Beam
Beam offers serverless infrastructure designed for Generative AI, enabling users to run GPU inference and training jobs efficiently. With features like autoscaling, fast cloud storage with storage vol
What is Beam?
Beam is a serverless infrastructure platform purpose-built for running generative AI workloads. It handles GPU inference and training jobs without requiring you to manage servers or infrastructure directly. You deploy your AI models using simple commands, and Beam automatically scales resources up and down based on demand. The platform includes fast cloud storage, quick cold start times for inference, and local debugging tools to test before deployment. Beam integrates with CI/CD pipelines so you can automate model updates and deployments. Beam suits developers and teams building AI applications who want to skip infrastructure management. Rather than provisioning and monitoring GPU instances yourself, you focus on your models and code. The freemium model lets you start experimenting at no cost, paying only for what you use as your projects grow.
Key Features
GPU inference and training
Run machine learning workloads on GPU hardware without managing instances
Autoscaling
Resources automatically adjust based on traffic and job queue depth
Fast cloud storage with volumes
Persistent storage optimised for AI workflows and model files
Local debugging
Test and debug your models locally before pushing to production
CI/CD integration
Connect to GitHub and other tools for automated deployments
Quick cold starts
GPU instances start rapidly so requests don't wait long
Pros & Cons
Advantages
- No infrastructure management required; focus on model code instead of servers
- Pay only for actual GPU usage with the freemium model, good for prototyping
- Fast deployment and iteration with simple commands and local debugging
- Automatic scaling handles traffic spikes without manual intervention
Limitations
- Vendor lock-in; moving models to another platform requires rework
- Less control over underlying hardware and configuration compared to self-managed infrastructure
- Pricing can grow quickly for heavy usage or long-running training jobs
Use Cases
Running inference APIs for large language models and image generation services
Fine-tuning and training models on GPUs without provisioning hardware upfront
Building chatbot backends that scale with user demand
Batch processing jobs for computer vision or NLP on scheduled intervals
Rapid prototyping and testing of new AI models before production deployment