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BeamvsRuncrate

Beam vs Runcrate: Pricing, Specs, Strengths & Weaknesses

Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Beam and Runcrate. Rates sourced from public APIs — verify current availability and billing terms before purchasing.

Overview

Attribute
Beam
Runcrate
Provider type
Specialist
Specialist
Founded
2022
2024
Headquarters
New York, NY
US
Billing model
Per-second usage
Per-second on-demand
Min commitment
None
None
Support tier
Standard
Standard
Regions
1 regions
3 regions

Strengths & Best For

Beam

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.

Strengths
  • 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
Weaknesses
  • Less control over underlying infrastructure vs raw VMs
  • Smaller GPU catalog than dedicated cluster providers
  • Not suitable for multi-node distributed training
Best For
Serverless LLM inference APIsBatch ML processing pipelinesPython-first teams wanting zero-ops GPU infrastructureRapid prototyping with free tier
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Runcrate

Runcrate is a GPU cloud founded in 2024 offering on-demand instances with per-second billing and no minimum commitment — the meter stops the moment you stop the instance. The platform aggregates GPU capacity across eight global regions spanning North America, Europe, and Asia, covering 14 GPU models from RTX A6000 and A100 through H100 and RTX PRO 6000. Full root access via SSH, Docker support, and custom images are included on every instance. An OpenAI-compatible inference API serves open-source models billed per token, and reserved or dedicated capacity is available for teams with monthly volume commitments.

Strengths
  • Per-second billing — no minimum commitment
  • OpenAI-compatible inference API
  • 14 GPU models including H100 and RTX PRO 6000
  • 8 global regions across NA, EU, and Asia
  • Full root SSH access with Docker and custom images
  • Reserved and dedicated capacity available
Weaknesses
  • No enterprise SLAs or uptime guarantees
  • Smaller provider — limited scale vs hyperscalers
  • No managed platform or containers
Best For
Teams needing flexible per-second GPU billingOpen-source model inference via APIMulti-region GPU workloadsCost-sensitive on-demand training and fine-tuning
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Region Coverage

Beam — 1 regions
US
Runcrate — 3 regions
USEUAPAC

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Provider pages

Beam pricing and specs Runcrate pricing and specs

Key differences

Beam

Serverless GPU infrastructure — Python-native, auto-scaling, per-second billing

Runcrate

GPU cloud with per-second billing and an OpenAI-compatible inference API

AttributeBeamRuncrate
Provider typespecialistspecialist
Billing modelPer-second usagePer-second on-demand
Min commitmentNoneNone
Regions1 (US)3 (US, EU, APAC)
Support tierStandardStandard
Best forServerless LLM inference APIs, Batch ML processing pipelinesTeams needing flexible per-second GPU billing, Open-source model inference via API
Founded / HQ2022 · New York, NY2024 · US