DataCrunch
DataCrunch is a Finnish GPU cloud provider offering H100 SXM5, A100, and V100 instances powered entirely by renewable Nordic hydropower. Competitive on-demand and reserved pricing makes it a strong choice for EU-based AI teams that want high-performance GPU compute with a low carbon footprint. DataCrunch provides bare-metal-level performance with cloud convenience, and its Nordic data center location offers natural cooling efficiency and strong EU data sovereignty guarantees.
Cheapest On-Demand
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Cheapest Spot
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GPU Listings
0
Billing
On-demand, Reserved
Performance Benchmarks
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Provider Info
Headquarters
Helsinki, Finland
Founded
2019
Regions
EU (Finland)
Min Commitment
None
Support
Standard
Strengths
- ▸100% renewable energy — hydropower powered
- ▸Competitive H100 and A100 pricing
- ▸EU data sovereignty and GDPR compliance
- ▸Low-latency Nordic interconnects
Limitations
- ▸Single region — Finland only
- ▸Smaller provider — limited scale for very large clusters
- ▸Fewer enterprise integrations vs hyperscalers
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DataCrunch GPU pricing overview
DataCrunch is a GPU cloud provider headquartered in Helsinki, Finland. DataCrunch is a Finnish GPU cloud provider offering H100 SXM5, A100, and V100 instances powered entirely by renewable Nordic hydropower. Competitive on-demand and reserved pricing makes it a strong choice for EU-based AI teams that want high-performance GPU compute with a low carbon footprint. DataCrunch provides bare-metal-level performance with cloud convenience, and its Nordic data center location offers natural cooling efficiency and strong EU data sovereignty guarantees. Billing is On-demand, Reserved with a minimum commitment of None. Available regions include EU (Finland). On-demand GPU instances can be provisioned in minutes with no upfront cost, making DataCrunch suitable for both short-duration experiments and sustained production workloads.
DataCrunch vs other GPU providers
DataCrunch competes with providers including Lambda Labs, CoreWeave, RunPod, Paperspace, Vast.ai, and the major hyperscalers (AWS, Google Cloud, Azure) for GPU compute workloads spanning LLM training, fine-tuning, and inference serving. Key differentiators include: 100% renewable energy — hydropower powered; Competitive H100 and A100 pricing; EU data sovereignty and GDPR compliance. Use the side-by-side comparison tool above to see DataCrunch pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all active listings alongside 94+ providers in a single sortable view.
Best use cases for DataCrunch
DataCrunch is best suited for: ESG-conscious AI teams, EU-based LLM training and fine-tuning, Researchers needing affordable H100 access, Teams with EU data residency requirements. Support tiers range from Standard, making it viable for both individual researchers and enterprise teams with SLA requirements. For workloads requiring the highest single-GPU throughput, H100 SXM5 instances with NVLink interconnect deliver the best performance per dollar at scale. For cost-sensitive fine-tuning or inference of models up to 13B parameters, A100 40GB or RTX 4090 instances typically offer the best value.
DataCrunch billing model and cost structure
DataCrunch uses On-demand, Reserved pricing. On-demand instances are billed per second or per hour depending on the instance type, with no termination fees. Spot pricing is not currently available on this provider — all instances are on-demand. Reserved instance pricing, where available, can reduce costs by 30–60% for predictable long-running workloads. Always compare the effective hourly rate including egress, storage, and networking costs when evaluating total cost of ownership across providers.
Choosing the right GPU on DataCrunch
GPU selection depends on model size, precision, and whether your workload is compute-bound or memory-bandwidth-bound. For LLM training above 30B parameters, H100 80GB SXM5 instances with NVLink are the standard choice — the 3,350 GB/s HBM3 bandwidth and 989 TFLOPS FP16 throughput make them 2–2.5× faster than A100 for transformer workloads. For inference of 7B–13B models in FP16 or BF16, A100 40GB offers the best cost-per-token on most providers. RTX 4090 instances are ideal for fine-tuning, prototyping, and quantized inference (INT4/INT8) of models up to 70B. Read the H100 vs A100 guide or the GPU benchmarks for ML guide for a full breakdown.
How DataCrunch pricing data is collected
Prices shown are sourced from DataCrunch's public pricing API or pricing page and refreshed every 15 minutes. On-demand rates reflect the current list price for a single GPU instance in the cheapest available region. Spot prices, where available, reflect interruptible instance rates at the time of the last snapshot. All prices are in USD per hour. Daily snapshots are retained for 90 days and visualised in the GPU price history charts — useful for identifying seasonal pricing patterns and evaluating whether current rates are above or below the 30-day average.
Evaluating managed LLM inference APIs as an alternative to self-hosted GPU compute? Compare live LLM token prices across OpenAI, Anthropic, Google, Groq, and 14+ other providers. The cheapest GPU cloud guide covers the break-even analysis between self-hosted and managed inference at different request volumes.
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0 GPU configurations available. On-demand, Reserved billing.