Private LLM hosting vs Nebius
We rent DGX Sparks, so weigh that against everything below. Nebius is an AI-infrastructure-focused cloud provider that, per public reporting and the company's own about page, originated as a spin-out from Yandex's cloud business and is now headquartered in Amsterdam, check their about page for the authoritative version of that history. Nebius rents GPU capacity at cluster scale, aimed at training and high-throughput inference workloads. GPUwerk rents one dedicated DGX Spark per tenant. The comparison is mostly about scale: Nebius is built for teams renting many GPUs, GPUwerk for teams that need one dedicated machine.
Side by side
| Nebius | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| What it is | An AI-infrastructure cloud provider renting GPU clusters, generally at multi-node scale, for training and inference workloads. | A single dedicated DGX Spark per tenant, rented by the hour, no cluster scheduling involved. |
| Region | Multiple data centers per Nebius's own current region list; check their site for availability by product. | EU-Central (Prague), exclusively. One location, no region selection to get wrong. |
| Tenancy model | GPU cluster capacity, typically allocated and scaled across many nodes for a workload, per Nebius's own product documentation. | One dedicated machine, held entirely for one tenant for the length of the reservation, with an optional two-node cluster for larger workloads. |
| Who can see your data | Governed by Nebius'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. Multi-GPU clusters can train or serve models far larger than a single Spark's 128GB allows. | Whatever you deploy too, bounded by 128GB unified memory: gpt-oss-120b, Llama 3.3 70B, Qwen3-Coder 30B, DeepSeek, and similar fit; larger models need cluster-scale capacity like Nebius offers. |
| Speed and concurrency | Depends entirely on cluster size and GPU type rented; Nebius publishes product specs but tok/s for a given model and cluster configuration 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 GPU-hour, varying by card and cluster configuration, published on Nebius'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 Nebius's own customer agreement and data processing terms; check their legal pages for the specifics that apply to your account and region. | 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 cluster: OS, orchestration, model server, monitoring, backups. Nebius manages the physical hardware and network underneath. | 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:
Nebius. GPU cluster pricing runs per GPU-hour and varies by card and configuration, published on Nebius's own pricing page. A fair comparison needs a specific cluster configuration 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 configuration you'd actually use from Nebius'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 Nebius multi-GPU cluster could clear the same 500 million tokens in far fewer wall-clock hours at a much higher hourly rate, so whether it's cheaper depends on the cluster's price-per-token relative to a Spark's, not the sticker rate alone. For a single-tenant inference workload at this scale, a Spark is likely to be the lower-overhead option; for training or much higher throughput, a Nebius cluster is a different tool for a different job. Run both through your own model and traffic pattern.
Migration path
A model served through vLLM on a Spark and a comparable single-node deployment on Nebius 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. Moving from a Nebius cluster deployment down to a single Spark means giving up multi-node orchestration and any model sharded across GPUs; moving up from a Spark to a Nebius cluster means adding that orchestration layer for the first time. Neither move is trivial if your workload was actually using the cluster's scale.
When Nebius is the right choice
- You're training models or need multi-GPU, multi-node capacity that a single Spark's 128GB can't provide.
- Your inference throughput needs exceed what a single dedicated machine or even a two-node cluster can sustain.
- You need to scale GPU capacity up and down dynamically across a fleet rather than reserve one fixed machine.
When a dedicated Spark is the right choice
- Your inference workload fits comfortably on one machine and you don't need cluster orchestration.
- You want a single, fixed hourly rate with no cluster-configuration decision to make first.
- Predictable, dedicated single-tenant capacity matters more than the ability to scale to many nodes.
FAQ
Is Nebius a good alternative to GPUwerk?
Depends on scale. Nebius is an AI-infrastructure-focused cloud provider offering GPU clusters, generally at larger scale than a single machine, for training and high-throughput inference. GPUwerk rents one dedicated DGX Spark per tenant, with 128GB unified memory and a flat hourly rate. If you need cluster-scale GPU capacity, Nebius is built for that. If a single dedicated machine covers your inference workload, GPUwerk is the simpler and likely cheaper path to get there.
Where is Nebius headquartered?
Nebius originated as a spin-out from Yandex's cloud business and, per public reporting and the company's own about page, is now headquartered in Amsterdam. Check Nebius's own about page for the current, authoritative statement of their corporate structure and history, since these can change and GPUwerk has not independently verified every detail.
Is a DGX Spark cheaper than a Nebius GPU cluster?
For a single-tenant inference workload, often yes, but the comparison depends heavily on scale. 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. Nebius prices GPU cluster capacity on its own pricing page, and a multi-GPU cluster serving the same token volume in far less wall-clock time could come out ahead or behind depending on the cluster's rate and achieved throughput. Check Nebius's current pricing for the specific configuration you'd use.
Why choose a dedicated Spark over a Nebius GPU cluster?
Mainly tenancy model and simplicity. GPUwerk gives each customer one dedicated Spark, no shared cluster scheduling, no instance-type selection, a single flat rate. Nebius's cluster model is built for teams that need to scale GPU capacity up and down across many nodes, which is the right tool if your workload actually needs that scale.
GPUwerk did not find a single Nebius cluster price that fairly represents every configuration in their catalogue; check Nebius's own pricing page for the setup you'd run. Spark throughput figures come from Dendro Logic's concurrency benchmark, detailed on our benchmarks page.