Beam

Beam is a serverless GPU compute platform that enables running Python functions on GPU cloud infrastructure via a simple API. Rather than managing GPU instances, teams decorate Python functions and Beam handles provisioning, execution, and teardown. For agents that need to trigger GPU-intensive AI workloads — running inference on large models, generating embeddings, or executing custom ML functions — Beam's job submission API provides async execution without infrastructure management overhead.

Authentication uses API keys for the REST API and SDK. The key model is straightforward for server-side agent integration. API keys control billing-sensitive GPU compute operations — teams should monitor usage and implement appropriate cost controls for agents triggering GPU jobs in response to user requests.

Documentation is developer-friendly and covers the function decoration, deployment, and invocation patterns clearly. The async job pattern documentation is essential for agents building on Beam — the polling and webhook patterns for result retrieval are well-explained. Teams evaluating Beam for serverless GPU workloads will find the documentation covers the practical integration patterns.

The API covers function deployment, job submission, and result retrieval. The primary agent pattern is async job submission: agents POST a function invocation request, receive a job ID, and poll for completion or receive a webhook when results are available. The Python SDK handles function packaging and deployment; the REST API handles runtime job management.

Reliability is appropriate for a serverless GPU compute platform. Cold start times for GPU functions can be significant (seconds to tens of seconds for large model weights), which agents should account for in timeout configurations. Beam's infrastructure handles GPU availability scheduling, which can introduce queue delays during high-demand periods.