Private AI Server Planning For 2027
If you are planning an AI deployment for 2027, the decisions you make now about where the model runs will outlast the model itself. Hardware refreshes. Vendors change terms. What does not change is who holds your data.
This is a planning guide, not a product pitch. It is a list of questions to put to any vendor - including us - written so the answers are checkable rather than reassuring.
What Changes In 2027
Two things are already visible in the direction of travel: models are getting more capable per watt, and buyers are getting far more specific about where their data sits. Neither trend favours a shared, general-purpose assistant.
A 2027 deployment should therefore be judged on architecture and control, both of which are decided before a single prompt is sent.
Seven Questions To Ask Any Vendor
1. Does the model run on dedicated hardware for us, or shared infrastructure? Shared infrastructure means your data competes for the same compute and sits in the same boundary as other customers. Dedicated means it does not.
2. Where physically does processing happen, and who can access it? Get the location, the custodian, and the access list in writing. “Trust us” is not a chain of custody.
3. What happens to our data when the contract ends? The answer should be: you can export your data and documents at any time, and they are deleted on your instruction. Ask specifically what is and is not exportable - a vendor who cannot answer clearly does not own their own deployment.
4. Is our data used to train anything? Not ours, and not anyone else's. Ask for it in the contract, not the brochure.
5. What is the audit trail for agent actions? If an agent touches a system, you need to see what it did, when, and on whose authority. Anything less is unaccountable automation.
6. What does the system do when it does not know? The right answer is that it says so and points at the record it could not find. A system that always answers is a system you cannot audit.
7. What are the real power and cooling requirements? This is where the operational cost lives. Ask for the number in watts, not adjectives.
Numbers You Can Compare Against
We publish our figures because they are checkable. Our servers draw 155 to 240 watts at peak. A traditional high-density AI rack draws roughly 40 to 100 kilowatts. Our systems run in a 72°F room, inside the operating range the hardware is certified for.
Those are the figures we will stand behind in 2027 and today. If a vendor cannot give you a number, that is itself an answer.
What We Do Not Claim
We will not publish invented performance figures, fabricated competitor benchmarks, or forecast pricing for hardware that has not shipped. If you are comparing vendors in 2027, treat any such number from anyone as marketing until you can verify it.
What we will do is answer the seven questions above, in writing, for your specific deployment.
Frequently Asked Questions
What should I look for in a private AI server in 2027?
Dedicated hardware, a documented processing location, a written no-training commitment, a clear written statement of what data you can export at contract end, an audit trail for agent actions, honest uncertainty behaviour, and published power and cooling figures in watts.
How much power does a private AI server need in 2027?
Plan around the number, not the adjectives. Our servers currently draw 155 to 240 watts at peak, against roughly 40 to 100 kilowatts for a traditional high-density AI rack.
Can I move my data off a dedicated AI server if I change vendors?
Your data and documents should be exportable at any time, and it should be clear in writing exactly what you can take with you. If a vendor cannot state that plainly, do not sign.
Is a shared AI assistant adequate for business use in 2027?
It depends on what you put into it. For general drafting it is fine. For pricing, client lists, contracts, and product plans, a shared assistant means that information leaves your boundary under someone else's terms.
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