* **FastAPI**: (uses Starlette) an API microframework with several additional features for building APIs, with data validation, etc.

* **Uvicorn**:
    * Will have the best performance, as it doesn't have much extra code apart from the server itself.

```

</div>

as Uvicorn by default will use the port `8000`, the documentation on port `8008` won't clash.

### Translations

Help with translations is VERY MUCH appreciated! And it can't be done without the help from the community. 🌎 🚀

Here are the steps to help with translations.

#### Tips and guidelines

Your server(s) is (are) a **resource**, you can consume or **utilize**, with your programs, the computation time on the CPUs, and the RAM memory available.

How much of the system resources do you want to be consuming/utilizing? It might be easy to think "not much", but in reality, you will probably want to consume **as much as possible without crashing**.

    ```Python
    from main import app
    ```

!!! warning
    Uvicorn and others support a `--reload` option that is useful during development.

    The `--reload` option consumes much more resources, is more unstable, etc.

    It helps a lot during **development**, but you **shouldn't** use it in **production**.

## Hypercorn with Trio

Many packages that simplify it a lot have to make many compromises with the data model, database, and available features. And some of these packages that simplify things too much actually have security flaws underneath.

---

**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.

FastAPI does several things internally with Pydantic to make sure that those same rules of class inheritance are not used for the returned data filtering, otherwise you could end up returning much more data than what you expected.

This way, you can get the best of both worlds: type annotations with **tooling support** and **data filtering**.

## See it in the docs

And it generates OpenAPI schemas.

That's how it works in Flask, Starlette, Responder, etc.

But then, we have again the problem of having a micro-syntax, inside of a Python string (a big YAML).

The editor can't help much with that. And if we modify parameters or Marshmallow schemas and forget to also modify that YAML docstring, the generated schema would be obsolete.

!!! info
    APISpec was created by the same Marshmallow developers.

That way I could find the best ways to reduce code duplication as much as possible, to have completion everywhere, type and error checks, etc.

All in a way that provided the best development experience for all the developers.

## Requirements

And we can add a list of `dependencies` that will be added to all the *path operations* in the router and will be executed/solved for each request made to them.

!!! tip
    Note that, much like [dependencies in *path operation decorators*](dependencies/dependencies-in-path-operation-decorators.md){.internal-link target=_blank}, no value will be passed to your *path operation function*.

Import the `Response` class (sub-class) you want to use and declare it in the *path operation decorator*.

For large responses, returning a `Response` directly is much faster than returning a dictionary.