Hire expert FastAPI developers

Add a dedicated FastAPI engineer who designs Pydantic schemas, keeps the event loop unblocked, writes async tests with httpx, and ships model-serving endpoints your frontend and data teams can trust.

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Average rating: 4.8/5
Response time: 24h

Hire a FastAPI developer

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A cleaner way to hire FastAPI developers

Hire FastAPI developers from Krapton when you need Python APIs that are typed end to end, documented automatically, and responsive under concurrent load. FastAPI sits on Starlette and Pydantic: the type hints on a path operation become...

Async without hidden blocking
The most common FastAPI performance bug is a synchronous call, such as requests, boto3, or a sync database driver, inside an async def endpoint. It stalls every request on that worker. Our developers profile for this and choose async clients like httpx and asyncpg, or deliberately use def endpoints instead.
Disciplined Pydantic schema design
Request and response models are your public contract. Engineers separate input, output, and database models so internal fields never leak through response_model, apply field validators and computed fields with care, and keep schema changes backward compatible so generated TypeScript clients and mobile apps keep working between releases.
Tests that override real dependencies
FastAPI’s Depends() system doubles as its testing seam. We write pytest suites that swap database sessions, auth, and third-party clients through app.dependency_overrides, run async tests with httpx.AsyncClient and ASGITransport, and cover validation errors and permission failures as thoroughly as the happy path.
Model serving built for production
Loading a PyTorch or scikit-learn model per request is slow and memory-hungry. Engineers load models once in the lifespan handler, batch where it helps, move heavy inference to worker processes or a dedicated GPU server, and add timeouts, health checks, and input limits before real traffic arrives.

Why FastAPI?

What makes FastAPI the right choice for modern engineering teams.

Type-safe APIs
Pydantic models provide automatic request validation, serialisation, and OpenAPI docs.
Async support
Full asyncio support for concurrent request handling without blocking the event loop.
Dependency injection
Clean, testable code via FastAPI's powerful dependency injection system.
Auto documentation
Interactive Swagger UI and ReDoc generated automatically from your code.
Background tasks
Run async tasks after returning a response for webhooks and notifications.
WebSocket support
Built-in WebSocket endpoints for real-time bidirectional communication.

What you can build with FastAPI

ML inference APIs
Endpoints that wrap PyTorch, scikit-learn, or ONNX Runtime models with Pydantic-validated inputs, explicit model versions, request timeouts, and structured prediction logs that let data scientists compare live traffic against the training data.
LLM and RAG backends
Streaming chat endpoints built on StreamingResponse or Server-Sent Events, retrieval against pgvector or a dedicated vector database, token accounting per user, and provider failover between hosted APIs and self-hosted open models.
Typed public APIs
Versioned REST APIs with an OpenAPI schema clean enough to generate TypeScript and Kotlin clients, consistent error envelopes, cursor pagination, idempotency keys on writes, and OAuth2 scopes enforced through dependencies.
Event-driven Python microservices
Small services that consume and publish over Kafka, RabbitMQ, or Redis Streams with async clients such as aiokafka, expose health and readiness endpoints Kubernetes probes can trust, and shut down gracefully through the lifespan handler.
Realtime WebSocket services
Live dashboards, notifications, and collaborative features on FastAPI WebSocket routes, with Redis pub/sub fanning messages out across Uvicorn workers so a horizontally scaled deployment still delivers every event to every subscriber.
Governed data access APIs
Services in front of a warehouse or feature store that give product teams controlled access to metrics, with query limits, Redis caching for hot aggregates, and audit logs showing who requested which dataset.

Async correctness and model serving in FastAPI

FastAPI’s performance depends on the event loop never being blocked, and that assumption fails quietly. An async def route that calls a synchronous database driver, the requests library, or a CPU-heavy pandas transform holds the loop for the full duration, so every other request on that worker waits behind it. Throughput looks fine in development and collapses under real concurrency. Our engineers treat this as a decision per endpoint: genuinely asynchronous I/O uses async def with httpx, asyncpg, or redis-py’s asyncio client; code that must stay synchronous goes in plain def routes, which FastAPI runs in a threadpool; CPU-bound work moves to a process pool or a queue worker. They confirm the result with load tests and asyncio debug mode rather than intuition, and they size database pools so adding workers does not exhaust Postgres connections.

