Private AI Setup Services: What They Cover and Cost

Private AI setup services put AI models where you control the data: your own servers, your cloud account or a dedicated managed environment. A good project covers model choice, GPU sizing, a serving stack, access control, logging, and testing on your own tasks. The directory is open for listings and names no provider yet, so you can request quotes through one brief.

What "private" can mean

Four different arrangements all get called private. They differ in who holds the data and who does the work.

Cohere, for example, sells private deployment through your virtual private cloud, on premises, or in a dedicated managed environment called Model Vault. Fireworks AI states that it has zero data retention by default for open models. Both are different points on the same line. For the host side see Cohere and Fireworks AI, and for the wider picture private AI for teams, self-hosted AI and on-premise AI.

Be clear about the reason you want private. "Our data is sensitive" can be met by several of these. "Our regulator requires data in our building" can only be met by the first. "We want to avoid per-token fees" is a cost question, and the answer depends on volume.

What a good engagement includes

Model selection is a test, not a vote. Open models differ in quality, size and licence, and the leaderboards do not know your tasks. A provider should run your cases through two or three candidates and show you the results. Our lists help you start: open source LLM, best open source LLM for coding, and the model entries such as Qwen, DeepSeek, Mistral and gpt-oss.

Read the licence. Open weights are not all alike. Apache 2.0 and MIT are permissive. Mistral's Large 4 uses a modified MIT licence that requires a separate commercial licence for anyone running a Model-as-a-Service business. Meta's Muse Spark Open asks every product that uses it to show "Built with Muse Spark Open". Cohere's older Command A weights are non-commercial only (CC-BY-NC 4.0), while Command A+ is Apache 2.0. A provider who has not read the licence of the model they propose has skipped a legal step. See Meta Llama API and Mistral AI for the clauses.

Sizing: how a provider should show their working

GPU sizing is where proposals are most often hand-waved. Ask for the arithmetic. Three public examples show the scale.

Those are minimums for holding the weights, not for serving many users. Serving needs memory for the context cache, and throughput depends on concurrency. A sizing note should state the model, the quantisation, the context length, the expected number of simultaneous users, and the throughput target, and then show how the hardware meets them. If a provider says "one server is plenty" without those inputs, ask for them.

What drives the cost

We quote no market rates. The drivers are these, and you can estimate the first from public prices.

For the GPU part, use published hourly rates. As checked on 2026-10-31, Runpod lists an H100 SXM at $2.69 an hour in its Community cloud and $3.99 in its Secure cloud, and a B200 at $5.98 and $7.99. Lambda lists one H100 SXM at $4.29 an hour, and Modal about $3.95. A server left on for a 730-hour month at $3.99 an hour is $2,912.70 for one GPU, before any engineering. That arithmetic is ours, and it shows why a rented GPU that sits idle is the most common waste. Compare it with the per-token price of a hosted open model, for example gpt-oss-120b at $0.15 in and $0.60 out per million tokens on Groq. At those prices, $2,912.70 buys about 19 billion input tokens, or about 4.9 billion output tokens, so a rented GPU only wins when your volume is high and steady, or when data rules leave no choice. See Runpod, Modal and Lambda Cloud, and the comparison cloud vs self-hosted AI.

A phased path that limits risk

You do not have to commit to hardware on day one. A staged project lets the evidence decide, and a good provider will propose something like this.

Stage one is where many projects end. If a zero-retention hosted API passes your security review, you have your answer at a fraction of the effort. If it does not, you have a test set and a model shortlist for stage two, which saves the provider's time and your money.

Security design for a private deployment

Running the model yourself moves the security job to you. A provider should design these, and you should ask to see each one.

Model provenance deserves a note. Several large model repositories are gated and need approval before download, and a build that pulls from a public hub on every restart will fail or fetch something unexpected. Keep a verified copy of the weights in your own storage once the licence is accepted.

Where private AI disappoints

Honest providers will tell you where the trade-offs bite. We would raise four points ourselves.

1. Quality. An open model that fits your hardware is often smaller than the hosted model you compared against. Test on your tasks, and accept a different tool for a different job if needed.
2. Operations. Someone has to own updates and outages. A private system with no owner decays.
3. Cost at low volume. A GPU bills while idle. Below steady, high usage, per-token pricing wins.
4. Speed of change. New models arrive monthly. A private stack should make swapping a model a routine task, not a project.

None of these is a reason to avoid private AI. They are reasons to size the project honestly and to start with the light option.

How to judge a provider

We name no provider and rank none. Use this on anyone.

The honesty row matters most. A provider who sells private AI to every client is selling a product, not advice. For many teams a hosted open-model API with zero retention, or a vendor's managed vault, is private enough and far less work.

Red flags

1. No sizing evidence. A hardware quote with no model, context or concurrency numbers.
2. "Unlimited" claims. A server has limits, and a provider should state them.
3. Skipped licence review. A legal problem waiting to happen.
4. No monitoring. You will learn about failures from users.
5. No update plan. Models, drivers and serving software all change, and security patches matter.
6. Shared credentials. One login for everyone is neither private nor auditable.
7. No quality test. A smaller private model can be worse than the hosted one you left, and only your tests will show it.
8. A fixed hardware purchase before testing. Rent first, buy later.

Questions to ask

How to request quotes here

The listings under /services/ are open, and no provider is listed yet. When providers join, paid placement will be labelled and will not change ratings or order.

1. Write a brief: the data classes, the users, the tasks, peak load, regulatory rules and where it must run.
2. State your constraint: "data stays in our building" and "avoid per-token fees" lead to different answers.
3. Send it once through get quotes. The plan is to match a brief to up to three providers.
4. Compare sizing, testing and operations first, and price last.

Providers can see claim your listing and plans and sponsored slots.

Our take

Private AI is a family of options, and the first job of any provider is to find the lightest one that meets your rule. The model quality gap between open and closed has narrowed in some areas, and our model pages show where, but the hardware and the operations are real and recurring work. Judge a provider on whether they show their sizing, test on your tasks, read the licence and plan for the day after launch.

We have not tested or ranked any provider. Hardware figures come from the model cards and price pages named above, as read on 2026-10-31, and the arithmetic is shown.

Related pages

Get quotes, private AI for teams, GPU requirements for LLMs, vLLM, Ollama.