DataVolt

DataVolt GPU pricing overview

DataVolt is a specialist GPU cloud provider headquartered in Europe. 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. Billing is On-demand, Spot with a minimum commitment of None. Available regions include EU. On-demand GPU instances can be provisioned in minutes with no upfront cost, making DataVolt suitable for both short-duration experiments and sustained production workloads.

DataVolt vs other GPU providers

DataVolt 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: Competitive H100/H200 pricing; Spot availability; EU data residency. Use the side-by-side comparison tool above to see DataVolt 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 DataVolt

DataVolt is best suited for: EU AI teams, Cost-sensitive H100 workloads, Spot-tolerant training. Support tiers range from Community → 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.

DataVolt billing model and cost structure

DataVolt uses On-demand, Spot 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 DataVolt

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 DataVolt pricing data is collected

Prices shown are sourced from DataVolt'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.