Model serving adds memory and latency concerns that ordinary CRUD APIs never meet. Loading weights inside a route handler reloads them on every call, and each Uvicorn worker process holds its own copy in memory, so naive horizontal scaling multiplies RAM use. The pattern Krapton follows is to load models once per process in the lifespan context manager, expose them through a dependency, and set worker counts from available memory rather than CPU cores. For GPU inference or large language models, FastAPI usually becomes the thin, authenticated gateway in front of a dedicated server such as NVIDIA Triton or vLLM, which handle batching and GPU scheduling far better than a Python web worker. Inputs are validated against Pydantic schemas that mirror the training features, and every prediction is logged with its model version so drift can be measured later.

FastAPI in action

from fastapi import FastAPI, Depends, HTTPException, BackgroundTasks
from pydantic import BaseModel, Field
from typing import Annotated
import asyncio

app = FastAPI(title="Inference API", version="1.0.0")

class PredictRequest(BaseModel):
    text: str = Field(..., min_length=1, max_length=2000)
    model: str = Field(default="gpt-4o", pattern="^gpt-|^claude-")

class PredictResponse(BaseModel):
    prediction: str
    confidence: float
    latency_ms: int

async def get_current_user(token: str) -> dict:
    # JWT validation logic
    return {"user_id": "u-123"}

@app.post("/predict", response_model=PredictResponse)
async def predict(req: PredictRequest, user: Annotated[dict, Depends(get_current_user)],
                  background_tasks: BackgroundTasks):
    result = await run_inference(req.text, req.model)
    background_tasks.add_task(log_prediction, user["user_id"], req.text, result)
    return result

What our FastAPI developers know

Every Krapton developer is vetted with real production experience in FastAPI across multiple industry domains.

Pydantic modellingDesigning request/response models with validators, aliases, and nested schemas.
Async database accessUsing SQLAlchemy async, Tortoise ORM, or Prisma with asyncpg.
AuthenticationJWT-based auth with OAuth2PasswordBearer and dependency injection.
Background jobsCelery, ARQ, or FastAPI background tasks for async workloads.
ML servingServing PyTorch and scikit-learn models as REST APIs with FastAPI.
TestingWriting async tests with httpx.AsyncClient and pytest-asyncio.

What our FastAPI developers go deep on

Dependency injection and app structure
Routers split by domain, dependencies declared with Annotated types for sessions, auth, and settings, pydantic-settings for configuration, and business logic kept outside path operations so it can be tested directly.
Async SQLAlchemy and alembic
An AsyncSession per request through a yield dependency, pool sizes matched to worker counts, selectinload instead of lazy loading, which fails in async code, and Alembic migrations reviewed like any other change.
OAuth2, JWT, and scopes
OAuth2PasswordBearer or an external identity provider such as Auth0 or Amazon Cognito, JWT verification with PyJWT, Security() scopes for fine-grained permissions, and refresh-token rotation handled on the server.
Background work and queues
BackgroundTasks only for short side effects you can afford to lose, such as a notification email; Celery, ARQ, or Dramatiq on Redis or RabbitMQ for anything that must retry or survive a deploy.
Observability for FastAPI services
OpenTelemetry traces across HTTP, database, and queue calls, Prometheus metrics per route, structured JSON logs carrying request IDs, and Sentry for exceptions, so a slow endpoint can be traced to the exact query.
Deployment with uvicorn and containers
Uvicorn workers behind a reverse proxy or ingress, slim Docker images with dependencies locked through uv or Poetry, readiness probes that check downstream services, and graceful shutdown that drains in-flight requests.

Common FastAPI projects we deliver

Moving a Flask service to FastAPI
Flask apps that grew without validation or documentation are good candidates. We migrate route by route behind the same URLs, introduce Pydantic models at the edges first, keep the existing database layer, and switch to async drivers only where profiling shows I/O wait.
Putting a data science model into production
A notebook model becomes a service with pinned dependencies, a container image, input schemas that reject malformed features, latency and error metrics in Prometheus, and a shadow-mode rollout that compares new predictions with the current model before traffic switches.
Fixing an API that stalls under load
When tail latency climbs with concurrency, the cause is usually blocking I/O in async routes, an undersized database pool, or too few workers. Engineers reproduce it with Locust or k6, trace it with OpenTelemetry, and remove the bottleneck instead of adding servers.
Adding streaming AI features to products
Teams that want chat, summarization, or document Q&A get a FastAPI layer that streams tokens, enforces per-tenant rate limits, stores conversation history in Postgres, and keeps model provider API keys on the server instead of in the browser.

