Private LLM hosting vs Replicate
We rent DGX Sparks, so weigh that against everything below. Replicate's value proposition is breadth: thousands of models published by their original authors, from LLMs to image, audio, and video models, each callable through the same API with no packaging work on your end. That's genuinely useful for prototyping or for products that need to call several different kinds of models without becoming an MLOps team. A dedicated Spark doesn't try to compete on breadth. It runs the one model, or handful of models, you've chosen, on hardware only you use, which is a narrower but more controlled setup.
Side by side
| Replicate | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| Tenancy | Shared, multi-tenant infrastructure that Replicate operates, scaling per-model containers up and down as requests arrive. | Single-tenant: one physical Spark, one SSH key set, nobody else's workload runs on it. |
| Model breadth | Thousands of published models across LLMs, image generation, audio, and video, each callable immediately with no setup, including community fine-tunes. | Any open-weight LLM you choose that fits in 128GB unified memory: gpt-oss-120b, Llama 3.3 70B, Qwen3-Coder 30B, DeepSeek, and others, but only what you download and configure, and only LLMs at meaningful scale. |
| Who runs the model code | The model's original author packages the inference code; for community-published models, you're trusting their code as well as Replicate's platform to run it correctly and safely. | You: whatever serving stack and model version you deploy is what runs, with no third-party model code in the request path unless you choose to add it. |
| Who can see your requests | Replicate, as platform operator. GPUwerk did not fetch Replicate's current data-usage and retention policy for this page; check Replicate's own privacy policy and terms for what is logged and by whom, including any model-author visibility for community models. | 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." |
| Cold starts and latency | Per-model containers can scale to zero between requests; GPUwerk did not fetch Replicate's current cold-start figures, check Replicate's own documentation for typical latency on a given model. | Always-on for the duration you're renting it: no cold start once the container you deployed is running. Measured throughput on our benchmarks page. |
| Pricing model | Per second of compute time used, varying by hardware tier and model. Check Replicate's own pricing page for current rates, GPUwerk did not fetch a current figure to avoid quoting a stale price. | Per hour, billed per minute: $0.79/hour on-demand, $0.59/hour for a customer-requested stop. One rate regardless of model or request volume, per pricing. |
| What you operate yourself | Nothing at the infrastructure or packaging layer for existing published models; Replicate manages scaling and serving. You manage your own model if you publish one privately. | 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. |
Where the honest difference actually is
Replicate solves a real problem: getting a huge range of models running behind a consistent API without becoming the person who packages and serves each one. For teams building products that call several kinds of models, an LLM here, an image model there, that convenience is hard to replicate with dedicated hardware, and a Spark, which runs LLMs well but isn't built as a general model marketplace, doesn't try to compete there.
The trade shows up in two places. First, tenancy: Replicate's infrastructure is shared and scaled per model across its whole customer base, the standard shape for a marketplace. Second, and specific to Replicate, is that many models on the platform are published by third-party authors, so calling one means trusting that author's packaging code in addition to Replicate's platform, a layer of trust that doesn't exist when you're running a model you selected and deployed yourself. A dedicated Spark removes both: single-tenant hardware, and only the serving code you chose to run.
Whether that's worth the loss of breadth depends on what you're building. A product that needs to call dozens of different specialized models genuinely benefits from a marketplace. A product built around one or two LLMs, where data handling and code provenance matter more than variety, is closer to what a Spark is for.
The worked cost example
Take a workload generating 500 million output tokens a month from a single LLM.
Replicate. GPUwerk did not fetch current per-second compute pricing for a specific model and hardware tier at the time of writing, and rates vary by GPU class and model. Run your own volume and model choice through Replicate's pricing page for a number you can trust.
Dedicated Spark. gpt-oss-120b reaches 862.8 tok/s aggregate at 256 concurrent requests, per the concurrency benchmark on our benchmarks page. That's 862.8 × 3,600 = 3,106,080 tokens per hour if the node stays saturated at that concurrency throughout. 500,000,000 ÷ 3,106,080 ≈ 161 hours. At $0.79/hour, 161 × $0.79 = $127.19, before tax.
That figure assumes constant saturation for the full 161 hours. Replicate's per-second billing means you pay only while a request is actually running, which can be cheaper at low or spiky volume; a steadily busy single-model workload is closer to what an hourly-billed dedicated machine is built for.
Migration path
Replicate's API is model-specific rather than a single unified chat completions shape, so migrating a call to a Spark serving a model through vLLM means adapting the request format, not just swapping a base URL, unless the specific model you're calling on Replicate already exposes an OpenAI-compatible interface. Community fine-tunes on Replicate that aren't openly available elsewhere may not have a direct equivalent to move to at all.
When Replicate is the right choice
- Your product calls a variety of model types, not just LLMs, and you don't want to operate infrastructure for each one.
- You want access to community fine-tunes and specialized models you couldn't easily source or run yourself.
- Usage is bursty enough that per-second billing beats paying for a machine around the clock.
When a dedicated Spark is the right choice
- You've settled on one or two LLMs and don't need marketplace breadth across model types.
- You want to know exactly what code is running your model, without a third-party author's packaging in the path.
- Your traffic is steady enough to keep the machine busy for most of the hours you're paying for.
FAQ
Is Replicate multi-tenant?
Replicate runs published models on shared GPU infrastructure it operates, scaling containers up and down per model as requests come in. That's the standard shape of a model marketplace, and it's different from a dedicated machine running only your workload.
Does Replicate see the inputs I send to models?
GPUwerk did not fetch Replicate's current data-retention and privacy policy for this page. Check Replicate's own privacy policy and terms for what is logged, for how long, and who can access request content, including for community-published models where the model author may have their own handling of inputs.
Is a Spark more private than Replicate?
On the question of who else touches a given request, yes: a Spark is one dedicated machine under your own SSH keys, and GPUwerk's terms state GPUwerk does not access, read, copy, index, or analyse instance content. Replicate runs models on shared infrastructure it operates, and for community-published models, the model's own code runs as part of serving your request, a code-trust question a dedicated Spark running a model you chose and configured yourself does not have.
Can I run the same breadth of models on a Spark?
Not with the same convenience. Replicate's value is that thousands of models, including fine-tunes, image and audio models alongside LLMs, are already packaged and callable. A Spark can run any open-weight LLM that fits in 128GB unified memory, but you find, download, and configure it yourself; Replicate's breadth across model types generally, not just LLMs, is not something a single Spark replicates.
GPUwerk did not fetch Replicate's current pricing, data-usage policy, or infrastructure documentation for this page; every Replicate-specific claim above is either general public knowledge about the product's shape or explicitly hedged. Check replicate.com directly for current terms. Spark figures come from our benchmarks page and our pricing page.