On-premise AI,
inside your own walls.
AI that runs on infrastructure you own and operate. No shared tenancy, no egress, no dependency on somebody else’s availability — and no data leaving the building.
What on-premise AI means
On-premise AI is the deployment of models, retrieval and agent runtimes onto hardware inside your own data centre or private cloud, rather than onto a managed service. You own the machines, the network path and the storage; the system is reachable only from inside your perimeter.
It is not the same as “hosted in your region”. Regional hosting still puts your data on a provider’s tenancy under their operational control. On-premise puts it on yours.
Deployment targets
- On-premises
- Your own data centre. Full control of hardware, network and physical access.
- Private cloud
- A dedicated tenancy in AWS, Azure or Google Cloud — your VPC, your keys, no shared inference.
- Air-gapped
- No outbound network path at all. Used where regulation or classification forbids any external connection.
- Hybrid
- Sensitive inference inside, non-sensitive workloads outside, with the boundary enforced in the architecture rather than by convention.
The infrastructure question
The practical blocker for on-premise AI is rarely the software. It is GPU capacity, and whether the workload justifies it. A retrieval-heavy system with a modest model has very different requirements from one running a large model at high concurrency.
We size that during discovery: which models the workload actually needs, what memory that implies, how much concurrency you are planning for, and whether existing hardware covers it. The answer is sometimes that a private cloud tenancy is the better first step, and we will say so.
Cloud or on-premise?
Cloud AI is faster to start and cheaper to trial. On-premise wins on three axes: data residency that survives an audit, inference cost that does not scale with success, and independence from a vendor’s pricing and availability decisions.
For regulated Indian enterprises there is a fourth. RBI data localization and the DPDP Act place requirements on where regulated data lives and who can reach it; an architecture where the data never leaves is a far shorter compliance conversation than one relying on contractual assurances.
Frequently asked questions
Is on-premise AI slower than cloud AI?
Not inherently. Latency is usually better, because inference happens on your own network instead of over the public internet. Throughput depends on how the hardware is sized, which is a design decision rather than a property of on-premise deployment.
What happens when a model needs updating?
Models are containerised and orchestrated on Kubernetes, so an update is a versioned deployment with a rollback path. Nothing requires an outbound connection at run time.
Can you deploy into an existing Kubernetes cluster?
Yes, where it has the capacity and the isolation the workload needs. Where it does not, we say what is missing rather than deploying into something unsuitable.
Do you offer on-premise AI solutions in India?
Yes. We are based in Hyderabad and deploy into Indian enterprise environments, including air-gapped ones.
Where this connects
See a private agent
run inside a perimeter.
A 30-minute walkthrough of an agent planning, retrieving, acting, and verifying — all on infrastructure that never touches the public cloud — followed by the questions that determine whether it fits your environment.
- Live, not slides
- Your workflow, if you bring one
- No unpaid proof of concept