FastAPI solutions by industry

Healthtech
Clinical and wellness APIs where Pydantic validation rejects malformed records at the boundary, audit logging tracks every access, and inference services run inside the client’s own cloud account.
Fintech and lending
Credit-scoring and fraud models served behind strict input schemas, a decision log for every prediction, and idempotent payment endpoints that survive retries from flaky mobile connections.
AI products
Startups building on large language models need streaming endpoints, usage metering, and evaluation hooks, and FastAPI is usually the glue between model providers and the product.
Logistics and IoT
High-volume ingestion endpoints for telemetry and tracking events, validated asynchronously and handed to queues so bursts of device traffic never block the API for other callers.

FastAPI vs Django REST Framework

Both are mature Python choices, and the honest answer depends on what else the product needs. Django REST Framework inherits Django’s admin, ORM, auth, and migrations, which saves real effort on CRUD-heavy back offices. FastAPI is leaner and async-first, with validation and OpenAPI docs generated from type hints, which suits ML serving and I/O-bound services. Plenty of teams run both, each where it fits.

FastAPI vs Django REST Framework
Dimension	FastAPI	Django REST Framework
Validation	Pydantic models derived from type hints	Serializer classes defined alongside Django models
API docs	OpenAPI and Swagger UI generated automatically	Added through drf-spectacular or similar packages
Concurrency	Async-first on ASGI; sync routes use threadpool	Synchronous views; async needs third-party packages
Included pieces	Bring your own ORM, admin, and auth	Admin, ORM, auth, and migrations built in
Data layer	SQLAlchemy or SQLModel, migrations with Alembic	Django ORM with its built-in migration framework
Best fit	ML serving, microservices, high-concurrency I/O APIs	Admin-heavy products with complex permission rules

How to hire FastAPI developers in 5 steps

A transparent, low-risk process — from first call to onboarded engineer in about two business days.

01Day 0
Share your brief
Tell us the stack, scope, seniority and the timezone overlap you need on a free 30-minute discovery call. No obligation, no sales pitch.
02Within 24 hours
Review matched profiles
We shortlist 2–3 pre-vetted FastAPI engineers from our in-house bench, with CVs and directly relevant work — usually within one business day.
03Days 1–2
Interview & test
Interview each developer yourself, run a paid trial task, or do a live pairing session. You decide who joins — we never push a profile.
04Within 2 days
Onboard securely
Sign a free NDA, then plug the developer into your Jira, repository and Slack. Engagements are month-to-month with no lock-in.
05Ongoing
Scale or replace
Scale the team up or down month by month as the roadmap changes. If the fit is not right, you get a free replacement within the first 30 days.

Other backend technologies we work with at Krapton.

Three ways to hire FastAPI developers

Pick the engagement that matches how you actually work. No multi-year contracts — scale up or down month by month.

Dedicated developer
Most popular
Full-time FastAPI engineer who reports only to you. Best for ongoing products, long-term roadmaps and teams that need a core hire without the HR overhead.
- 40 hours / week
- Your Jira, your repo
- Month-to-month
Hourly / time & materials
Pay only for billable hours. Ideal for research spikes, code audits, or variable-load FastAPI work where scope is still being discovered.
- Weekly timesheets
- Slack-first comms
- No minimum commit
Fixed-price milestones
Scoped delivery with clear milestones and acceptance criteria. Best for well-defined FastAPI builds like an MVP, a migration or a specific module.
- Scope locked upfront
- Milestone acceptance
- Predictable budget

Compare ways to hire FastAPI developers

Pick the model that matches how you work. Switch between them as your FastAPI project evolves — no multi-year contracts.

Compare ways to hire FastAPI developers
Engagement	Dedicated developer (popular)	Hourly / time & materials	Fixed-price milestones
Best for	Ongoing products & long-term roadmaps	Exploratory or variable-load work	Well-scoped MVPs, modules & migrations
Billing	Monthly, full-time (~160 hrs)	Per billable hour	Per accepted milestone
Commitment	Month-to-month, no lock-in	No minimum commitment	Scope locked up front
Flexibility	Scale up or down monthly	Highest — pay for what you use	Lowest — defined deliverables
You manage	Day-to-day in your tools	Weekly priorities	Acceptance criteria only
Reporting	Daily standups	Weekly timesheets	Milestone demos

Hire FastAPI developers from anywhere

Our New Delhi team partners with founders and engineering leaders across eight key markets, with enough daily timezone overlap to feel in-house.

