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The right European GPU cloud provider is the one that can run your specific model, in the required region, at an acceptable measured cost and with contracts that satisfy your data obligations. Start by defining whether you need training, fine-tuning or inference; then match GPU memory, GPU count, interconnect, CPU, storage and network to that workload. Only after those requirements are fixed should you compare prices from Scaleway, OVHcloud, Nebius or another provider.
A catalog listing is not proof that a GPU is available in your preferred zone, and an hourly figure is not the cost of a completed training run. Verify capacity, normalize the full configuration and benchmark the actual software stack before signing a long commitment.
1. Define the workload before comparing providers
“AI workload” covers jobs with very different resource profiles. A provider that is excellent for short inference calls may be a poor choice for distributed training. Write down the job you are buying capacity for, not just the model name.
Training
Training usually needs sustained GPU utilization, fast local or attached storage, high-bandwidth communication between GPUs and predictable capacity for days or weeks. Multi-GPU jobs can be limited more by interconnect and network behavior than by the advertised peak compute of one accelerator.
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Fine-tuning
Fine-tuning may use fewer GPUs than pre-training, but memory demand varies with parameter count, sequence length, batch size, optimizer and precision. A configuration that fits inference may still run out of memory during checkpointing or optimizer updates. Include checkpoint storage and the time required to reload data when estimating cost.
Inference and serving
Inference is commonly constrained by latency, concurrency, model memory and data-transfer paths. For a chatbot or interactive API, determine the required requests per second and tail-latency target. For batch inference, throughput and storage bandwidth may matter more than single-request latency. Scaleway’s GPU selection material explicitly discusses inference and chatbot use cases, but you should validate the recommendation against your own model and traffic pattern.
Mixed or graphics workloads
Some teams combine model development, visualization or graphics jobs with AI experiments. A bare GPU virtual machine can be flexible, while a managed cluster or model service may reduce operations work. Record whether you need a VM, a multi-node cluster, or a managed endpoint before comparing offers that are not equivalent.
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2. Turn requirements into a configuration sheet
Use one worksheet for every candidate configuration. This prevents a low hourly number from hiding missing CPU, storage or networking capacity.
- Model and framework: record the model version, framework, CUDA or equivalent runtime, precision and any custom kernels.
- Peak GPU memory: measure the largest allocation during loading, forward and backward passes, optimizer steps and checkpointing. Leave headroom for fragmentation and concurrent processes.
- GPU quantity and topology: specify one GPU, a fixed group or elastic scaling. Note whether GPUs are in one host, one rack or multiple nodes, and ask what interconnect is supplied.
- CPU and RAM: include data preprocessing, tokenization, compilation and host-side batching. A GPU can be idle while waiting for an undersized CPU or memory tier.
- Storage: state capacity, throughput, IOPS, persistence requirements and whether datasets and checkpoints remain in the same region as the GPU.
- Network: estimate ingress, egress, east-west traffic and the need for private networking. Training across nodes requires a different network profile from a single-GPU endpoint.
- Runtime: estimate startup time, active GPU hours, idle development hours, checkpoint duration and how often the job is restarted.
- Geography and legal constraints: identify permitted countries or regions, required availability zones, data classes, retention rules and any restrictions on administrator access or subprocessors.
- Operations: list image requirements, Kubernetes or scheduler integration, API access, quota needs, monitoring, support response and migration requirements.
3. Compare the whole configuration, not just the GPU name
The table below is a research starting point, not a performance ranking. “Not stated” means the cited public material did not establish a comparable value. Confirm every cell with a current quote for your account, region and service.
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| Provider | Workload fit | Accelerator configuration | Geography and capacity | Performance evidence | Cost information | Data governance | Operations |
|---|---|---|---|---|---|---|---|
| Scaleway | GPU instances marketed for training, fine-tuning and serving; broader offerings include clusters and model-as-a-service. | Published Paris (PAR-1) example: one L4, 24 GB GPU memory, 8 vCPUs and 48 GB RAM. Interconnect details for this example: not stated. | European data-residency messaging; live quota and zone capacity must be checked when ordering. | No matched independent benchmark for a reader’s model was established. | PAR-1 L4 example lists €0.79 per hour; the page’s monthly figure is an estimate. Storage, network, tax and support are separate questions. | Provider statements about European location, sovereignty and compliance require service-specific contractual verification. | Instances, clusters and model-service options are described; exact images, APIs, quotas and support terms depend on the service. |
| OVHcloud | L4 product material describes AI and other GPU use cases; public-cloud configurations include L40S, A10 and A100 examples. | GPU, CPU, RAM, storage and network combinations are shown in the public price material; exact values vary by configuration. | European hosting is claimed on the product material. Confirm the required region, zone and present inventory. | No matched independent benchmark for a reader’s model was established. | The surfaced public price table says its prices start 1 October 2026. Currency, tax treatment, storage, network, support and commitment terms require a fresh quote. | Compliance and European-hosting claims should be checked against the exact service, DPA, subprocessors and access controls. | Assess image availability, APIs, orchestration, quotas, support and exit procedures for the selected public-cloud product. |
| Nebius AI Cloud | GPU cloud positioned for AI workloads and large-scale cluster reservations. | Exact model, memory, host CPU, RAM and interconnect depend on the quoted configuration; not stated as one comparable bundle in the cited pricing material. | Confirm the country, region, zone and capacity for the requested cluster before committing. | No matched independent benchmark for a reader’s model was established. | Pricing material identifies effective GPU-hour prices from 1 October 2026 for some configurations and says multi-month, large-scale cluster reservations can receive up to 35% off on-demand rates. This is a provider-stated offer subject to reservation terms. | Obtain service-specific processing and storage locations, DPA language, subprocessors, certifications and administrative-access details. | Validate cluster provisioning, scheduler or Kubernetes integration, quota, support and portability with a technical trial. |
The European Commission has announced awarded provider groupings that include one involving OVHcloud and another involving Scaleway. That procurement context can matter to public-sector diversification, but it does not rank GPU performance or establish technical fit for a private workload.
