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AI, decoded

Why do some enterprises need to run AI on-premise?

Because for regulated industries, the data can't leave the building. Sending prompts and documents to an outside AI provider means your sensitive data — patient records, financial data, regulated IP — crosses a boundary your compliance team can't allow. Running the models and the observability stack on-premise, behind your own firewall, keeps the data, the audit trail, and the control inside your perimeter. It costs more and is harder to operate, which is why it's a requirement for the regulated, not a default for everyone. It is also no longer limited to open models, since some providers now ship frontier models for air-gapped data centers. Poolside CTO Eiso Kant told Chain of Thought that his team focuses on bringing models safely behind customers' firewalls, where the data is, instead of sending customer data out.

· Updated · Chain of Thought

Level 3: Leading with AI · 3.2 Build, buy or rent

Enterprise AIAI Observability

  • 2 episodes
  • 2 guest clips
  • 6 glossary terms
A prompt document stopped at a brick wall before it can cross to the outside, beside the question: can this data leave? If not, the AI comes inside.

In detail

The data boundary problem

A hosted AI service works by receiving your input. For most companies that’s fine. For a hospital, a bank, or a defense contractor, it’s a non-starter: the prompt itself contains data that legally or contractually cannot leave their environment. No amount of provider security solves it, because the problem is the boundary crossing, not the provider.

What goes on-premise

It’s not just the model. To run AI behind the firewall you need the whole pipeline inside the perimeter — the model, the data it draws on, and the observability and evaluation stack that lets you trace and trust it. Observability is the part teams forget: if you can’t send traces to an outside tool, you have to monitor agents with something that also runs on-prem, or you’re flying blind in the one environment where mistakes are most costly.

The trade-offs

On-premise buys control and compliance at the price of convenience. You own the infrastructure, the scaling, the model updates, and the operational burden that a hosted provider would otherwise carry. That’s why it’s the right answer for regulated, high-sensitivity workloads and the wrong answer for a team that could simply use a hosted API. On-premise is also no longer limited to open models: Google, for one, offers Gemini on its air-gapped data-center platform. The deciding question isn’t “is on-prem better” — it’s “can this data leave.”

Why some enterprises run AI on-premise: the data can't leave Inside the company's perimeter, behind its own firewall, sit the model, the data it draws on, the observability and evaluation stack, and the audit trail. A prompt carrying patient records, financial data or regulated IP is blocked at the boundary before it can reach an outside hosted AI provider, because the problem is the boundary crossing, not the provider. Observability is the part teams forget: if traces can't leave, monitoring has to run inside too. The trade-off: you gain control and compliance, and you carry the infrastructure, scaling, model updates and operational burden a provider would. The deciding question is not whether on-prem is better but whether this data can leave. Keep the whole pipeline inside the wall For regulated data, the problem is the boundary crossing, not the provider YOUR PERIMETER · BEHIND YOUR FIREWALL The model The data it draws on Observability + evals the part teams forget The audit trail THE PROMPT ITSELF Patient records, financial data, regulated IP: data that legally or contractually can't leave. If traces can't go to an outside tool, monitoring has to run in here too, or you're flying blind where mistakes cost the most. OUTSIDE A hosted AI provider works by receiving input Fine for most companies. A non-starter for a hospital, a bank, or a defense contractor. Not "is on-prem better?" but "can this data leave?" You gain control and compliance. You carry the infrastructure, scaling, model updates and operational burden a hosted provider would. Right for the regulated, wrong as a default for everyone.
On-premise keeps the model, the data, the monitoring and the audit trail inside the perimeter. It buys control and compliance at the cost of running it all yourself. Download the image

Hear it from the guest

“So in our platform today, you can take the whole platform and deploy it on basically the platform of your choice, either on premises or the cloud of your choice, right?”
Maryam Ashoori, IBM EP 17 · around 8:10 · read the transcript
“Can you bring the model to the data instead of sending off the data to the model somewhere else?”
Eiso Kant, Poolside AI EP 23 · around 25:07 · read the transcript

Quotes lightly edited to remove filler words.

From the conversation

This explainer is drawn from these episodes — each carries its full transcript.

Concepts in this explainer

On-Premises AIModel ServingSovereign AIAudit TrailData CenterFrontier Model

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