraries are all Python. FastAPI is the common way to put them behind a typed, streaming endpoint.
✓ Bad input has a real cost. In payments, claims or health data, a malformed request should fail at the edge. Pydantic v2 rejects it with a clear error before your business logic runs.
> You need an admin, user accounts and server-rendered pages. FastAPI ships no admin and no full auth system, so you would assemble them yourself. For that kind of product, Django developers get you there faster.
> Your team runs a stable Flask estate. If the services work, are mostly synchronous and the team knows Flask, a rewrite buys little. Our Flask developers can extend what you have instead.
> Your team writes JavaScript end to end. If the front end is React or Next.js and nobody writes Python, a FastAPI backend adds a second language to hire for. Our Node.js development team keeps it to one.
We start from the API contract: Pydantic models for every request and response, versioned routes and generated OpenAPI docs. Dependency injection keeps auth, database sessions and tenant context out of route code, so endpoints stay short and testable.
Recent FastAPI releases dropped Pydantic v1, so services still on v1 are stuck on old FastAPI versions. We run that upgrade behind tests and move Flask or Django endpoints to FastAPI one route group at a time, as part of wider application modernization.
We run FastAPI on Uvicorn workers in slim containers, with health checks, graceful shutdown and config from environment variables. Deployments land on Amazon ECS, Azure Container Apps or Google Cloud Run, defined in Terraform and sized against real load tests.
We pair FastAPI with React or Next.js and generate a typed TypeScript client from the OpenAPI schema. A backend change then breaks the build, not production. Jinja templates or HTMX work too when a full single-page app is overkill.
Our DevOps and quality engineering setup tests every route on each pull request with pytest and HTTPX. Schema checks catch breaking API changes before they merge. After launch, we trace slow endpoints with OpenTelemetry and pin FastAPI to a minor version, upgrading on purpose.
Most AI features need a Python service that streams tokens, calls tools and checks permissions. We build those endpoints with streaming responses and background tasks. They join existing products as AI integration work, not a rewrite.
80%
Reduction in Manual Reconciliation Effort
Built a Python and Flask backend that uses LLMs on AWS Bedrock to read invoices and match them against purchase orders and goods receipt notes.
3X
Accelerated prior authorization processing by 3X
Moved a monolithic platform onto microservices, added OCR and LLMs to pull clean data from authorization documents, and built a rule engine for payer checks.
100+
Global Sales Channels reached through a single centralized API integration
Built one central API that standardizes inventory feeds, syncs ticket availability in real time and brings new sales channels on through a single feed.
Get a 20-minute technical read from a senior engineer. No pitch deck, no sales team.
Yes, FastAPI is production-ready, but its 0.x version number means breaking changes can land in minor releases. FastAPI's own docs recommend pinning a minor range, such as >=0.142.0,<0.143.0, and upgrading on purpose with tests. FastAPI's own site quotes engineers at Microsoft, Uber, Netflix and Cisco on using it in production. Current releases need Python 3.10 or newer and Pydantic v2.
FastAPI is an API framework first, but it is a solid base for modern Python web development when a separate front end renders the pages. It serves JSON to React, Next.js or mobile apps, and it can render Jinja templates for simple server-side pages. What it lacks is the rest of a web framework: sessions, forms and an admin. Teams add those with libraries, or choose Django when they need them on day one.
FastAPI ranks among the fastest Python frameworks in independent TechEmpower benchmarks. It can't beat Starlette and Uvicorn, the layers it runs on. In real apps, your own code matters more than the framework. A single blocking call inside an async route, such as a sync database driver, stalls the whole worker. Use async drivers like asyncpg, or declare the route with plain def so FastAPI runs it in a thread pool.
FastAPI gives you security building blocks, not a finished system. You get OAuth2 and API key helpers, auth checks as dependencies and strict Pydantic validation. Teams still add an identity provider for OIDC, rate limiting, CORS rules and secrets management. A common miss is leaving the interactive /docs page public in production, so turn it off or put it behind auth. HIPAA or SOC 2 compliance then depends on audit logs and access controls around the API.
Look for a FastAPI development company that can explain async correctly, not one that only lists FastAPI on its website. Ask how it keeps blocking calls out of async routes, how it pins and upgrades 0.x releases, and whether it has moved a client off Pydantic v1. Ask to see test coverage and an OpenAPI contract from a past project. Then interview the engineers who will actually do the work.
Tell us the shape of the problem. A senior engineer reads it and replies. You won't get a templated capability deck.
