Introduction

Cerebrium is a serverless infrastructure platform for building and scaling data and AI workloads.

• Launch your code in the cloud in seconds
• Define your own containers environments or bring your own Dockerfile
• Run CPUs or GPUs at scale—with support for thousands of concurrent containers
• Scale based on concurrency, Requests per second or CPU/Memory utilization,
• Serve WebSockets, REST APIs, or any ASGI-compatible app
• Store model weights, files, and more with distributed storage
• Pay only for the compute you use — billed by the second

Cerebrium abstracts the infrastructure complexity so you can focus on building AI products users love!

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Getting Started

Setting up and deploying an app on Cerebrium takes just a few steps:

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1. Install the CLI

pip install cerebrium
cerebrium login  # Redirects to your browser for authentication

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2. Initialize a Project

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. This is was the main.py file contains:

def run(prompt: str):
    print(f"Running on Cerebrium: {prompt}")

    return {"my_result": prompt}

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3. Run code remotely

We can then run this function in the cloud and pass it a prompt.

cerebrium run main.py::run --prompt "Hello World!"

Your should see logs that output the prompt you sent in - this is running in the cloud! Let us now turn this into a scalable REST endpoint.

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4. Deploy your app

Run the following command:

cerebrium deploy

This will turn the function into a callable endpoint that accepts json parameters (prompt) and can scale to 1000s of requests automatically!

Once deployed, an app becomes callable through a POST endpoint https://api.cortex.cerebrium.ai/v4/{project-id}/{app-name}/{function-name} and takes a json parameter, prompt

Great! You made it! Join our Community Discord for support and updates.

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How It 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 our Defining Container Images Guide.