* **Uvicorn**:
    * Will have the best performance, as it doesn't have much extra code apart from the server itself.
    * You wouldn't write an application in Uvicorn directly. That would mean that your code would have to include more or less, at least, all the code provided by Starlette (or **FastAPI**). And if you did that, your final application would have the same overhead as having used a framework and minimizing your app code and bugs.

But still, FastAPI got quite some inspiration from Requests.

**Requests** is a library to *interact* with APIs (as a client), while **FastAPI** is a library to *build* APIs (as a server).

They are, more or less, at opposite ends, complementing each other.

Requests has a very simple and intuitive design, it's very easy to use, with sensible defaults. But at the same time, it's very powerful and customizable.

So we create a new `dict`, that contains the key-value pairs from `note.dict()` with:

```Python
{**note.dict()}
```

`**note.dict()` "unpacks" the key value pairs directly, so, `{**note.dict()}` would be, more or less, a copy of `note.dict()`.

And then, we extend that copy `dict`, adding another key-value pair: `"id": last_record_id`:

```Python
{**note.dict(), "id": last_record_id}
```

An interesting detail is that the percentage of the **CPU used** by each process can **vary** a lot over time, but the **memory (RAM)** normally stays more or less **stable**.

If you have an API that does a comparable amount of computations every time and you have a lot of clients, then the **CPU utilization** will probably *also be stable* (instead of constantly going up and down quickly).

Pydantic version 2 has the **core** re-written in **Rust** and includes a lot of improvements and features, for example:

* Improved **correctness** in corner cases.
* **Safer** types.
* Better **performance** and **less energy** consumption.
* Better **extensibility**.
* etc.

...all this while keeping the **same Python API**. In most of the cases, for simple models, you can simply upgrade the Pydantic version and get all the benefits. 🚀

When you run `fastapi dev`, it will run on development mode.

By default, it will have **auto-reload** enabled, so it will automatically reload the server when you make changes to your code. This is resource intensive and could be less stable than without it, you should only use it for development.

By default it will listen on the IP address `127.0.0.1`, which is the IP for your machine to communicate with itself alone (`localhost`).

## `fastapi run`

!!! note
    When defining a <a href="https://docs.pydantic.dev/latest/concepts/types/#unions" class="external-link" target="_blank">`Union`</a>, include the most specific type first, followed by the less specific type. In the example below, the more specific `PlaneItem` comes before `CarItem` in `Union[PlaneItem, CarItem]`.

=== "Python 3.10+"

    ```Python hl_lines="1  14-15  18-20  33"

* **Intuitive**: Great editor support. <abbr title="also known as auto-complete, autocompletion, IntelliSense">Completion</abbr> everywhere. Less time debugging.
* **Easy**: Designed to be easy to use and learn. Less time reading docs.
* **Short**: Minimize code duplication. Multiple features from each parameter declaration. Fewer bugs.

### `UJSONResponse`

An alternative JSON response using <a href="https://github.com/ultrajson/ultrajson" class="external-link" target="_blank">`ujson`</a>.

!!! warning
    `ujson` is less careful than Python's built-in implementation in how it handles some edge-cases.

```Python hl_lines="2  7"
{!../../../docs_src/custom_response/tutorial001.py!}
```

!!! tip

This idea of **asynchronous** code described above is also sometimes called **"concurrency"**. It is different from **"parallelism"**.

**Concurrency** and **parallelism** both relate to "different things happening more or less at the same time".

But the details between *concurrency* and *parallelism* are quite different.

To see the difference, imagine the following story about burgers:

### Concurrent Burgers