4. Verify regional capacity and quotas
A product page proves that a configuration exists in a catalog, not that it can be provisioned now. Capacity is time- and zone-dependent. The OECD’s cloud measurement approach treats public-cloud regions and availability zones as point-in-time observations, which is a useful reminder to record when and where you checked.
- Select the exact country, region and availability zone required by your policy.
- Ask the provider to confirm current inventory for the exact GPU, count, host type and storage tier. Request the expected lead time, not just a “generally available” statement.
- Check account quotas and whether a quota increase is required. Ask how long an increase normally takes and whether it is guaranteed for a reservation.
- Test provisioning through the console or API in the intended zone. Record the timestamp, configuration identifier and any placement or capacity error.
- For a cluster, test the full node count and networking mode. One available GPU does not prove that eight GPUs, or several nodes, can be placed together.
- Ask what happens during maintenance, host failure or a capacity shortfall: automatic restart, replacement capacity, queueing or termination.
5. Calculate the real cost
Normalize every quote to the unit that represents your work: cost per completed training run, per million tokens served, per image, or per hour of useful throughput. A GPU-hour comparison is only meaningful when the configurations and utilization are comparable.
Cost components to include
- Compute: GPU and instance-hour charges, including attached CPU and RAM.
- Storage: persistent volumes, object storage, snapshots, checkpoint copies and backup retention.
- Network: ingress, egress, inter-zone traffic, private links and traffic to external data sources.
- Software and support: managed control planes, premium support, licenses and monitoring.
- Taxes and currency: determine whether VAT is included, which entity invoices you and which exchange rate your finance team will use.
- Commitments: reservations, minimum terms, cancellation rules, burst pricing and whether unused capacity expires.
- Idle time: development, debugging, queueing and startup hours when the GPU is allocated but not processing your workload.
Published figures and their limits
Scaleway’s pricing page, accessed 13 October 2026, lists an L4-1-24G configuration in PAR-1 at €0.79 per hour. The bundle is one L4 with 24 GB of GPU memory, 8 vCPUs and 48 GB of RAM. The page’s monthly equivalent is an estimate, and both figures are volatile; confirm them before purchase and add storage, networking, tax and support.
OVHcloud’s surfaced public price table states that its listed prices start 1 October 2026 and shows L40S, A10 and A100 configurations with associated resources. Treat those amounts as dated provider prices, not a market-wide benchmark, and request the current regional total.
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Nebius states that multi-month reservations of large-scale clusters can be up to 35% below on-demand rates. “Up to” is not a guaranteed discount: confirm the minimum term, committed quantity, eligible GPU types, payment schedule, cancellation terms and treatment of unused capacity.
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For a first estimate, multiply the fully loaded hourly instance cost by allocated hours, then add storage, network, support and tax. Divide by measured useful throughput to obtain a unit cost. Run the calculation at realistic utilization—for example, include time spent loading data, compiling kernels and writing checkpoints—rather than assuming 100% GPU utilization.
6. Benchmark the model you actually run
No comparable independent benchmark was established for these providers. Vendor generation claims or a peak theoretical figure cannot substitute for a matched test.
Make the test reproducible
- Use the same model checkpoint, dataset slice, software versions, drivers, precision and tokenizer on every provider.
- Fix batch size, sequence length, gradient accumulation, compilation settings and random seed where applicable.
- Measure warm-up separately from steady-state performance. Record tokens per second, samples per second, step time, latency percentiles, throughput at target concurrency and peak GPU memory.
- Include data loading, checkpoint writes and any object-storage reads in a second end-to-end measurement. A fast GPU attached to slow storage can lose in a production run.
- For distributed training, record scaling efficiency as GPU count increases and measure communication time, not only aggregate compute.
- Repeat enough times to expose variability. Save the instance type, zone, start and end times, and any throttling or pre-emption event.
- Convert the result to cost per useful unit—such as cost per million tokens or completed training step—using the current quote.