Hire FastAPI developers in India
Full working-day overlap
Krapton is an on-shore FastAPI studio in New Delhi. Hire FastAPI developers in India and get senior, full-time engineers at roughly 50–70% of US/UK rates — with English-fluent communication and IST working hours.
Hire FastAPI developers in USA
4–6 hours of daily overlap — your morning, our evening
Hire dedicated FastAPI developers for your USA team without the local price tag. US companies work with our vetted, full-time FastAPI engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.
Hire FastAPI developers in UK
Overlap across your whole morning (our afternoon)
Hire dedicated FastAPI developers for your UK team without the local price tag. UK companies work with our vetted, full-time FastAPI engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.
Hire FastAPI developers in Canada
4–6 hours of daily overlap with ET and PT
Hire dedicated FastAPI developers for your Canada team without the local price tag. Canada companies work with our vetted, full-time FastAPI engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.
Hire FastAPI developers in Australia
Strong overlap — our morning is your afternoon
Hire dedicated FastAPI developers for your Australia team without the local price tag. Australia companies work with our vetted, full-time FastAPI engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.
Hire FastAPI developers in UAE
Near-complete overlap — just 90 minutes apart
Hire dedicated FastAPI developers for your UAE team without the local price tag. UAE companies work with our vetted, full-time FastAPI engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.
Hire FastAPI developers in Germany
Overlap through your morning and our afternoon (DACH)
Hire dedicated FastAPI developers for your Germany team without the local price tag. Germany companies work with our vetted, full-time FastAPI engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.
Hire FastAPI developers in Singapore
Near-complete overlap — only 2.5 hours apart
Hire dedicated FastAPI developers for your Singapore team without the local price tag. Singapore companies work with our vetted, full-time FastAPI engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

FastAPI developer rates

Hire dedicated FastAPI developers from Krapton's team in India. Rates below are blended hourly bands; dedicated developers are billed monthly with no lock-in.

Junior (1–3 yrs)
Feature work under senior review
$22–$28/hr
Mid-level (3–6 yrs)
Owns modules end-to-end
$28–$36/hr
Senior / lead (6+ yrs)
Architecture, reviews, mentoring
$36–$45/hr

Why teams hire FastAPI developers from Krapton

Projects delivered: 50+
In-house engineers: 60+
Average client rating: 4.8/5
On-time delivery: 98%

Vetted, full-time engineers
Every FastAPI developer is a full-time, in-house Krapton engineer — never a freelancer or subcontractor.
Ready in 24 hours
Shortlisted FastAPI CVs within a business day and an onboarded developer within two.
Clear hourly rates
Published hourly bands by seniority, billed monthly, with no lock-in.
Your IP, your repos
Full IP assignment, free NDA on request, and all code in your accounts from day one.
30-day replacement
Not the right fit? Free replacement within five business days in the first 30 days.
Real timezone overlap
4–6 hours of daily overlap with US, UK, EU, UAE and AU teams for live collaboration.

Most FastAPI engagements also benefit from these Krapton services. Browse full offerings on the services page.

Hire FastAPI developer

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Hiring FastAPI developers — answered

Practical answers to the questions CTOs and founders ask us most often before they hire.

How quickly can I hire a FastAPI developer from Krapton?

Within 24 hours of the initial call. We keep a bench of pre-vetted FastAPI engineers, so once you confirm the brief we line up two or three shortlisted CVs within a business day and can onboard the selected developer within two.

How much does it cost to hire a dedicated FastAPI developer from India?

Blended hourly rates for senior FastAPI engineers typically fall in the $22–$45 USD range depending on seniority and engagement length. Dedicated developers are billed monthly with no lock-in.

Are your FastAPI developers full-time employees or freelancers?

Every FastAPI developer you hire through Krapton is a full-time, in-house engineer — we never subcontract to freelancers or agencies. That means consistent code quality, signed NDAs, and a single point of accountability for your project.

Who owns the code and IP my FastAPI developers write?

