Private LLM hosting vs FluidStack
We rent DGX Sparks, so weigh that against everything below. FluidStack operates its own GPU clusters directly, which makes it a single-fleet operator like GPUwerk rather than a marketplace reselling third-party capacity. The real difference is what each is built for: FluidStack is sized around large multi-GPU clusters for training runs and heavy inference workloads, while GPUwerk rents a single dedicated DGX Spark per customer, sized for a team running its own private LLM stack rather than training a model from scratch. If your workload actually needs a multi-node cluster, this page won't talk you out of that; it's here to help you tell which category your workload falls into.
Side by side
| FluidStack | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| What it is | A GPU cloud provider operating its own clusters directly, sized for large multi-GPU training and inference workloads. | A single-purpose GPU host: one dedicated DGX Spark, owned and operated by GPUwerk, rented by the hour. |
| Who operates the hardware | FluidStack directly; it's a single-fleet operator, not a marketplace of third parties. | GPUwerk directly. The company you're paying is the company running the machine. |
| Sizing | Built for multi-GPU clusters, from single instances up to large training deployments; check FluidStack's current site for available configurations. | One machine per deployment, fixed at 128GB unified memory. No cluster sizing decision to make. |
| Region | Multiple data center locations globally; confirm current EU availability directly with FluidStack for your compliance needs. | EU-Central (Prague), exclusively. One location, no region selection to get wrong. |
| Who can see your data | Governed by FluidStack's own terms and data processing agreement; review their current published terms for specifics. | 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." |
| Hardware consistency | Depends on the GPU model and cluster configuration selected at deployment time. | Every node is the same: 128GB unified memory, NVIDIA's DGX Spark architecture, identical specification across the fleet. |
| Pricing model | Set by GPU model, cluster size, and commitment term; check FluidStack's current pricing page for the configuration you'd need. | 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. |
| Contracts and DPA | FluidStack's own standard terms and DPA; review their current published documents. | 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: orchestration, model server, monitoring, backups, plus multi-node networking if you're training across GPUs. | OS, model server, any RAG or agent layer, monitoring, backups, on a single machine with no multi-node networking to manage. GPUwerk keeps only a recovery copy of /workspace for hardware failures, refreshed roughly every six hours; it is not a backup service. |
The worked cost example
Take a private inference workload that runs continuously across a full month on one machine, not a training cluster. Two ways to serve it:
FluidStack. Priced by GPU model, cluster size, and commitment term, published on FluidStack's own pricing page and varying with the configuration you'd actually deploy. A fair single-machine comparison needs FluidStack's smallest available instance rate, not their cluster pricing, so GPUwerk didn't invent a blended figure here. Check FluidStack's current site for that number.
Dedicated Spark. At $0.79/hour, running continuously for a 730-hour month costs 730 × $0.79 = $576.70, flat, at one known rate for one dedicated machine. If you hold the reservation instead of running it, the held rate drops that to 730 × $0.59 = $430.70.
For a workload that genuinely needs multiple networked GPUs, FluidStack's cluster model is the right tool and a single Spark won't substitute for it. For a workload that fits on one machine, a Spark avoids paying for cluster orchestration you don't need.
Migration path
Both expose a Linux GPU instance, so migration for a single-node workload is mostly re-deploying your own stack. Serve a model through vLLM on a Spark and on a single FluidStack instance, and both expose an OpenAI-compatible endpoint from vLLM itself. Put LiteLLM in front of either for request logging and key management. If you're moving a multi-GPU training setup down to a single Spark, expect to re-plan around one machine's memory ceiling rather than a cluster's aggregate capacity.
When FluidStack is the right choice
- You need multiple GPUs networked together for training or a large-scale inference workload.
- Cluster size and GPU model selection matter more than a single fixed-spec machine.
- You're comfortable managing multi-node orchestration and want flexibility on commitment terms.
When a dedicated Spark is the right choice
- Your workload fits on one machine and you don't need cluster orchestration overhead.
- A single, fixed, published hourly rate under one standard EU DPA matters more than configuration flexibility.
- You want the same specification every time, with no sizing decisions between deployments.
FAQ
Is FluidStack a good alternative to GPUwerk for LLM hosting?
FluidStack operates its own GPU clusters directly rather than reselling third-party capacity, which puts it in the same single-operator category as GPUwerk rather than a marketplace. Where the two differ is scale and target workload: FluidStack is built around large multi-GPU clusters for training and big inference jobs, while GPUwerk rents a single dedicated DGX Spark per customer for smaller, self-hosted private LLM workloads at a fixed EU rate. If you need hundreds of GPUs for a training run, FluidStack's clusters fit that better. If you need one known, private, EU-hosted machine for inference or a small team's private AI stack, a Spark fits better.
What's the difference between a DGX Spark and a FluidStack cluster node?
A FluidStack cluster is sized to the customer's request, typically multiple high-end datacenter GPUs networked together for training or large-scale inference; check FluidStack's own site for current GPU models and cluster sizes. A DGX Spark is a single, standard machine GPUwerk operates directly: 128GB unified memory, NVIDIA's DGX Spark architecture, the same specification on every node in EU-Central, sized for a single team's private inference workload rather than a multi-node training run.
Is a DGX Spark cheaper than FluidStack?
They're usually not solving the same sizing problem, so a direct dollar comparison can be misleading. A dedicated Spark costs $0.79/hour flat for one machine. FluidStack's pricing depends on GPU model, cluster size, and commitment term, so check FluidStack's current pricing page for a real number against the specific cluster you'd need. For a single-machine private LLM workload, compare the Spark's flat rate against FluidStack's smallest available instance rather than its cluster pricing.
Why choose a DGX Spark over FluidStack?
Mainly fit and simplicity for a single-machine private workload. A Spark is sized, priced, and billed for exactly one dedicated node in EU-Central, at one published hourly rate under one standard DPA, with no cluster sizing or commitment decisions to make. FluidStack is built for teams that need multiple GPUs networked together; if that's not your workload, a single Spark is the simpler, cheaper way to get a private, dedicated machine.
GPUwerk did not find a single FluidStack price representative of every cluster configuration; check FluidStack's own pricing page. GPUwerk's own figures ($0.79/hour, $0.59/hour, $576.70/month) come from our published pricing.