Private LLM hosting vs OVHcloud
We rent DGX Sparks, so weigh that against everything below. OVHcloud is a French cloud provider and one of Europe's larger infrastructure companies, and like GPUwerk it operates from the EU, so this comparison is about scope rather than region. OVHcloud runs public cloud GPU instances, bare metal, managed Kubernetes, object storage, and a lot more, with GPU compute as one line in a large catalogue. GPUwerk runs one product: a dedicated DGX Spark by the hour, with nothing else attached to the bill.
Side by side
| OVHcloud GPU instances | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| What it is | A GPU compute product inside a large general-purpose cloud, alongside bare metal, managed Kubernetes, storage, and hosted private cloud. | A single-purpose GPU host: one dedicated DGX Spark, rented by the hour, nothing else attached. |
| Region | Multiple European and international data centers; check OVHcloud's own region list for which products are available where. | EU-Central (Prague), exclusively. One location, no region selection to get wrong. |
| Hardware choice | Multiple GPU instance types and bare-metal GPU servers, per OVHcloud's own current catalogue. | One machine: the DGX Spark, 128GB unified memory, with an optional two-node cluster for larger workloads. |
| Who can see your data | Governed by OVHcloud's own terms and data processing addendum; check their legal pages for the current text. | Nobody at GPUwerk. Our DPA states GPUwerk "hosts the machine but does not access, read, copy, index, or analyse the content of the controller's instance." |
| Model choice | Whatever you deploy, unrestricted by the provider. Larger GPU or bare-metal instance types can run bigger models or serve higher concurrency than a single Spark. | Whatever you deploy too, but bounded by 128GB unified memory: gpt-oss-120b, Llama 3.3 70B, Qwen3-Coder 30B, DeepSeek, and similar fit; models needing substantially more memory don't, without the two-node cluster. |
| Speed and concurrency | Depends on the GPU type and count rented; OVHcloud publishes instance specs but tok/s for a given model is yours to benchmark. | A single Spark, single-stream: gpt-oss-120b decodes at 33.5 tok/s and reaches 862.8 tok/s aggregate at 256 concurrent requests (Dendro Logic's concurrency benchmark, run on their own Spark). Raw logs and methodology on our benchmarks page. |
| Pricing model | Per hour, varying by GPU type and whether it's public cloud or bare metal, published on OVHcloud's own pricing page. | Per hour, billed per minute: $0.79/hour on-demand, $0.59/hour for a customer-requested stop that holds your reservation. One rate regardless of workload, per pricing. |
| Worked cost example | See the full arithmetic below the table. | |
| Contracts and DPA | Governed by OVHcloud's own customer agreement and data processing terms; both companies are EU entities, so check the specifics against your own requirements. | A standard GDPR Article 28 DPA published free at /legal/dpa, no negotiation required. Sub-processor list at /legal/sub-processors states none are engaged for instance workloads. |
| What you operate yourself | Everything on the instance: OS, model server, monitoring, backups. OVHcloud manages the physical hardware and network underneath, and offers a wide range of managed products on top if you want them. | The same: root SSH access to your own container, the model server, any RAG or agent layer, monitoring, and backups. GPUwerk keeps only a recovery copy of /workspace for hardware failures, refreshed roughly every six hours; it is not a backup service, so that responsibility is entirely yours. |
The worked cost example
Take a workload that generates 500 million output tokens a month, roughly what a busy internal support bot or a document-processing pipeline running continuously might produce. Two ways to serve it:
OVHcloud. GPU instance and bare-metal GPU pricing varies by tier and card, published on OVHcloud's own pricing page. A fair comparison needs a specific instance type and its measured tok/s for the model you'd run, both of which depend on your choice, so GPUwerk did not invent a blended figure here. Pull the current rate for the instance you'd actually use from OVHcloud's site and benchmark your model on it for a real number.