You do — 100%. IP assignment and confidentiality are covered by the contract and a free NDA on request. All code lives in your repositories, on your infrastructure, under your accounts from day one.

What should we test for when we hire FastAPI developers?

Test async judgment first. A senior candidate can explain why a blocking call inside async def hurts every request on the worker, when a plain def route is the better choice, how dependency_overrides makes code testable, and how to separate Pydantic input, output, and database models. They should also know SQLAlchemy’s async session rules and why BackgroundTasks is no substitute for Celery or ARQ.

Is FastAPI or Django REST Framework the better choice for our API?

It depends on what surrounds the API. Choose FastAPI for ML inference, streaming AI endpoints, and I/O-heavy microservices where async concurrency and automatic OpenAPI docs matter most. Choose Django REST Framework when you also need an admin panel, mature permissions, and an ORM with migrations out of the box. Mixed estates are common and reasonable: Django for the back office, FastAPI for model serving.

How do you migrate an existing Flask or Django API to FastAPI?

Incrementally, behind the same public URLs. Engineers put a reverse proxy in front, move endpoints one domain at a time, write Pydantic models that reproduce the current response shapes exactly, and run contract tests against both versions before switching traffic. The existing database and ORM usually stay in place at first; async drivers come later, and only where profiling shows the service is waiting on I/O.

How do you secure and test a FastAPI application?

Security is enforced through dependencies, not scattered if-statements. Every protected router declares an auth dependency, scopes are checked with Security(), CORS origins are listed explicitly, and request size limits sit at the proxy. Tests use pytest with httpx.AsyncClient, override auth and database dependencies, and include negative cases such as expired tokens, the wrong tenant, and malformed payloads. pip-audit runs in CI beside the test suite.

What changes when upgrading to Pydantic v2 or a newer FastAPI release?

Pydantic v2 is the breaking upgrade older codebases most often still face. Validators move to field_validator and model_validator, class Config becomes model_config, .dict() becomes model_dump(), and some coercion rules are stricter, so tests catch behavior changes that type checkers miss. FastAPI ships frequent releases, so engineers pin versions, read release notes, and upgrade in small steps while diffing the generated OpenAPI schema.

Keep exploring

More ways to plan your FastAPI build and team with Krapton.

Ready to build with FastAPI?

Get a free 30-minute consultation with our FastAPI team. Clear roadmap, transparent pricing, no obligation.

Hire a developer Schedule a call

Free NDA on request
Response within 24 hours
Certified FastAPI developers
Flexible engagement models
US, UK, UAE & India clients served

Hire a FastAPI developer

A cleaner way to hire FastAPI developers

Async without hidden blocking

Disciplined Pydantic schema design

Tests that override real dependencies

Model serving built for production

Why FastAPI?

Type-safe APIs

Async support

Dependency injection

Auto documentation

Background tasks

WebSocket support

What you can build with FastAPI

ML inference APIs

LLM and RAG backends

Typed public APIs

Event-driven Python microservices

Realtime WebSocket services

Governed data access APIs

Async correctness and model serving in FastAPI

FastAPI in action

What our FastAPI developers know

What our FastAPI developers go deep on

Common FastAPI projects we deliver

Moving a Flask service to FastAPI

Putting a data science model into production

Fixing an API that stalls under load

Adding streaming AI features to products

FastAPI solutions by industry

Healthtech

Fintech and lending

AI products

Logistics and IoT

FastAPI vs Django REST Framework

How to hire FastAPI developers in 5 steps

Share your brief

Review matched profiles

Interview & test

Onboard securely

Scale or replace

More backend technologies

Three ways to hire FastAPI developers

Dedicated developer

Hourly / time & materials

Fixed-price milestones

Compare ways to hire FastAPI developers

Hire FastAPI developers from anywhere

Hire FastAPI developers in India

Hire FastAPI developers in USA

Hire FastAPI developers in UK

Hire FastAPI developers in Canada

Hire FastAPI developers in Australia

Hire FastAPI developers in UAE

Hire FastAPI developers in Germany

Hire FastAPI developers in Singapore

FastAPI developer rates

Why teams hire FastAPI developers from Krapton

Vetted, full-time engineers

Ready in 24 hours

Clear hourly rates

Your IP, your repos

30-day replacement

Real timezone overlap

Services that pair well with FastAPI

API development

Custom software services

Application development