Beam Cloud
FreemiumRun agents, sandboxes, and inference on the compute you own, and burst to Beam when you need more.
Beam Cloud is an open-source serverless cloud for AI and ML workloads. It lets developers deploy Python functions, REST APIs, task queues, and sandboxes on CPUs and GPUs with sub-second cold starts and autoscaling to zero. Teams can run workloads on their own cloud accounts or burst to Beam's managed fleet, paying only for the time their containers are actively running.
What is it
Beam Cloud is an open-source serverless platform that runs AI and ML workloads, including inference endpoints, task queues, sandboxes, and containerized services, across CPUs and GPUs.
What it can do
Deploy Python functions as autoscaling HTTP endpoints, run durable background queues, execute untrusted code in isolated sandboxes, serve custom GPU models, and burst from private clouds to Beam's managed infrastructure.
Who is it for
ML engineers, AI developers, and platform teams who need on-demand GPU and CPU compute without provisioning virtual machines or writing YAML.
Key Features
Serverless GPU Inference — Production model serving
Deploy Python functions as autoscaling HTTP endpoints on GPUs such as H100, A100, and RTX 4090, with sub-second cold starts and per-second billing.
AI Sandboxes — Isolated code execution
Run untrusted or LLM-generated code in secure, stateful containers with persistent storage, file system operations, and memory snapshots.
Durable Task Queues — Async job processing
Build background pipelines with configurable retries, event-based callbacks, scheduled jobs, and no timeouts for long-running ML work.
Memory Snapshots — Fast restore and branching
Snapshot a running container and restore it into thousands of concurrent isolated runs, each with realtime streaming output.
Use Cases
Deploy Custom LLM Inference APIs
Serve open-source LLMs via vLLM or custom PyTorch models as autoscaling HTTP endpoints without managing Kubernetes or load balancers.
Run AI Agent Sandboxes
Safely execute LLM-generated code in isolated, stateful containers with persistent storage and snapshots for agentic workflows.
Process Async ML Pipelines
Fan out OCR, summarization, embedding, or batch inference jobs across thousands of task-queue workers for bursty, cost-effective processing.
Fine-Tune and Train Models
Launch on-demand GPU training jobs on A100 or H100 that automatically spin down when finished, paying only for active compute time.
Pricing plans
Frequently Asked Questions
Discussion
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