CanallOn-Premise LLM Deployment.
Run private AI agents on your own hardware. Any model, hard cost ceilings, secrets your agent never sees. Self-hosted, portable, no vendor lock-in.
In plain terms: self-hosted LLM deployment on bare metal, a private cloud, or your datacenter.
Maximum flexibility — your metal, your datacenter, your rules. The agent platform runs in your environment, against your data, on the hardware you chose.
On-premise is the shape for regulated industries and data-residency requirements. Nothing leaves your walls unless you say so. Run it on bare metal, in a private cloud, or in your customer's datacenter — the platform, the agent library, and the license are identical across hosted, VPC, on-premise, and air-gapped deployments, so an upgrade is a hardware swap, not a migration.
The answer that lives in one person's head.
The canonical on-premise use case is institutional knowledge: the answer buried in a 200-page SOP, a closed channel, and one senior engineer who gets interrupted forty times a day. The knowledge is real. It just doesn't scale past the one person holding it.
The agent indexes the SOPs, the manuals, the old tickets — and answers in plain language, with a citation. When it doesn't know, it tells you who to ask instead of inventing something plausible. A sealed or local model runs this with zero egress — nothing indexed, nothing asked, nothing answered leaves your walls. The one place this works is the one place your data already lives.
No lock-in. Any model, any provider.
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Your data stays where you say
The platform installs inside your perimeter. The keys live in a broker the agent never sees. Egress is a list you approve, not a default we set.
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Any model, swapped per agent
Frontier, open-weight, on-host, or sealed — the multi-model abstraction means you change models without rewriting the agent.
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Budgets enforced before tokens spend
Hard ceilings per agent, per task, per tenant. It stops the bill before the bill stops you.
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Upgrade, don't migrate
One architecture across the lineup means moving up a tier is a hardware swap, not a revalidation project. You keep the keys, the agents, and the accumulated ontology.
Related deployment shapes.
On-premise is one of four shapes the same platform runs in. Each owns a different perimeter; pick the one that matches your risk model.
Questions we hear.
- Can we run the platform on hardware we already own?
- Yes. On-premise is the maximum-flexibility shape: bare metal, private cloud, or your datacenter. Run on hardware you supply or on a validated appliance built and warrantied by our integrator partner. The platform, agent library, and license are identical either way.
- Which models can we self-host?
- Any model — frontier, open-weight, on-host, or sealed. The multi-model abstraction layer means you swap models per agent without rewriting the agent, and you can run a sealed or local model with zero egress for your most sensitive work.
- How is data residency handled?
- On-premise keeps data, models, and tools inside your environment by construction. For regulated industries and data-residency requirements this is the shape that removes the question rather than answering it.