Deploying AI projects and vibe-coded apps on Serverless Containers

Reviewed on May 12, 2026

Building AI-powered applications is now accessible to every developer. Whether you are using Scaleway Generative APIs to integrate AI capabilities or developing AI-driven applications, moving your projects from local development to production is seamless.

Scaleway Serverless Containers provides the ideal deployment platform for AI applications. Deploy any containerized workload with automatic scaling, SSL-enabled URLs, and pay-per-use pricing that charges only when your code runs.

Before you start

To complete the actions presented below, you must have:

A Scaleway account logged into the console
Owner status or IAM permissions allowing you to perform actions in the intended Organization
Created a containers namespace
Created a Container Registry namespace and pushed a container image to it
A Dockerfile for your application

Why Serverless Containers for AI projects

AI applications have unique traffic patterns and resource requirements. Serverless Containers addresses these specific needs:

Scale to zero: AI projects and proofs of concept often experience bursty traffic. You only pay when your container processes requests. When idle, it scales to zero at no cost.
High-resource capabilities: Scaleway supports up to 12 GB of RAM and 6 vCPUs per instance, enabling you to run local embeddings or resource-intensive libraries.
Complete flexibility: Containers support any language, framework, or version. Install any OS-level dependency your AI application needs, such as ffmpeg for audio processing or custom system libraries.
Instant deployment: Each container receives a unique, SSL-secured URL automatically. No load balancer configuration or SSL certificate management required.

From AI-generated code to production

Your AI development workflow integrates seamlessly with Scaleway Serverless Containers:

Generate code: Use your AI assistant to create your application. Generate Dockerfile: Ask your AI agent to create a Dockerfile. The AI handles environment setup and dependency management. Deploy to Scaleway: Push your image to Container Registry and deploy to Serverless Containers in a few clicks.

Key platform features

Serverless Containers abstracts infrastructure complexity so you can focus on your application:

Namespaces: Organize related services together. Group your frontend, backend, and worker containers while sharing environment variables across them.
Automatic SSL: Every container receives a secure HTTPS endpoint without certificate configuration.
Autoscaling: The platform automatically creates new instances during traffic spikes and removes them when demand decreases.

Best practices for AI applications

Optimizing startup performance

Containers that scale to zero experience a brief cold start on the first request after inactivity.

Tip

Use lightweight base images like python:3.11-slim or node:20-alpine to reduce startup time.

Tip

For user-facing applications requiring instant responses, configure min_scale=1 to keep one instance warm.

Monitoring and debugging

Use Scaleway Cockpit to monitor your application:

Track memory consumption (critical for LLM workloads)
View real-time logs to debug issues
Set up alerts for performance anomalies

Quickstart: Deploy your AI application

Create a Dockerfile for your Python application:

FROM python:3.11-slim

# Set environment variables
ENV PYTHONUNBUFFERED=1
WORKDIR /app

# Install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY . .

# Start the application
# Serverless Containers injects a PORT variable; ensure your app listens on it
CMD ["sh", "-c", "uvicorn main:app --host 0.0.0.0 --port ${PORT:-8080}"]

Deploy your application:

Push your image to your Scaleway Container Registry namespace using docker push.
Navigate to Serverless > Containers in the Scaleway Console.
Select your namespace and click Deploy container.
Choose your image from the registry and configure resources.
Click Deploy. Your application receives a production URL within minutes.

Refer to How to deploy a container for complete deployment instructions.

Going further

Enhance your deployment with these features:

Connect your container to a Private Network for secure backend communication
Add a custom domain for branded URLs
Configure autoscaling to optimize performance and costs

Still need help?

Create a support ticket