Overview
Strengths & Best For
Beam is a serverless GPU platform that lets developers deploy AI models and run H100, A100, and T4 compute jobs with automatic scaling and per-second pay-per-use billing — no infrastructure management required. A Python-native SDK (`beam deploy`) and fast cold starts make it easy to build and ship LLM inference APIs, batch ML pipelines, and AI model serving endpoints quickly. Beam handles container builds, GPU provisioning, and scaling automatically. Free tier available. A strong choice for Python-first teams that want serverless GPU infrastructure with predictable, usage-based pricing.
- Python-native SDK — `beam deploy` ships a GPU endpoint in minutes
- Auto-scaling with fast cold starts — no idle cost
- Per-second billing — pay only for actual compute time
- Free tier available for prototyping
- Handles container builds and GPU provisioning automatically
- Less control over underlying infrastructure vs raw VMs
- Smaller GPU catalog than dedicated cluster providers
- Not suitable for multi-node distributed training
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CloudGPU rents on-demand GPUs billed per minute, with root SSH access and templates for Ollama, JupyterLab, ComfyUI and LLaMA-Factory. Prices come from CloudGPU's public price feed.
- Per-minute billing — pay only for what you use
- Root SSH access on every instance
- Ready-to-use templates: Ollama, JupyterLab, ComfyUI, LLaMA-Factory
- Wide consumer and data-center GPU selection
- Smaller provider — limited scale vs hyperscalers
- No enterprise SLAs or compliance certifications
- Region information not published per instance
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Live GPU Pricing
Green = lower price. On-demand hourly rates. Updated every 15 minutes.
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Key differences
Beam
Serverless GPU infrastructure — Python-native, auto-scaling, per-second billing
CloudGPU
On-demand GPUs billed per minute — RTX 3090 to A100, root SSH, Ollama and ComfyUI templates