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⚡ FastAPI

Modern, fast Python APIs built for scale and maintainability

5+ Years Experience
25+ Projects Delivered
✓ Available for new projects
Used at:
Anaqua Flowrite Sparrow Intelligence The Virtulab Pipelinepharma

$ cat services.json

API Development from Scratch

Build production-ready FastAPI services with best practices baked in.

Deliverables:
  • RESTful API design and implementation
  • OpenAPI/Swagger documentation
  • Pydantic models and validation
  • Authentication (JWT, OAuth2, API keys)
  • Rate limiting and security headers

AI/ML API Services

Build FastAPI backends specifically designed for AI workloads.

Deliverables:
  • LLM integration endpoints
  • Streaming response support
  • Background task processing
  • Model serving infrastructure
  • Cost monitoring and optimization

Microservices Architecture

Design and implement scalable microservices using FastAPI.

Deliverables:
  • Service decomposition strategy
  • Inter-service communication
  • API gateway integration
  • Docker containerization
  • Kubernetes deployment configs

$ man fastapi

Why I Choose FastAPI for AI Projects

After building backends with Django, Flask, and Node.js, I’ve found FastAPI to be the ideal choice for AI/ML systems:

Performance: Async support means efficient handling of I/O-bound LLM calls Type Safety: Pydantic models catch errors before they hit production Documentation: Auto-generated OpenAPI docs reduce integration time Modern Python: Full support for async/await, type hints, and dataclasses

My FastAPI Production Template

Every FastAPI project I build includes:

  • Structured logging with correlation IDs
  • Health checks and readiness probes
  • Graceful shutdown handling
  • Database connection pooling
  • Redis caching layer
  • Comprehensive error handling
  • Request/response validation
  • Security middleware (CORS, rate limiting, headers)

$ cat README.md

Why FastAPI?

FastAPI has become my go-to framework for Python backends because it hits the sweet spot of performance, developer experience, and production readiness.

FeatureFastAPIFlaskDjango
Async SupportNativeExtensionLimited
Type SafetyBuilt-inManualPartial
Auto DocsOpenAPIManualDRF
PerformanceExcellentGoodGood
AI/ML FitPerfectOkayHeavy

My FastAPI Architecture

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# Production FastAPI Structure
from fastapi import FastAPI, Depends, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from contextlib import asynccontextmanager

@asynccontextmanager
async def lifespan(app: FastAPI):
    # Startup: Initialize connections
    await database.connect()
    await redis.initialize()
    yield
    # Shutdown: Clean up
    await database.disconnect()
    await redis.close()

app = FastAPI(
    title="AI Backend Service",
    version="1.0.0",
    lifespan=lifespan
)

# Structured API with dependency injection
@app.post("/api/v1/generate", response_model=GenerationResponse)
async def generate(
    request: GenerationRequest,
    user: User = Depends(get_current_user),
    llm: LLMService = Depends(get_llm_service),
    db: AsyncSession = Depends(get_db)
):
    # Validate, process, and respond
    result = await llm.generate(request.prompt, user.context)
    await db.log_usage(user.id, result.tokens_used)
    return GenerationResponse(content=result.text, citations=result.sources)

Integrations I Build

  • Databases: PostgreSQL (async), MongoDB, Redis
  • Message Queues: Celery, RabbitMQ, Redis Streams
  • AI/ML: LangChain, OpenAI, Anthropic, HuggingFace
  • Cloud: AWS Lambda, GCP Cloud Run, Docker, Kubernetes
  • Monitoring: Prometheus, Grafana, Sentry, OpenTelemetry

Experience:

Case Studies: Enterprise RAG System | LLM Orchestration

Related Services: Python, LangChain, REST APIs, OpenAI Integration

$ ls -la projects/

AI Backend for IP Management

@ Anaqua (RightHub)
Challenge:

Build a unified AI backend to power multiple LLM features across the platform.

Solution:

Designed FastAPI microservices architecture with standardized AI service templates, shared authentication, and centralized LLM cost tracking.

Result:

99.9% uptime, 50% faster search, became the AI backbone for the entire product.

Email Generation API

@ Flowrite
Challenge:

Handle 100K+ users with fast, reliable email generation while controlling costs.

Solution:

Built FastAPI service with streaming responses, intelligent model routing, and Redis caching for common patterns.

Result:

Sub-second response times, 40-50% cost reduction.

Real-time IoT Data API

@ Spiio
Challenge:

Ingest and serve data from 1,000+ soil sensors with 40,000+ hourly data points.

Solution:

FastAPI with async database operations, efficient batch processing, and WebSocket support for real-time updates.

Result:

Handled 10x traffic growth without infrastructure changes.

$ diff me competitors/

+ Built FastAPI systems handling millions of requests in production
+ Specialize in AI/ML backends—not just CRUD APIs
+ Full async expertise—proper handling of concurrent LLM calls
+ Security-first approach with OAuth2, JWT, and API key patterns
+ Can take projects from zero to deployed on AWS/GCP

Build Your FastAPI Backend

Within 24 hours

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