Dedicated Spark. gpt-oss-120b reaches 862.8 tok/s aggregate at 256 concurrent requests, per Dendro Logic's concurrency benchmark on a single Spark (cited on our benchmarks page). That is 862.8 × 3,600 = 3,106,080 tokens per hour if the node stays saturated at that concurrency the whole time. 500,000,000 ÷ 3,106,080 ≈ 161 hours. At $0.79/hour, 161 × $0.79 = $127.19, before tax.
That Spark figure only holds under the assumption stated: the node is busy at 256 concurrent requests for the full 161 hours, back to back. A larger OVHcloud GPU or bare-metal instance could plausibly clear the same 500 million tokens in fewer wall-clock hours at a higher hourly rate, so whether it's cheaper overall depends on the instance's price-per-token-per-hour relative to a Spark's, not just its sticker price. Run both through your own model and traffic pattern; a raw GPU-hour comparison without a specific instance type and a specific tok/s figure isn't one either provider can hand you.
Migration path
Both are bare GPU instances, so migration is mostly re-deploying your own stack. A model served through vLLM on a Spark and the same setup on an OVHcloud instance expose an identical API shape if you run the same serving software on both, the OpenAI-compatible endpoint comes from vLLM, not from the underlying hardware provider. Put LiteLLM in front of either for request logging and key management. What changes is the hardware spec underneath: a model tuned to a larger OVHcloud instance's memory and bandwidth may need re-quantizing or a smaller variant to fit a single Spark's 128GB, and vice versa moving the other way.
When OVHcloud is the right choice
- You already run other infrastructure on OVHcloud, bare metal, Kubernetes, object storage, and want the GPU on the same account and billing.
- You need a GPU type or memory footprint beyond a single Spark's 128GB, or bare-metal hardware options a single dedicated machine doesn't offer.
- You want a wider choice of regions than one dedicated location provides.
When a dedicated Spark is the right choice
- You want GPU compute and nothing else to configure, no instance-type catalogue to read through first.
- A single, fixed hourly rate matters more than platform breadth.
- Your model fits comfortably in 128GB and you'd rather not pay for headroom you won't use.
FAQ
Is OVHcloud a good alternative to GPUwerk?
Depends on scope. Both are EU-headquartered (OVHcloud is French, GPUwerk operates from EU-Central in Prague), so this isn't a data-residency argument the way US-headquartered competitors are. OVHcloud is a large general-purpose cloud with GPU instances as one product among many, bare metal, managed Kubernetes, object storage, and more. GPUwerk sells one thing: a dedicated DGX Spark by the hour. If GPU inference is the whole job, GPUwerk removes the instance-catalogue decision. If you already run infrastructure on OVHcloud, keeping the GPU on the same account may matter more.
Does OVHcloud offer dedicated GPU hardware?
OVHcloud sells both public cloud GPU instances and bare-metal servers with GPUs, per their own current catalogue; check their site for which tier fits dedicated-hardware requirements. GPUwerk's DGX Spark is dedicated for the length of your reservation by default, no other tenant shares the box while it's yours.
Is a DGX Spark cheaper than OVHcloud's GPU instances?
It depends on the instance type you'd compare it against. In the worked example on this page, 500 million generated tokens a month costs about $127 in Spark compute at $0.79/hour, assuming the node sustains 256 concurrent requests the entire time. OVHcloud publishes its GPU pricing on its own site; pull the current rate for the instance you'd actually run before comparing directly.
Why pick a single dedicated Spark over an OVHcloud GPU instance?
Mainly simplicity and a fixed rate. OVHcloud's catalogue spans public cloud, bare metal, and hosted private cloud, useful range if you need it, but more to compare before you start. A Spark is one machine, one hourly rate, run end to end by GPUwerk.
GPUwerk did not find a single OVHcloud GPU price that fairly represents every tier in their catalogue; check OVHcloud's own pricing page for the instance you'd run. Spark throughput figures come from Dendro Logic's concurrency benchmark, detailed on our benchmarks page.