7. Check data governance at the service and contract level
“European provider” or “European hosting” is not a complete data-residency answer. A service may use different locations for control-plane metadata, support operations, backups, logs or subprocessors. Ask for written answers for the exact GPU service and region.
- Where are customer data, model weights, checkpoints, logs and backups processed and stored?
- Can support or engineering personnel outside the permitted geography access systems, and under what approval and logging controls?
- Which subprocessors handle storage, networking, monitoring or support?
- What data-processing agreement, retention schedule, deletion process and breach-notification terms apply?
- Which certifications cover the selected service rather than the provider generally?
- Can you export images, datasets, checkpoints, logs and infrastructure definitions if you leave?
- Are encryption keys customer-controlled, and how are tenant isolation and secure deletion documented?
Scaleway and OVHcloud publish European-location or compliance claims, and Nebius provides its own service documentation, but those statements should be treated as starting points for contract review rather than proof that a particular legal requirement is met.
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8. Evaluate operations and exit effort
A technically suitable GPU can still be expensive if engineers spend days rebuilding environments or recovering failed jobs. During a proof of concept, test the operational path you will use in production.
Provisioning and images
Confirm whether the required driver, framework and accelerator libraries are available as a maintained image. Test image customization, boot time, secrets handling and repeatable infrastructure deployment through the provider API or infrastructure-as-code tooling.
Scheduling and scaling
Determine whether the service supports your scheduler, Kubernetes integration, queue priorities, autoscaling and placement constraints. Ask how quotas interact with scale-out and whether capacity is reserved for the entire job.
Observability and support
Check GPU utilization, memory, temperature, power, host health, network counters and storage latency in the standard monitoring tools. Clarify support hours, severity definitions, escalation paths and the provider’s replacement procedure for a failed host.
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Portability
Export a test checkpoint and recreate the environment elsewhere. Document proprietary APIs, image formats, networking dependencies and egress charges that could make migration difficult.
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9. A practical selection procedure
- Document the job: workload type, model, framework, peak memory, GPU count, runtime, throughput or latency target and multi-GPU communication needs.
- Set non-negotiables: permitted countries and zones, data classes, contract terms, support level, availability target and required integrations.
- Build a shortlist: select configurations that meet memory and scaling requirements, not merely the cheapest GPU listing.
- Check live capacity: verify quota, inventory and placement for the exact zone and node count; save the date and response.
- Request normalized quotes: ask each provider for compute, CPU/RAM, storage, network, support, tax, currency and commitment terms in one document.
- Run the same benchmark: use the representative model and record reproducible throughput, latency, utilization, scaling and end-to-end storage behavior.
- Review governance: obtain the DPA, processing locations, subprocessors, access controls, retention and deletion terms for the chosen service.
- Model failure and exit: test restart, checkpoint recovery, quota expansion, capacity loss and export of data and infrastructure.
- Choose against your trade-off: rank measured performance, total cost, governance, operational effort and capacity reliability according to the workload’s priorities.
- Recheck volatile facts: confirm prices, catalogs, capacity and program terms immediately before signing or publishing a comparison.
10. When each researched provider merits a trial
Scaleway
Investigate Scaleway when you want European GPU instances and a product range that also discusses clusters and model services. Its published PAR-1 L4 example is a useful baseline for a small inference or development configuration, but it is not evidence that the L4 is the best choice for your model. Verify current monthly pricing, storage and network charges, quota and the contractual meaning of its residency and sovereignty statements.
OVHcloud
Investigate OVHcloud when the available L40S, A10 or A100 configurations match your memory or throughput target and you need a European public-cloud region. The price table exposes more than the GPU alone, which helps build a full estimate. Confirm the dated pricing basis, tax, region, capacity, interconnect and service-specific data terms.
Nebius AI Cloud
Investigate Nebius when you need a larger cluster or are considering a multi-month reservation. Its stated discount of up to 35% may matter for sustained utilization, but only after you verify the reservation’s eligibility, minimum commitment and exit conditions. Benchmark the complete cluster and obtain written processing-location and access terms.
Common mistakes to avoid
- Choosing by GPU brand or model name while ignoring memory, CPU, storage and interconnect.
- Assuming a European headquarters guarantees that every service component and support operation stays in Europe.
- Using a catalog count as proof of live capacity in a particular zone.
- Comparing an on-demand GPU-hour with a discounted reservation without modeling utilization and commitment risk.
- Benchmarking only a synthetic kernel instead of the model, data path and concurrency you will operate.
- Ignoring egress and checkpoint traffic because ingress is free or inexpensive.
- Signing a long reservation before testing quota increases, failure recovery and export.
- Presenting provider claims as independent performance or compliance findings.
Final decision rule
Select the provider whose verified configuration meets the workload’s memory and scaling needs, is actually available in the required European zone, delivers the best measured cost for useful output and satisfies your contractual data controls. If two candidates are close, prefer the one with clearer capacity commitments, simpler operations and a tested exit path rather than the one with the lowest headline GPU-hour.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
