DataVolt vs Runcrate: Pricing, Specs, Strengths & Weaknesses

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

Overview

Attribute

DataVolt

Runcrate

Provider type

Specialist

Founded

2023

2024

Headquarters

Europe

Billing model

On-demand, Spot

Per-second on-demand

Min commitment

None

Support tier

Community → Standard

Standard

Regions

1 regions

3 regions

Strengths & Best For

DataVolt

DataVolt is a European GPU cloud offering H100, H200, A100, and L40S instances with competitive on-demand and spot GPU rental pricing, full EU data residency, and straightforward access for AI and ML workloads. Spot availability makes it a cost-effective option for interruptible LLM training and fine-tuning jobs, while on-demand instances suit production inference. A practical European GPU cloud for teams that need GDPR-compliant infrastructure with flexible billing.

Strengths

Competitive H100/H200 pricing
Spot availability
EU data residency
Simple pricing

Weaknesses

Smaller provider — limited scale vs hyperscalers
Fewer regions than major cloud providers
Less mature ecosystem and fewer integrations

Best For

EU AI teamsCost-sensitive H100 workloadsSpot-tolerant training

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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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