Beam vs Paperspace: Pricing, Specs, Strengths & Weaknesses
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Beam and Paperspace. Rates sourced from public APIs — verify current availability and billing terms before purchasing.
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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Paperspace (now part of DigitalOcean) offers A100, H100, A6000, A5000, A4000, and RTX 5000 GPU instances alongside Gradient, its managed ML platform with Jupyter notebooks, experiment tracking, and one-click model deployment. Monthly dedicated options available. Gradient plans include a Free tier through paid tiers. A beginner-friendly on-demand GPU cloud with a polished notebook-centric experience, now integrated into the DigitalOcean ecosystem.
- Gradient managed ML platform with Jupyter notebooks
- Simple UI and beginner-friendly onboarding
- H100 on-demand availability
- Free tier available for beginners
- DigitalOcean ecosystem integration
- Acquired by DigitalOcean — Gradient product direction uncertain
- Limited GPU catalog vs specialist providers
- Notebook-centric UX not ideal for production pipelines
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Live GPU Pricing
Region Coverage
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Key differences
Beam
Serverless GPU infrastructure — Python-native, auto-scaling, per-second billing
Paperspace
ML platform with Gradient notebooks and GPU instances — now part of DigitalOcean