Private LLM hosting vs DeepInfra
We rent DGX Sparks, so weigh that against everything below. DeepInfra is a serverless inference API: pick an open model from their hosted catalogue, call an OpenAI-compatible endpoint, and pay per token generated. There's no server to manage and no capacity to plan for. A dedicated Spark is the opposite shape: you rent the physical machine, deploy whatever model you choose, and run it yourself. The comparison is really about who operates the model and who else's traffic shares the hardware underneath it.
Side by side
| DeepInfra | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| What it is | A serverless API for open-weight models, hosted and scaled by DeepInfra, billed per token. | A dedicated physical machine rented by the hour; you deploy and run the model server yourself. |
| Infrastructure model | Shared, multi-tenant GPU infrastructure. Requests from different customers can land on the same underlying hardware. | Single-tenant. One Spark, allocated to one customer, no other workload on the same GPU. |
| Region | Determined by DeepInfra's own infrastructure footprint; check their site for current data center locations. | EU-Central (Prague), exclusively. One location, no region selection to get wrong. |
| Who can see your data | Governed by DeepInfra's own terms and privacy policy; requests pass through their serving stack. Check their current legal pages for retention and processing details. | 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 | Limited to models DeepInfra has added to its catalogue; check their model list for current availability. | Any model that fits 128GB unified memory: gpt-oss-120b, Llama 3.3 70B, Qwen3-Coder 30B, DeepSeek, fine-tunes, or anything else you can deploy yourself. |
| Speed and concurrency | Depends on DeepInfra's own capacity and the model's popularity at request time; not something a customer controls directly. | 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 million tokens, priced per model, published on DeepInfra's own pricing page; rates vary by model size and change over time. | 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 model or workload, per pricing. |
| Worked cost example | See the full arithmetic below the table. | |
| Contracts and DPA | Governed by DeepInfra's own customer agreement; check their legal pages for jurisdiction and data-processing terms. | 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 | Nothing beyond your own client code. DeepInfra runs the model server, scaling, and infrastructure. | Everything: OS, model server, any RAG or agent layer, monitoring, 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 runs inference continuously across a full month, roughly what a production support bot or an always-on document pipeline looks like. Two ways to serve it:
DeepInfra. Billed per token, priced per model on their own site. A fair dollar comparison needs a specific model and its actual per-token rate, both of which change over time and vary by model size, so GPUwerk didn't invent a blended figure here. Pull the current per-token rate for the model you'd run from DeepInfra's pricing page and multiply by your expected monthly token volume for a real number.
Dedicated Spark. At $0.79/hour, running continuously for a 730-hour month costs 730 × $0.79 = $576.70, flat, regardless of how many tokens that machine actually generates in the month. If you hold the reservation instead of running it, the held rate drops that to 730 × $0.59 = $430.70.
The comparison tips on utilization. A workload that keeps the Spark busy most of the month spreads that $576.70 across a lot of tokens, and the effective per-token cost falls the more you use it. A workload that generates relatively few tokens against a metered per-token rate on DeepInfra may cost less there, since you're only paying for what the model actually produces. Estimate your monthly token volume against DeepInfra's current per-model rate before deciding.
Migration path
Moving off DeepInfra means standing up your own serving stack, which is the main cost of switching. Deploy the same open model behind vLLM on a Spark and you get an OpenAI-compatible endpoint with a similar request shape to what DeepInfra already exposes, so client code often needs only a base-URL change. Put LiteLLM in front for request logging, key management, and a fallback path if you want to keep DeepInfra as a secondary route during the transition. What you gain in exchange for the ops work is a machine nobody else's traffic touches.
When DeepInfra is the right choice
- Your token volume is low, spiky, or unpredictable, and paying only for what you generate beats a flat hourly rate.
- You want an API key and nothing else to manage, no server, no scaling, no monitoring.
- The model you need is already in their catalogue and shared-tenancy hosting is acceptable for your data.
When a dedicated Spark is the right choice
- Your data can't sit on infrastructure shared with other tenants, even behind an API.
- Your volume is steady enough that a flat hourly rate beats a metered per-token bill.
- You want to run a model, fine-tune, or serving stack that isn't in a hosted catalogue.
FAQ
Is DeepInfra a good alternative to GPUwerk for LLM hosting?
For teams that just want an API endpoint for an open model without managing any infrastructure, yes, that's what DeepInfra is built for: pick a model from their hosted catalogue, call an OpenAI-compatible endpoint, and pay per token generated. GPUwerk rents you the hardware itself, a dedicated DGX Spark, so you run and control the model server yourself. If you want zero infrastructure, DeepInfra's model fits. If you want the model, the weights, and the data path to be entirely under your own control, a dedicated node fits better.
What's the difference between a DGX Spark and DeepInfra's serverless API?
DeepInfra runs the model for you on shared infrastructure and charges per token; you get an API key and a bill. A DGX Spark is a dedicated physical machine rented by the hour: root access, your own model server, your own data never touching anyone else's tenancy. DeepInfra's approach removes ops work; the Spark removes the shared-tenancy question entirely.
Is a DGX Spark cheaper than DeepInfra?
It depends on your volume and how continuously you run. DeepInfra publishes per-token pricing by model on its own site, check the current rate card for the model you'd use. A dedicated Spark costs $0.79/hour flat, so at high, steady utilization the per-token cost of a dedicated node tends to fall relative to a metered API; at low or spiky volume, paying only for tokens generated can come out cheaper. Run the arithmetic on your own traffic before deciding.
Why choose a DGX Spark over DeepInfra?
Mainly data control and predictable cost. A dedicated Spark means no other tenant's workload shares the machine, and GPUwerk's DPA states we don't access the content on your instance. If continuous, high-volume inference is your pattern, a flat hourly rate is also easier to budget than a per-token bill that moves with traffic. If your volume is low or unpredictable, DeepInfra's pay-per-token model avoids paying for idle hardware.
GPUwerk did not find a single DeepInfra per-token rate that fairly represents every model in their catalogue; check DeepInfra's own pricing page for the model you'd run. Spark throughput figures come from Dendro Logic's concurrency benchmark, detailed on our benchmarks page.