What is a single-tenant deployment?
A single-tenant deployment is an AI system set up for one organization alone. The GPU servers, the inference server, the vector database, the application, and the logs exist only for that organization and hold only its data. It can run in the company’s data center, in a cloud account it controls, or in a dedicated environment a provider operates for that one customer. The contrast is multi-tenancy, where many customers share one platform.
Single tenancy removes a whole class of risk: there is no neighbor whose data could leak into a shared index, no shared cache, and no other customer whose traffic can slow the system down.
Single-tenant deployments in practice
A mid-sized law firm adopts an assistant for document review. Client confidentiality rules and several clients’ outside counsel guidelines prohibit mixing matter data with other firms’ data on shared systems. The firm chooses an open-weight model on two GPU servers in its own cloud account, with its own identity provider and audit logs. Role-based access control separates matters inside the deployment. FlatClaw, the open-source private AI platform from Kirk Tech Solutions, is built for this pattern, with one instance per organization on infrastructure the organization controls.
What to watch for
- Single tenancy costs more per user because nothing is shared, so it fits organizations with a regulatory or contractual need.
- Ask a provider offering dedicated instances whether the control plane, logging, and model cache are dedicated as well.
- Plan for upgrades, since a single-tenant system does not inherit fixes automatically from a shared platform.