Private LLM hosting vs the Anthropic API
We rent DGX Sparks, so weigh that against everything below. If your task needs Claude specifically, this page cannot get you there, a Spark only runs open-weight models, and that is the shape of the product, not a gap we're closing. If an open model already meets your quality bar and your reason for looking at the Anthropic API is where the data goes and who can read it, a dedicated Spark in EU-Central gives you one machine, assigned to you alone, instead of a shared API endpoint operated from the US.
Side by side
| Anthropic API | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| Where data is processed | Anthropic is a US company; its API is primarily served from US and partner cloud infrastructure. GPUwerk did not independently verify current regional processing options for this page; check Anthropic's own trust and compliance documentation for what applies to your account. | EU-Central (Prague), on one dedicated machine. No shared endpoint, no region to configure because there's only the one machine. |
| Who can see your prompts | Anthropic publishes privacy and data usage terms describing retention and whether API data is used for training. GPUwerk has not reviewed the current, complete terms closely enough to restate their specifics as verified fact here; read them directly at Anthropic's own privacy and trust documentation before relying on them for a compliance decision. | 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." There is no abuse-monitoring or logging layer at all, because GPUwerk operates infrastructure, not a model API. |
| Model choice | Claude, Anthropic's closed frontier model family, across several size and capability tiers, including long-context and extended-reasoning variants. | Open-weight models only: gpt-oss-120b, Llama 3.3 70B, Qwen3-Coder 30B, DeepSeek, and anything else you can fit in 128 GB. Claude is not available on a Spark under any arrangement. |
| Speed and concurrency | Anthropic does not publish a general tokens/second figure for the API; rate limits are stated as tokens and requests per minute by usage tier. | 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). Qwen3-Coder 30B-A3B AWQ decodes at 80.9 tok/s single-stream on our own fleet. Raw logs and methodology on our benchmarks page. |
| Pricing model | Per token, billed separately for input and output, varying by Claude model tier. See anthropic.com/pricing for current rates; GPUwerk did not fetch a specific figure for this page. | 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. USD, tax extra, per pricing. |
| Worked cost example | See the full arithmetic below the table. | |
| Contracts and DPA | Anthropic publishes commercial terms and a DPA for API customers on its own site; GPUwerk has not reviewed the current version's clauses closely enough to characterise them here. | 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. |
| Lock-in and portability | The Messages API has its own request and response shape, distinct from the OpenAI-style chat completions format most other providers and Sparks use. A gateway like LiteLLM can normalise between the two; direct client code written against the Messages API needs an adapter. | Open weights mean you can move the model file itself to another Spark, another GPU box, or your own hardware; the model is never Anthropic's or GPUwerk's to withhold. You lose Anthropic's managed platform and have to build any equivalent yourself. |
| What you operate yourself | Nothing at the infrastructure layer; Anthropic manages capacity, scaling, and model serving. You manage prompts, application code, and your own use of rate limits. | Root SSH access to your own container: the model server, any RAG or agent layer, monitoring, and 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 generates 500 million output tokens a month, roughly what a busy internal support bot or a document-processing pipeline running continuously might produce. Two ways to serve it:
Anthropic API. Pricing is per token, billed separately for input and output, and varies by Claude model tier. GPUwerk did not fetch and verify a current per-token price for a specific model at the time of writing. Run your own model and volume through anthropic.com/pricing for a figure you can trust.
Dedicated Spark. gpt-oss-120b reaches 862.8 tok/s aggregate at 256 concurrent requests, per Dendro Logic's concurrency benchmark on a single Spark (cited on our benchmarks page). That is 862.8 × 3,600 = 3,106,080 tokens per hour if the node stays saturated at that concurrency the whole time. 500,000,000 ÷ 3,106,080 ≈ 161 hours. At $0.79/hour, 161 × $0.79 = $127.19, before tax.
That Spark figure only holds under the assumption stated: the node busy at 256 concurrent requests for the full 161 hours, back to back, with no idle time. Real traffic arrives in bursts, so a Spark billed by the hour that sits half-idle waiting for requests can lose ground against a per-token price with no idle charge. Put a current Anthropic number for your chosen model next to $127.19 for your own volume, and you have the real comparison; which one wins is a utilisation question specific to your traffic, and separately, a question of whether your task needs Claude at all.
