Private LLM hosting vs CoreWeave
We rent DGX Sparks, so weigh that against everything below. CoreWeave is a large GPU cloud built for AI labs and enterprises running training jobs and high-throughput inference across many GPUs, often through committed contracts. If what you actually need is one machine to run one or a few open-weight models for one team, a Spark is a much smaller purchase in every sense: smaller hardware, smaller commitment, smaller bill.
Side by side
| CoreWeave | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| What it's built for | Large-scale training and inference clusters: many GPUs, high-speed interconnect, Kubernetes-native orchestration, sized for AI labs and enterprise workloads. | One dedicated machine for one team's inference workload. Not built to scale to multi-node training; that's not the product. |
| GPU hardware | A range of current-generation NVIDIA data-center GPUs at cluster scale; specific availability and generation mix change over time, check CoreWeave's own capacity pages. | NVIDIA DGX Spark: Grace Blackwell, 128GB unified memory, one unit per instance. |
| Commitment and minimums | Historically oriented around committed, contracted capacity for larger customers; self-serve, smaller-scale access has existed but check current terms directly, this is an area CoreWeave has changed over time. | No minimum term. $0.79/hour on-demand, billed per minute, cancel any time. |
| Where data is processed | Whichever CoreWeave data center region you provision in; region selection and current EU footprint should be confirmed on CoreWeave's own site. | EU-Central (Prague), on one dedicated machine, no region choice because there's only the one. |
| Who can see your data | Governed by CoreWeave's own customer agreement and data processing terms; review those directly for the specifics of access and subprocessors. | 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. |
| Pricing model | Per-GPU-hour, typically scaled and discounted by commitment length and cluster size. Published figures vary by GPU generation and contract term, check CoreWeave's pricing page for a current number. | One flat hourly rate regardless of workload: $0.79/hour on-demand, $0.59/hour held. Per pricing. |
| Operational model | Managed Kubernetes-native platform with CoreWeave-specific tooling (CKS, Slurm on Kubernetes, storage and networking products) layered on top of raw compute. | Root SSH access to a container on the Spark. No managed orchestration layer; you run vLLM, LiteLLM, or whatever stack you choose yourself. |
The worked cost example
Take a team that wants one always-on model endpoint for internal use, no training, no cluster. On a Spark, running spark-1x continuously for a full month is 730 hours × $0.79/hour = $576.70, before tax, per pricing. That's one number, fixed, regardless of how many requests hit the endpoint or which open-weight model is loaded.
CoreWeave doesn't publish a comparable single number for this scenario because its pricing depends on which GPU generation you provision, whether you're on a committed contract or a shorter-term rate, and how much capacity you reserve. If your workload genuinely needs multiple GPUs with fast interconnect, or you're training rather than just serving an already-trained model, CoreWeave's cluster pricing and volume discounts can end up considerably cheaper per GPU-hour than a single retail Spark. If you need exactly one GPU-class machine for inference and don't want to negotiate a contract, the Spark's flat $576.70/month or $0.79/hour is the simpler number to plan around. Run your actual GPU count and term through CoreWeave's own pricing page for a real comparison.
Migration path
Model weights for open models move freely between the two: a checkpoint trained or fine-tuned on CoreWeave can be copied to a Spark and served there with vLLM, and vice versa. What doesn't move is anything built on CoreWeave's platform layer, its Kubernetes-native tooling, storage products, or multi-node scheduling, none of which has an equivalent on a single Spark because a Spark is one machine, not a cluster.
When CoreWeave is the right choice
- You're training models rather than only serving them, or your inference workload genuinely needs multiple GPUs with fast interconnect between them.
- You can commit to a contract term and your volume is large enough that cluster-scale, committed pricing beats retail hourly rates.
- You want a managed Kubernetes-native platform handling orchestration, storage, and networking rather than building that yourself.
When a dedicated Spark is the right choice
- You need one machine for one team's inference workload, not a cluster, and don't want to negotiate a contract to get a reasonable rate.
- You want a fixed, predictable hourly number with no volume tiers or commitment discounts to work out.
- You want the shortest possible answer to who can access your data: one dedicated machine, root access under your own SSH keys, hosted in EU-Central.
FAQ
Is CoreWeave a fit for a single small deployment?
It can be, but CoreWeave built its business and its pricing around large training and inference clusters for AI labs and enterprises, so a single-GPU or single-node workload is often better served by a provider sized for that, including a single dedicated Spark. Check CoreWeave's own site for current minimums and self-serve options, since these change.
Does CoreWeave host in the EU?
CoreWeave operates data centers in multiple regions including parts of Europe; which regions are available and what capacity is on offer changes over time, so confirm current EU availability directly on CoreWeave's site before assuming a specific country. A GPUwerk Spark is fixed in EU-Central, Prague, with no region selection because there is only the one machine.
What GPUs does CoreWeave offer versus a DGX Spark?
CoreWeave offers a range of NVIDIA data-center GPUs at cluster scale, including current-generation Hopper and Blackwell parts, sized for training and large-batch inference. A DGX Spark is a single desktop-form-factor unit built around Grace Blackwell with 128GB of unified memory, aimed at running one or a few open-weight models for one team, not multi-node training.
Is a Spark cheaper than CoreWeave?
It depends on scale and commitment. CoreWeave's per-GPU-hour pricing is typically tied to committed contracts and cluster sizing, and published rates vary by GPU generation and term length, so check CoreWeave's own pricing page for a number you can trust. A single GPUwerk Spark runs $0.79/hour on-demand with no minimum term, which is a different shape of purchase entirely: one machine, hourly, no contract.
Can I move a model between CoreWeave and a Spark?
Yes, for open-weight models. The model weights themselves are portable; what does not move is any orchestration built around CoreWeave's Kubernetes-native platform, its Slurm integration, or multi-node training setup, none of which has an equivalent on a single Spark.
GPUwerk's own figures on this page ($0.79/hour, $0.59/hour, $576.70/month) come from our published pricing. CoreWeave does not publish a single comparable per-machine rate; its pricing depends on GPU generation, cluster size, and contract term, so check CoreWeave's own pricing page directly rather than relying on a figure here.