Beam review
A flexible hosting layer for serverless GPU inference, queues, and batch jobs.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Beam is a cloud platform for deploying AI inference endpoints, task queues, sandboxes, training jobs, and other GPU workloads. It is designed for developers and engineering teams building AI products without managing traditional cloud infrastructure. Its serverless execution model combines GPU-backed containers, autoscaling, persistent storage volumes, memory snapshots, and task management. Developers can also run long-lived code-execution sandboxes and distributed batch workloads through function mapping.
Pricing starts with the free Developer plan, which includes unlimited apps, 5 GPU containers, 30 CPU containers, unlimited API requests, and 1 seat. The $89-per-month Team plan raises the allowance to 50 GPU containers and 1,000 CPU containers, keeps unlimited apps and API requests, and includes 3 seats. The Growth plan includes unlimited apps, 1,000+ GPU containers, unlimited CPU containers, unlimited API requests, and unlimited seats. Monthly usage-tier fees are separate from pay-as-you-go compute charges, so teams should account for both the plan and workload costs. The main fit is developers who need to move from a free environment to shared capacity while retaining serverless execution.
Beam’s deployment options are broader than a cloud-only hosting service. Workloads can run on Beam’s cloud, connect to AWS, GCP, or other infrastructure through bring-your-own-cloud deployment, or run in a private environment with the self-hosted product. GPU checkpoint restore and persistent volumes support models and weights that need to survive individual runs, while queues and scheduled jobs suit asynchronous processing. Teams that want a managed path for inference, batch jobs, and GPU workloads should find the feature mix relevant. Organizations seeking only a narrow inference endpoint, or wanting a single predictable compute bill without separate usage charges, may prefer a simpler hosting option.
Beam pros and cons
- Where it wins
- Serverless GPU inference with workload-based autoscaling
- Queues support retries, callbacks, scheduled jobs, and batch processing
- Cloud, bring-your-own-cloud, and private self-hosted deployment options
- Where it doesn't
- Developer limits usage to 5 GPU and 30 CPU containers
- Monthly plan fees are separate from pay-as-you-go compute charges
- Growth includes no published monthly price in the available plan details
Beam fact sheet, pricing and score →
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