Private LLM hosting vs SambaNova
We rent DGX Sparks, so weigh that against everything below. It's worth saying plainly first: SambaNova isn't a GPU rental company. It designs its own reconfigurable dataflow chips and sells fast inference on a curated set of models as a managed API, so comparing it to a Spark is really comparing two different products that both end up serving LLM requests. A Spark is a machine you rent and control. SambaNova is an API you call. Which one fits depends on whether you want to operate hardware or just get tokens back.
Side by side
| SambaNova | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| What it is | A managed inference API running on SambaNova's own custom accelerator chips, not rented general-purpose GPU hardware. | A dedicated NVIDIA DGX Spark you rent by the hour and access with root SSH. |
| What you control | Whichever models and settings SambaNova exposes through its API; check their current site for the model list and customization options, since these change. | Everything on the machine: OS, model server, any open-weight model or your own fine-tune, monitoring, backups. |
| Where it runs | SambaNova's own data centers; confirm current facility locations and any EU presence directly on their site. | EU-Central (Prague), on one dedicated machine, no region choice because there's only the one. |
| Who can see your data | Governed by SambaNova's own API terms and data handling policy for requests sent through their service; review those directly. | 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 | Typically priced per token processed through the API; published rates change over time, check SambaNova's current pricing page. | One flat hourly rate regardless of request volume: $0.79/hour on-demand, $0.59/hour held. Per pricing. |
| Operational overhead | Minimal: call the API, get a response, no infrastructure to run. | You run the model server, updates, and monitoring yourself. More control, more responsibility. |
The cost shape, not a head-to-head number
Because SambaNova prices by tokens and a Spark prices by the hour, there's no single fair dollar comparison, so we're not going to invent one. A Spark running spark-1x continuously for a full month costs 730 hours × $0.79/hour = $576.70, flat, per pricing, regardless of how many tokens you push through it. That favors steady, high-volume usage, since the machine costs the same whether it's idle or saturated. Token-metered pricing like SambaNova's favors bursty or low-volume usage, since you only pay for what you actually process. Check SambaNova's current per-token rate for the specific model you'd use and run your own expected volume through both models before deciding.
Migration path
There isn't a direct migration in the usual sense, since SambaNova doesn't expose the underlying hardware or let you deploy arbitrary weights onto it. What moves is the application layer: if your app calls an OpenAI-compatible endpoint, pointing it at a Spark running vLLM instead of SambaNova's API is mostly a base-URL and model-name change, assuming SambaNova's API is also OpenAI-compatible for your use case, which you should confirm on their docs.
When SambaNova is the right choice
- You want fast inference on a supported model without operating any hardware at all.
- Your usage is bursty or unpredictable, so per-token pricing suits your traffic better than a flat hourly rate.
- You don't need to run your own fine-tune or a model outside SambaNova's supported list.
When a dedicated Spark is the right choice
- You want to run your own open-weight model or fine-tune, not a fixed menu of hosted models.
- You want root access to the machine and full control over the stack, not an API you call.
- Your usage is steady enough that a flat hourly rate beats per-token pricing, or EU data residency and a standard DPA are requirements.
FAQ
Is SambaNova a GPU rental service like GPUwerk?
No. SambaNova builds its own reconfigurable dataflow chips and sells access to models running on that hardware as a managed API, not as a rented GPU you SSH into. GPUwerk rents a dedicated NVIDIA DGX Spark you control directly. They solve overlapping problems, fast LLM inference, in structurally different ways.
Can I run my own model on SambaNova hardware?
SambaNova's platform is generally oriented around a curated set of models it runs for you as an API service, not arbitrary root access to the underlying chips; check their current site for exactly which models and customization options are on offer, since this has evolved over time. On a Spark, you have root access and can run any open-weight model or your own fine-tune.
Is SambaNova cheaper than a Spark?
The two aren't priced the same way, so a direct dollar comparison is misleading. SambaNova typically prices its API by tokens processed; check their current pricing page for a number. A Spark is a flat $0.79/hour regardless of token volume, which can be cheaper at high, steady request volume and more expensive if you barely use the machine.
Why would I choose a dedicated Spark over SambaNova's API?
Mainly control and data handling. A Spark is hardware you operate yourself, with root access, in an EU data center, running whatever model and stack you choose. SambaNova is a managed API on proprietary silicon: less to operate, but you're calling someone else's service rather than running your own machine.
GPUwerk's own figures on this page ($0.79/hour, $0.59/hour, $576.70/month) come from our published pricing. SambaNova's per-token API pricing is not something GPUwerk can verify or restate; check SambaNova's own pricing page directly.