Own your AI.
AI is becoming infrastructure. Renting all of it, from a handful of providers, on terms they can change overnight, is a strange way to build anything that has to last. There is another way: run it yourself.
Your data stays yours
Every prompt you send to a hosted model is data leaving your walls: customer records, contracts, source code, the half-formed idea you haven’t told anyone yet. Providers promise to handle it well, and the paid enterprise tiers often add no-retention and regional options. But promises are policies, and policies get rewritten.
When the model runs on your hardware, the question “who sees this?” has a one-word answer. No retention windows to read, no sub-processors to audit, no opt-out toggles to hope survive the next terms update.
The safest data transfer is the one that never happens.
Sovereignty and compliance
Where data is processed, and under whose jurisdiction, is no longer a footnote. Regulation such as GDPR, sector rules in health and finance, and public-sector procurement all push in the same direction: know where your data lives and who can compel access to it.
Self-hosting turns that from a legal puzzle into an architecture decision. Keep inference inside your own borders, your own network, or your own air gap, and many compliance questions get simpler to answer.
It also removes a quieter risk: lock-in. Open weights and open tooling mean you can switch models, runtimes and hardware without asking permission.
And a supplier can change. It can be acquired, raise prices, leave a region, or be cut off by export controls or sanctions. Weights you already hold keep working whatever happens to the company that made them.
Cost: rent vs. own
Per-token pricing is wonderful at the start: zero upfront, pay for what you use. It is also a meter that never stops running, priced by someone else, and it scales exactly as fast as your success.
Owned hardware flips the shape of the bill. You pay upfront, then the marginal cost of one more request trends toward the price of electricity. For steady, high-volume workloads, that is a very different spreadsheet. For small or spiky ones, the API may well win. The point is that you get to do the maths instead of accepting the invoice.
Capacity is fixed, too. There are no rate limits, quotas or slow hours when everyone else is busy, and a backlog to re-embed, classify or translate can run overnight at no extra cost per token.
Predictable beats clever: a fixed cost you control is easier to plan around than a variable one you don’t.
Nobody can deprecate you
Hosted models are moving targets. Versions get retired, behaviour shifts between releases, rate limits tighten, and outages happen on someone else’s schedule. If your product depends on a specific behaviour, you are one announcement away from a rewrite.
Weights on your disk don’t get sunset. The model you validated in March is byte-for-byte the model running in December, for as long as you want it to be. Upgrade when you choose, on your own timeline, after your own tests.
Shape it to your task
A hosted model is a product you configure. An open model is material you can work with. Fine-tune it on your own data, add adapters, constrain its output to a grammar or schema, and fix the prompts and sampling settings for good.
For a narrow, repeated job, a smaller model tuned and checked on that job can be cheaper and more reliable than a general one, and it is yours to keep improving.
Measure, don’t guess
You can’t judge what keeps changing underneath you. A pinned model, a known quantisation and fixed settings give you results you can repeat, compare and trust.
It matters later, too. When someone asks months from now why the system gave a particular answer, or an auditor wants it explained, you can rerun the same model, version and settings. With a hosted model, that exact version may no longer exist.
That is why we benchmark models you can download and run yourself, quantisation by quantisation, and show what each one needs in memory. Treat our numbers as a starting point, then measure on your own task.
When not to self-host
We’d rather you hear it from us. Self-hosting is not always the right call:
- You need the very best frontier capability. The largest hosted models can still outperform what you can run locally on hard, open-ended tasks, and they may lead on tool use, very long context and vision.
- Your usage is tiny or unpredictable. Hardware you barely use is the most expensive hardware.
- You are counting only the hardware. The real cost also includes power, cooling, space, spares and the time of the people who run it. GPUs date quickly, too: a new generation of models or chips can make last year’s purchase look weak.
- You have nobody to operate it. Running inference is real work: updates, monitoring, capacity.
- You assume local means secure. You become responsible for patching, access control, logging and defending against prompt injection. A badly protected model server can leak more than a good provider would.
- The model you can afford to run is smaller. What fits your memory is often a smaller or more heavily quantised model than the one in the headline results, and quantisation costs quality. Serving many users at once needs capacity on top of that.
- You are still exploring. Prototype on an API, then move what proves itself.
The smart default is hybrid: keep what is sensitive, steady or strategic in-house, and rent the rest on purpose.
FAQ
Can I really run a useful model on my own hardware?
Yes. Open-weight models now cover a wide range of everyday work: summarising, translating, extracting, classifying, coding help. How good depends on the task and the model, which is exactly why we publish benchmarks for models you can run yourself.
Do I need a data centre?
No. Plenty of useful models run on a single workstation GPU, and smaller ones run on a laptop. Our leaderboard shows how much memory each model needs at each context length. Start small, measure, then scale only if the numbers justify it.
Is self-hosting cheaper?
Sometimes, and it depends on volume. Steady, high-volume workloads tend to favour owned hardware; spiky or tiny workloads often favour paying per token. Do the arithmetic for your own usage instead of trusting anyone’s slogan, including ours.
Is a self-hosted model as good as ChatGPT or Claude?
Not on the hardest, most open-ended tasks: the largest hosted models still lead there. On narrower, well-defined work, such as translating, extracting or classifying, a good open model is often enough, and sometimes it is all you need. Test it on your own task before deciding.
Is self-hosted the same as secure?
No. Keeping data inside your walls removes one risk, the provider, and leaves the rest to you: patching the runtime, locking down the endpoint, controlling who can call it, and defending against prompt injection. Treat a model server like any other service that handles sensitive data.
What happens when a better model comes out?
Nothing, until you decide. A hosted model may improve under you without notice; a self-hosted one stays as it is. When a promising release appears, run it against your own tests, and switch only if it earns it. The cost is that noticing and migrating is your job.
Is it only about privacy?
No. Privacy is the headline, but there is more: the model never changes unless you change it, capacity is fixed and always there, you can tune it to your task, and no single supplier or jurisdiction can switch you off.
Can I mix self-hosted and cloud models?
Absolutely, and many teams should. Route sensitive or high-volume work to models you run, and send the rare hard problem to a frontier API. Self-hosting is a capability, not a religion.
How do I know which model to pick?
Measure on your task. Our benchmarks compare models, quantisations and runs side by side so you can start from data, not hype.