Beam Cloud
What is Beam Cloud?
Quick Summary / TL;DRBeam Cloud is a serverless GPU and cloud infrastructure platform for running AI workloads. It helps developers deploy Python functions, model inference, background jobs, and compute-heavy tasks without spending time managing servers, containers, queues, or GPU setup manually.
- Run AI agents, sandboxes, and persistent runtimes
- Deploy GPU inference, training, and batch workloads
- Autoscaling serverless CPU and GPU compute
About
Use Cases
Automated Testing & Bug Resolution
Easy Local DebuggingTest your code before deploying it, using the exact configuration you'll run in production.
Serverless Gpu Inference
Execute serverless gpu inference inside unified automated pipelines.
Key Features
- Run AI agents, sandboxes, and persistent runtimes
- Deploy GPU inference, training, and batch workloads
- Autoscaling serverless CPU and GPU compute
- Deploy to AWS, GCP, Azure, and Hetzner
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Pricing summary
Beam Cloud offers a free Developer plan with $30 monthly credits, a Team plan at $89 per month, and a custom Growth plan. CPU and GPU resources are billed separately based on usage.
Pricing & Plans
Free Credits
Test Beam Cloud and run small development workloads.
- Starter compute credits
- Python SDK
- Development deployments
- Usage dashboard
Pay As You Go
Metered GPU and CPU infrastructure for production workloads.
- Usage-based compute
- Autoscaling endpoints
- Batch jobs
- Persistent services
Enterprise
Custom infrastructure and support for larger deployments.
- Custom limits
- Volume arrangements
- Security requirements
- Dedicated support
Other pricing notes
- Pricing verified from the official Beam pricing page on 2026-07-25.
- All plans include $30 monthly credits and usage-based compute billing.
- Review GPU pricing, concurrency limits, and runtime costs before production deployment.
Pros & Cons
Pros
- Free developer plan with monthly credits
- Millisecond usage-based billing
- Supports inference, training, and batch jobs
- Autoscaling serverless infrastructure
- Supports deployment to your own cloud
Cons
- Usage costs increase with heavy GPU workloads
- Requires developer or Python experience
- Production workloads may require paid plans
- Costs depend on runtime duration
- Infrastructure platform rather than a no-code tool
Top 3 Alternatives
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