Private LLM hosting vs Paperspace
We rent DGX Sparks, so weigh that against everything below. Paperspace, now part of DigitalOcean, is a GPU cloud built around Gradient, a managed notebook and ML workflow product aimed at data scientists, alongside raw GPU instances. That managed layer is genuinely useful if you're training or experimenting interactively; it's extra structure you don't need if you just want to stand up an inference endpoint for an open-weight model.
Side by side
| Paperspace (DigitalOcean) | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| What it's built for | Managed notebooks and ML workflows (Gradient) plus general-purpose GPU cloud instances, aimed at data scientists and ML teams. | A dedicated machine for one team's inference or fine-tuning workload, no managed notebook layer required. |
| Where data is processed | Whichever DigitalOcean region you provision in; GPU availability by region changes, check their current capacity. | EU-Central (Prague), on one dedicated machine. |
| Who can access your data | Governed by DigitalOcean's own terms and data processing agreement; review those directly for the current 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. |
| Tooling | Gradient notebooks, managed pipelines, model deployment tooling built on top of the raw compute layer. | Root SSH access to a bare container. You install and run vLLM, LiteLLM, or whatever stack fits, nothing pre-built. |
| Hardware | A range of NVIDIA GPU instance types; specific models and current availability should be checked on DigitalOcean's site since the lineup changes. | NVIDIA DGX Spark: Grace Blackwell, 128GB unified memory, one consistent spec. |
| Pricing model | Per-hour, varying by GPU instance type and tier. Check DigitalOcean's current GPU pricing page for a specific number. | One flat hourly rate: $0.79/hour on-demand, $0.59/hour held. Per pricing. |
| Contracts and DPA | DigitalOcean's standard terms and data processing agreement. | A standard GDPR Article 28 DPA published free at /legal/dpa, no negotiation required. |
The worked cost example
An always-on internal inference endpoint for a month on a Spark: 730 hours × $0.79/hour = $576.70, before tax, fixed regardless of which open-weight model you run. Per pricing.
DigitalOcean's GPU instance pricing varies by which card and tier you provision, and those figures are best pulled from their own current pricing page rather than repeated here secondhand. If your GPU tier of choice on DigitalOcean happens to price below $576.70/month for equivalent always-on hours, that's a real reason to pick them; if it's above, or if you'd rather not compare tiers and just want one fixed number, the Spark's flat rate is the simpler plan. Either way, run the actual instance type you'd use through DigitalOcean's pricing page before deciding.
Migration path
Open-weight model weights and standard container images move between the two without much friction, a checkpoint doesn't care which cloud trained it. What doesn't move is anything you've built specifically on Gradient's notebook and pipeline tooling; a Spark gives you a bare container instead, so any Gradient-specific workflow gets rebuilt as a plain script or service.
When Paperspace is the right choice
- You want a managed notebook environment for interactive experimentation and training, not just an inference endpoint.
- You're already using DigitalOcean for other infrastructure and want GPU capacity in the same account and billing relationship.
- Gradient's pipeline and deployment tooling fits your workflow and saves you from building that layer yourself.
When a dedicated Spark is the right choice
- You just need an inference endpoint for an open-weight model and don't want a notebook layer between you and the machine.
- You want one flat, predictable hourly rate rather than comparing GPU instance tiers.
- You want the shortest possible answer to who can access your data: one dedicated machine, root access under your own SSH keys, EU-Central.
FAQ
Is Paperspace the same company as DigitalOcean?
DigitalOcean acquired Paperspace in 2023 and has since folded parts of its GPU offering into DigitalOcean's own product lineup, while Paperspace-branded products including Gradient notebooks continue to operate. Check DigitalOcean's current site for the up-to-date structure, since this has changed since the acquisition.
Does Paperspace host in the EU?
DigitalOcean operates data centers in multiple regions including some in Europe; specific GPU availability by region changes, so confirm current EU GPU capacity on DigitalOcean's own site before assuming a given country. A GPUwerk Spark is fixed in EU-Central, Prague.
What is Gradient and do I need it?
Gradient is Paperspace's managed notebook and ML workflow product, aimed at data scientists who want a hosted Jupyter-style environment with GPU backing rather than raw machine access. If you just need an inference endpoint for an already-trained open-weight model, that managed layer is often more than you need; a Spark gives you root access to a container and you run whatever server you like, no notebook required.
Is a Spark cheaper than Paperspace's GPU instances?
It depends on the GPU tier and instance type you'd otherwise pick on Paperspace or DigitalOcean, since their per-hour rates vary by GPU class. Check DigitalOcean's current GPU pricing page for a number you can trust. A Spark runs a flat $0.79/hour on-demand regardless of workload, which is a simpler number to plan around even if it isn't always the lowest for every GPU tier.
Can I move a workload from Paperspace to a Spark?
Open-weight model files and standard container images move over without much friction. What doesn't move is anything built specifically on Gradient's notebook and pipeline tooling, since that layer has no equivalent on a Spark, which gives you a bare container and root access instead.
GPUwerk's own figures on this page ($0.79/hour, $0.59/hour, $576.70/month) come from our published pricing. DigitalOcean's current GPU instance pricing was not fetched or reproduced here; check their pricing page directly for figures specific to a GPU tier.