> ## Documentation Index
> Fetch the complete documentation index at: https://cerebrium.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> Start with Cerebrium when latency, burst traffic, and production AI constraints matter from day one.

Cerebrium is the infrastructure platform for **real-time and high-performance AI workloads**.

It is a strong fit when an app requires one or more of the following:

* **Low latency and low cold starts**
* **Bursty traffic** that should scale without wasting GPU capacity
* **Multi-region deployments** for global users or data residency
* **Realtime voice, video, and streaming workloads**
* **GPU-heavy production inference** that has to stay reliable under load

## Why teams choose Cerebrium

* Launch code in the cloud in seconds
* Run [CPUs](/hardware/cpu-and-memory) or [GPUs](/hardware/using-gpus) with automatic scaling
* Serve [REST APIs](/endpoints/inference-api), [streaming endpoints](/endpoints/streaming), [webSockets](/endpoints/websockets), or any [ASGI-compatible app](/container-images/custom-web-servers)
* Deploy across [multiple regions](/deployments/multi-region-deployment) for lower latency and residency requirements
* Tune [concurrency and batching](/scaling/batching-concurrency) for real production traffic
* Improve startup performance with [cold-start optimization strategies](/other-topics/faster-cold-starts)
* Store model weights and files with [persistent storage](/storage/managing-files)
* Pay only for the compute you use - [billed by the second](https://www.cerebrium.ai/pricing)

## Start by workload

Pick the closest path below to get started:

* **OpenAI-compatible LLM endpoint** → [Serve an OpenAI Compatible LLM with vLLM](/v4/examples/gpt-oss)
* **Voice AI / real-time speech** → [Deploy a Twilio Voice Agent with Pipecat](/v4/examples/twilio-voice-agent)
* **Image and video generation** → [Generate images using SDXL](/v4/examples/sdxl)
* **Python Apps** → [Deploy Gradio Chat Interface](/v4/examples/asgi-gradio-interface)

For the fastest first deployment, follow the quickstart below.

## Quickstart

Set up and deploy an app on Cerebrium in a few steps.

### 1. Install the CLI

<Tabs>
  <Tab title="Python (pip)">
    ```bash theme={null}
    pip install cerebrium
    ```
  </Tab>

  <Tab title="macOS (Homebrew)">
    ```bash theme={null}
    brew tap cerebriumai/tap
    brew install cerebrium
    ```
  </Tab>

  <Tab title="Linux">
    ```bash theme={null}
    # Ubuntu/Debian
    wget https://github.com/CerebriumAI/cerebrium/releases/latest/download/cerebrium_linux_amd64.deb
    sudo dpkg -i cerebrium_linux_amd64.deb

    # Or binary installation
    curl -L https://github.com/CerebriumAI/cerebrium/releases/latest/download/cerebrium_cli_linux_amd64.tar.gz | tar xz
    sudo mv cerebrium /usr/local/bin/
    ```
  </Tab>

  <Tab title="Windows">
    ```powershell theme={null}
    # PowerShell (Run as Administrator)
    Invoke-WebRequest -Uri "https://github.com/CerebriumAI/cerebrium/releases/latest/download/cerebrium_cli_windows_amd64.zip" -OutFile "cerebrium.zip"
    Expand-Archive -Path "cerebrium.zip" -DestinationPath "."
    # Add cerebrium.exe to PATH
    ```
  </Tab>
</Tabs>

### 2. Log in to the CLI

```bash theme={null}
cerebrium login
```

This opens your browser so you can authenticate your CLI session.

### 3. Initialize a project

```bash theme={null}
cerebrium init my-first-app
cd my-first-app
```

This creates a basic project with `main.py` for app code and `cerebrium.toml` for configuration.

```python theme={null}
def run(prompt: str):
    print(f"Running on Cerebrium: {prompt}")
    return {"my_result": prompt}
```

### 4. Run code remotely

Run the function in the cloud and pass it a prompt:

```bash theme={null}
cerebrium run main.py::run --prompt "Hello World!"
```

The prompt appears in the logs. This is useful for quick code iteration, testing snippets, or one-off scripts that need cloud CPU/GPU resources.

### 5. Deploy your app

```bash theme={null}
cerebrium deploy
```

This turns the function into a persistent [REST endpoint](/endpoints/inference-api) that accepts JSON input and can scale automatically.

Once deployed, the app is callable at a POST endpoint:

```text theme={null}
https://api.aws.us-east-1.cerebrium.ai/v4/{project-id}/{app-name}/{function-name}
```

### 6. What to do next

Useful next steps after a first deployment:

* [Define Container images](/container-images/defining-container-images)
* [Tune scaling and concurrency](/scaling/scaling-apps)
* [Store model weights in persistent storage](/storage/managing-files)
* [Deploy to multiple regions](/deployments/multi-region-deployment)

Join the Community [Discord](https://discord.gg/ATj6USmeE2) for support and updates.

## How Cerebrium works

Cerebrium uses containerization to ensure consistent environments and reliable scaling for apps. When code is deployed, Cerebrium packages it with all necessary dependencies into a container image. This image serves as a blueprint for creating instances that handle incoming requests. The system automatically manages scaling, creating new instances when traffic increases and removing them during quiet periods.

For a detailed explanation of how Cerebrium builds and manages container images, see the [Defining Container Images Guide](/container-images/defining-container-images).

<Info>
  Content-Aware Storage forms the foundation of Cerebrium's speed. This system
  intelligently manages container images by understanding their content
  structure. When launching new instances, it pulls only the specific files.
  This targeted approach significantly reduces cold start times and optimizes
  resource usage.
</Info>