Beam
Cloud platform for running AI inference, sandboxes and task queues
Beam is a cloud platform for running AI workloads, including GPU inference endpoints, durable task queues, and stateful sandboxes for AI agents. It aims to reduce infrastructure management by handling container orchestration, autoscaling, and cold-start performance, with sub-second cold starts and memory snapshotting. Workloads can run on Beam's infrastructure or in a user's own AWS, GCP, or Azure account.
The platform provides GPU inference endpoints with autoscaling and checkpointing, durable task queues with retries and scheduled jobs, and stateful sandboxes with persistent storage, filesystem snapshots, and Docker-in-Docker support. It supports Python, TypeScript, and Go SDKs, integrates with GitHub Actions for CI/CD, and offers logging, monitoring, and secrets management. Workloads can span multiple clouds and regions.
Beam is aimed at developers and teams building AI products, model inference services, RL environments, and training pipelines. Pricing is usage-based, billed by the millisecond, with serverless GPU tasks, sandboxes, on-demand machines, reserved multi-node clusters, and storage tiers. Usage tiers include a free Developer plan and paid Team and Growth plans with seat-based pricing; bring-your-own-cloud is charged as a management fee on compute.
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