Beam

Beam is a GPU compute platform built specifically for AI workloads. It handles serverless inference, durable task queues, and isolated sandboxes, all defined in Python without Dockerfiles or YAML config. You can run on Beam's cloud or connect your own AWS, GCP, or bare metal accounts and let Beam orchestrate across all of them.

The core differentiator is boot time. Beam uses memory snapshots to restore GPU containers in under a second, up to 35× faster than a traditional cold start. That matters when you're running inference at scale or building agent pipelines where latency compounds quickly.

Key capabilities:

• GPU inference endpoints – deploy models on H100s, A10Gs, or 4090s with autoscaling baked in. Scale to zero when idle, burst to hundreds of containers when traffic spikes.
• Task queues – durable, queue-depth-based autoscaling with automated retries, scheduled jobs, and event callbacks. Good for batch workloads like transcription or document processing.
• Sandboxes – stateful, long-running environments for AI agents. Attach persistent storage, run Docker-in-Docker, snapshot the filesystem, and branch into thousands of parallel isolated runs.
• Multi-cloud routing – workloads route across clouds and 30+ regions in real time, keeping execution close to your users.
• CI/CD integration – deploy directly from GitHub Actions without changing your workflow.

Compared to tools like Modal or dstack, Beam leans hard on the sandbox and snapshotting story, which makes it a natural fit for agent frameworks that need stateful, parallelizable execution environments.

Pricing starts at $0.69/hr for a 4090, with $30 in free credits refreshed monthly. SOC 2 Type II certified.