10 Performance Tips to Make Your FastAPI App Lightning Fast ⚡

FastAPI is fast by default — but these tips make it blazingly fast.

10 Performance Tips to Make Your FastAPI App Lightning Fast ⚡

FastAPI is already one of the fastest Python web frameworks out there — but fast doesn’t mean optimized. Once your app starts handling real traffic, performance bottlenecks can creep in where you least expect them.

Whether you’re building an MVP or deploying a high-load microservice, here are 10 actionable tips to supercharge your FastAPI app and unleash its full potential. Let’s dive in.

1. Use uvicorn with gunicorn (for production)

While uvicorn is great for development, running it solo in production isn't ideal.

Instead, pair it with gunicorn for multi-worker support:

gunicorn app.main:app -k uvicorn.workers.UvicornWorker --workers 4

This combo helps you leverage multiple CPU cores and improves throughput dramatically.

Pro Tip: Start with workers = (2 × CPU) + 1 as a rule of thumb.

2. Enable Async Everywhere

FastAPI shines with async I/O. If you’re calling external APIs, databases, or file systems, make sure you’re using async def and compatible libraries (like httpx or databases).

from fastapi import FastAPI 
import httpx 
 
app = FastAPI() 
 
@app.get("/external-data") 
async def get_data(): 
    async with httpx.AsyncClient() as client: 
        response = await client.get("https://api.example.com/data") 
        return response.json()

Blocking code in async routes kills performance. Avoid it like the plague.

3. Use a Connection Pool

Creating a new DB connection per request? That’s a recipe for latency.

Use connection pooling with libraries like SQLAlchemy or asyncpg:

from sqlalchemy.ext.asyncio import create_async_engine 
 
engine = create_async_engine( 
    "postgresql+asyncpg://user:pass@host/db", 
    pool_size=10, 
    max_overflow=20 
)

Pooling minimizes overhead and keeps your database happy.

4. Cache Aggressively (but Wisely)

Not every request needs a roundtrip to your database. Use in-memory caches (like functools.lru_cache or Redis) for expensive or static operations.

from functools import lru_cache 
 
@lru_cache() 
def get_static_data(): 
    # Fetch or compute something expensive 
    return {"message": "Hello, world!"}

For distributed environments, go with Redis and libraries like aiocache or fastapi-cache2.

5. Cut Down Middleware Overhead

Every middleware adds a cost. Use only what’s essential — and profile their impact using tools like Py-Spy or cProfile.

Watch out especially for:

• Logging middleware writing to disk
• CORS and GZip (enabled globally without filters)
• Custom auth logic in middleware

Audit and trim the fat regularly.

6. Validate Only What You Must

Pydantic is fast — but heavy validation adds up. If you don’t need strict type enforcement, skip BaseModel in internal routes or reduce nesting.

# Instead of full Pydantic validation 
@app.get("/health") 
async def health_check(): 
    return {"status": "ok"}

You can also set Config options like validate_assignment = False if you're doing post-parse assignments.

7. Serve Static Files with a Reverse Proxy

FastAPI can serve static files — but shouldn’t. Use NGINX or Cloudflare for static content like images, JS, and CSS.

Let FastAPI handle what it does best: dynamic routes and APIs.

Bonus: NGINX also handles gzip, TLS, caching, and HTTP/2 more efficiently.

8. Compress Responses (Smartly)

Enable GZip compression selectively using FastAPI’s GZipMiddleware:

from fastapi.middleware.gzip import GZipMiddleware 
 
app.add_middleware(GZipMiddleware, minimum_size=1000)

Avoid compressing small payloads — you might spend more CPU than you save in bandwidth.

9. Profile and Benchmark

You can’t optimize what you don’t measure.

Use these tools to profile your app:

• Py-Spy: CPU sampling profiler
• Locust or k6: Load testing
• Prometheus + Grafana: Monitoring

Identify slow routes, memory leaks, and unawaited I/O — and fix what matters.

10. Use HTTP/2 and Keep-Alive

If you’re using a reverse proxy like NGINX or a cloud platform (e.g., AWS ALB), enable HTTP/2 and keep-alive.

Why?

• HTTP/2 reduces latency with multiplexed streams.
• Keep-alive avoids costly TCP handshakes.

Faster connections = happier users.

Final Thoughts

FastAPI gives you raw speed out of the box, but true performance comes from careful tuning, smart design decisions, and ongoing profiling.

If you’re building APIs at scale, apply these tips, and your FastAPI app will not just be fast — it’ll be lightning fast ⚡.

Over to You

Got your own optimization trick? Found a sneaky bottleneck? Share it in the comments — the FastAPI community thrives on collaboration.

And if this helped you, consider clapping 👏 and following for more deep dives on performance, architecture, and Python backend engineering.