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Runcrate vs Vultr: Pricing, Specs, Strengths & Weaknesses

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

Overview

Attribute
Runcrate
Vultr
Provider type
Specialist
Specialist
Founded
2024
2014
Headquarters
US
Matawan, NJ
Billing model
Per-second on-demand
On-demand (hourly)
Min commitment
None
None
Support tier
Standard
Basic → Enterprise
Regions
3 regions
5 regions

Strengths & Best For

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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Vultr

Vultr offers H100, A100, L40S, RTX 6000 Ada, and RTX 4000 Ada GPU instances across 32 global locations with simple hourly billing and no long-term commitment required. A developer-friendly GPU cloud with a clean API, straightforward pricing, and broad geographic coverage for teams needing AI inference or training capacity close to their users. Vultr Cloud GPU instances include dedicated VRAM, NVMe storage, and generous transfer allowances. A solid choice for global deployment of AI workloads without the complexity of hyperscaler pricing models.

Strengths
  • 32 global locations — wide geographic coverage among specialist GPU clouds
  • Simple hourly billing — no long-term commitment
  • H100, A100, L40S, RTX 6000/4000 Ada in catalog
  • Clean API and developer-friendly onboarding
  • Generous transfer allowances included
Weaknesses
  • Smaller GPU catalog than specialist AI clouds
  • Less ML-focused tooling vs CoreWeave or Lambda
  • Limited spot/preemptible GPU options
Best For
Global deployment of AI workloadsSimple inference and training workloadsDeveloper-friendly teams wanting simple billingTeams needing GPU capacity in emerging regions
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Region Coverage

Runcrate — 3 regions
USEUAPAC
Vultr — 5 regions
USEUAPACAUSA

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Provider pages

Runcrate pricing and specs Vultr pricing and specs

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Key differences

Runcrate

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

Vultr

H100, A100, L40S, RTX 4000/6000 Ada — simple hourly billing across 32 global locations

AttributeRuncrateVultr
Provider typespecialistspecialist
Billing modelPer-second on-demandOn-demand (hourly)
Min commitmentNoneNone
Regions3 (US, EU, APAC)5 (US, EU, APAC, +2)
Support tierStandardBasic → Enterprise
Best forTeams needing flexible per-second GPU billing, Open-source model inference via APIGlobal deployment of AI workloads, Simple inference and training workloads
Founded / HQ2024 · US2014 · Matawan, NJ