Why FastAPI is So Fast: Under the Hood of Pydantic & Starlette

FastAPI isn’t just fast in name — it’s built on two lightning-fast engines that make it one of Python’s top web frameworks.

Why FastAPI is So Fast: Under the Hood of Pydantic & Starlette

Discover how Pydantic powers blazing input validation and how Starlette delivers high-performance async under the hood — making FastAPI a true beast for modern APIs.

FastAPI has taken the Python web development world by storm. Known for its blazing speed, intuitive design, and automatic interactive docs, it’s often the first recommendation for building modern REST and async APIs.

But what actually makes FastAPI so fast?

To understand this, we need to dive under the hood and look at two of its secret weapons:

Pydantic — for data validation and serialization

Starlette — the high-performance ASGI framework powering its core

In this article, we’ll explore how these two libraries contribute to FastAPI’s performance, developer experience, and sheer elegance.

The Performance Promise of FastAPI

FastAPI boldly claims to be “one of the fastest Python frameworks available”. And it’s not just marketing fluff — independent benchmarks consistently place it near the top.

But FastAPI itself isn’t doing all the heavy lifting.

Instead, it delegates crucial responsibilities to two best-in-class libraries:

• Starlette (for web requests, routing, middleware)
• Pydantic (for data parsing, validation, and serialization)

Let’s examine each.

Starlette: The Lightweight ASGI Powerhouse

At its core, FastAPI runs on Starlette, a lightning-fast ASGI framework built by Tom Christie, the creator of Django REST Framework.

What is ASGI?

ASGI (Asynchronous Server Gateway Interface) is the successor to WSGI. It allows for asynchronous request handling, meaning your app can:

• Handle thousands of concurrent requests
• Perform non-blocking I/O (like DB calls or HTTP requests)
• Leverage async/await syntax fully

WSGI frameworks like Flask and Django operate synchronously. ASGI frameworks like Starlette (and therefore FastAPI) scale better under load by allowing efficient multitasking.

Why Starlette is Fast

• Minimalist Design: Starlette is built with low overhead and modularity in mind.
• Async-First: It’s optimized for asyncio, making it ideal for real-time APIs and WebSockets.
• Middleware Efficiency: It features a high-performance middleware stack with minimal bottlenecks.
• Built-in Tools: Starlette provides static files, sessions, background tasks, and testing utilities — all tuned for speed.

And since FastAPI uses Starlette directly, every FastAPI app is, in essence, a Starlette app with extra features.

Pydantic: Lightning-Fast Data Validation

The other pillar of FastAPI’s speed and reliability is Pydantic — a data validation and settings management library based on Python type hints.

Why Pydantic is a Game-Changer

Traditionally, validating and parsing incoming JSON data required lots of boilerplate:

# The old way 
def validate_user(data): 
    if 'name' not in data or not isinstance(data['name'], str): 
        raise ValueError("Invalid name")

With Pydantic, it becomes elegant and automatic:

from pydantic import BaseModel 
 
class User(BaseModel): 
    name: str

Just like that, FastAPI:

Automatically parses incoming request bodies

Validates types and fields

Raises helpful errors if the data is malformed

But What Makes It Fast?

Cython + Rust: Pydantic V2 (the latest major version) uses Rust under the hood, improving parsing performance up to 50x in some cases.

Zero Runtime Cost on Type Hints: Pydantic inspects type annotations at startup and compiles efficient validators.

Caching: Pydantic caches validators to avoid redundant work on repeated requests.

And best of all? FastAPI auto-generates OpenAPI docs (Swagger UI) using these Pydantic models. So your API docs are always in sync with your code — without extra effort.

How They Work Together in FastAPI

Let’s bring it all together with an example.

from fastapi import FastAPI 
from pydantic import BaseModel 
 
app = FastAPI() 
 
class Item(BaseModel): 
    name: str 
    price: float 
 
@app.post("/items/") 
async def create_item(item: Item): 
    return {"name": item.name, "price": item.price}

Here’s what’s happening behind the scenes:

1. Starlette handles the incoming HTTP POST request.
2. Pydantic parses and validates the JSON body against the Item schema.
3. FastAPI routes the request to create_item, automatically injecting the validated Item instance.
4. FastAPI serializes the response using Pydantic again.
5. All of this happens with zero boilerplate and maximum performance.

Benchmarks Don’t Lie

According to the TechEmpower benchmarks, FastAPI (with Uvicorn and Starlette) performs nearly as fast as Node.js, Go, and even Rust web frameworks — while still being Python.

This is largely thanks to:

• Starlette’s async ASGI core
• Pydantic’s optimized data handling
• FastAPI’s ability to orchestrate the two with minimal overhead

Developer Productivity Meets Speed

While raw speed is great, FastAPI also offers:

• Automatic Swagger & ReDoc docs
• Validation errors with precision
• Async and sync support out of the box
• Dependency injection system
• Type-hint-based autocompletion

This mix of speed and DX (developer experience) is rare — and it’s what makes FastAPI a fan favorite.

Conclusion

FastAPI isn’t just fast because it’s well-marketed — it’s fast because it builds on the best tools available:

• Starlette for ultra-efficient async web handling
• Pydantic for type-safe, high-speed data parsing

If you’re building modern APIs in Python, you’re not just choosing a web framework — you’re choosing an ecosystem that values both speed and developer joy.

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Got questions or want a deep dive into Pydantic V2 or Starlette middleware? Let me know in the comments!