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BEAM CLOUD
Serverless GPU compute for Python with sub-second cold starts
ABOUT
Deploying Python AI workloads to the cloud typically requires managing Dockerfiles, Kubernetes clusters, GPU instance provisioning, and auto-scaling rules before running a single inference job. Beam solves this by providing a Python decorator that turns any function into a serverless cloud endpoint running on GPUs — with automatic scaling to zero when idle, sub-second cold starts, and pay-per-second billing that eliminates idle GPU costs.
INSTALL
pip install beamINTEGRATION GUIDE
1. Deploy ML model inference endpoints with GPU acceleration by adding a single decorator to a Python function
2. Run batch inference and training jobs on serverless GPUs that auto-scale and shut down when idle
3. Execute sandboxed Python code in secure, isolated environments for trusted execution or code evaluation
4. Build and deploy AI-powered APIs with automatic HTTPS, authentication, and rate limiting out of the box
5. Run scheduled GPU workloads like model retraining or data processing on a cron without managing infrastructure
TAGS
serverlessgpucloudinferencepythoncomputeinfrastructure