Wenn Sie sich die Benchmarks ansehen, werden häufig mehrere Tools mit unterschiedlichen Eigenschaften als gleichwertig verglichen.

Konkret geht es darum, Uvicorn, Starlette und FastAPI miteinander zu vergleichen (neben vielen anderen Tools).

Je einfacher das Problem, welches durch das Tool gelöst wird, desto besser ist die Performanz. Und die meisten Benchmarks testen nicht die zusätzlichen Funktionen, welche das Tool bietet.

Die Hierarchie ist wie folgt:

When you check the benchmarks, it is common to see several tools of different types compared as equivalent.

Specifically, to see Uvicorn, Starlette and FastAPI compared together (among many other tools).

The simpler the problem solved by the tool, the better performance it will get. And most of the benchmarks don't test the additional features provided by the tool.

The hierarchy is like:

* **Uvicorn**: an ASGI server

## Intro

**FastAPI** wouldn't exist if not for the previous work of others.

There have been many tools created before that have helped inspire its creation.

I have been avoiding the creation of a new framework for several years. First I tried to solve all the features covered by **FastAPI** using many different frameworks, plug-ins, and tools.

And there has to be something in charge of **renewing the HTTPS certificates**, it could be the same component or it could be something different.

### Example Tools for HTTPS

Some of the tools you could use as a TLS Termination Proxy are:

* Traefik
    * Automatically handles certificates renewals ✨
* Caddy
    * Automatically handles certificates renewals ✨
* Nginx

## **FastAPI** Tools

FastAPI stellt für jedes dieser Sicherheitsschemas im Modul `fastapi.security` verschiedene Tools bereit, die die Verwendung dieser Sicherheitsmechanismen vereinfachen.

In den nächsten Kapiteln erfahren Sie, wie Sie mit diesen von **FastAPI** bereitgestellten Tools Sicherheit zu Ihrer API hinzufügen.

Aber in den meisten Fällen, wenn wir so etwas machen, wollen wir nur, dass das Modell einige der Daten **filtert/entfernt**, so wie in diesem Beispiel.

But in most of the cases where we need to do something like this, we want the model just to **filter/remove** some of the data as in this example.

And in those cases, we can use classes and inheritance to take advantage of function **type annotations** to get better support in the editor and tools, and still get the FastAPI **data filtering**.

## Deployment Strategies

There are several ways to do it depending on your specific use case and the tools that you use.

You could **deploy a server** yourself using a combination of tools, you could use a **cloud service** that does part of the work for you, or other possible options.

Because of this, **FastAPI** itself provides an alternative API documentation (using ReDoc), which you can access at <a href="http://127.0.0.1:8000/redoc" class="external-link" target="_blank">http://127.0.0.1:8000/redoc</a>:

<img src="/img/tutorial/path-params/image02.png">

The same way, there are many compatible tools. Including code generation tools for many languages.

## Pydantic

**FastAPI** doesn't make any compromise with any database, data model or tool.

It gives you all the flexibility to choose the ones that fit your project the best.

And you can use directly many well maintained and widely used packages like `passlib` and `python-jose`, because **FastAPI** doesn't require any complex mechanisms to integrate external packages.