Migration path
The Anthropic Messages API has its own request and response shape, different from the OpenAI-style chat completions format a Spark serves through vLLM. Client code written directly against the Messages API doesn't drop straight onto a Spark endpoint. Put LiteLLM in front of both and it normalises the difference, giving your application one interface regardless of which one is behind it, along with request logging, key management, and rate limits. What doesn't normalise away is anything tuned to Claude's specific behaviour, reasoning style, or output format, that stays specific to Claude regardless of the gateway in front of it.
When the Anthropic API is the right choice
- Your task requires Claude specifically, and no open-weight model has matched it on your evaluation, particularly for long-context or extended-reasoning work.
- Your token volume is spiky or low, so paying only for tokens generated beats paying for a machine that sits idle between requests.
- You want Anthropic managing capacity, scaling, and rate limits rather than operating that layer yourself.
When a dedicated Spark is the right choice
- An open-weight model, gpt-oss-120b, Llama 3.3, Qwen3-Coder, or similar, already meets your quality bar, so Claude's advantage doesn't apply to your task.
- Your traffic is steady enough to keep the machine busy for most of the hours you're paying for, batch document processing, an internal assistant used all day, or an agent fleet running continuously.
- You want a single dedicated machine with root access under your own SSH keys, and an operator that states in writing it does not read your instance's content, rather than trusting a shared API by policy alone.
FAQ
Can I run Claude on a DGX Spark?
No. Claude is a closed model family and runs only on Anthropic's own infrastructure and its cloud partners. A Spark runs open-weight models: gpt-oss-120b, Llama 3.3, Qwen3-Coder, DeepSeek, and similar. If your task requires Claude specifically, the Anthropic API is the only place to get it, GPUwerk cannot offer Claude on a Spark under any arrangement.
Does Anthropic train on my API data?
Anthropic publishes its own privacy and data usage terms for the API, and its general public position is that it does not train on customer API data by default. GPUwerk has not independently reviewed Anthropic's current, complete API terms closely enough to state that as settled fact on your behalf, read Anthropic's own privacy and trust documentation, or your organisation's specific agreement with Anthropic, before relying on it for a compliance decision. On a Spark, GPUwerk's published DPA states GPUwerk hosts the machine but does not access, read, copy, index, or analyse the content of your instance, a structural fact about the product rather than a training policy.
Where does the Anthropic API process my data?
Anthropic is a US company; its API is primarily served from US and partner cloud infrastructure. Check Anthropic's own trust and compliance documentation for current details on any regional processing options. A dedicated Spark runs in one place: EU-Central, on a single machine assigned to you alone.
Is a DGX Spark cheaper than the Anthropic API?
It depends on token volume, which Claude model you'd use, and how continuously you can keep a Spark busy. In the worked example on this page, 500 million generated tokens a month costs about $127 in Spark compute at $0.79/hour, assuming the node sustains 256 concurrent requests the entire time. The Anthropic API's per-token price for the same volume depends on the model tier, and GPUwerk did not fetch a specific current figure for this page, check anthropic.com/pricing for your own model and volume.
Does Anthropic offer a Data Processing Addendum?
Anthropic publishes commercial terms and a DPA for API customers on its own site. GPUwerk has not reviewed the current version closely enough to characterise its specific clauses here, go to Anthropic's own legal and trust pages for the governing document. GPUwerk's own DPA for a Spark is published free at /legal/dpa and does not require a sales conversation to obtain.
Can I switch between the Anthropic API and a Spark without rewriting my application?
Partially. Anthropic's Messages API has its own request and response shape, distinct from the OpenAI-style chat completions format a Spark serves through vLLM. Client code written directly against the Messages API needs an adapter or a gateway; put LiteLLM in front of both endpoints and it normalises the difference so your application code can call either without knowing which one is behind it. Prompts tuned to Claude's specific behaviour and output style do not port regardless.
GPUwerk did not fetch a current per-token Anthropic API price for this page; prices vary by model tier and change often, check anthropic.com/pricing directly. The data-usage and DPA claims above note the existence of Anthropic's own published terms rather than asserting their specific current content, GPUwerk has not reviewed those terms closely enough to restate them as verified fact. Anthropic does not publish a general tokens/second figure for the API anywhere GPUwerk could find; that row above reflects that gap rather than a guess.