On-Premise AI Is Not an Oxymoron

The default AI/vector stack — a managed vector database, a hosted embedding API, a separate ML platform — is cloud-native by assumption. Every piece expects data to flow out to a service. For teams under data-residency rules, that assumption is disqualifying, so they're pushed towards the false choice between compliance and capability.

Tessera ships the full hybrid retrieval stack — graph, vector, full-text, SQL, and time-series over one copy of your data — as a single self-contained binary with no JVM and no separate install. It runs entirely on your own hardware with no cloud call-home, so residency and compliance are never in question. It starts in seconds, and backing up one data folder preserves the whole database.

The part teams assume they'll have to send away — training and serving models — happens in-database on the Enterprise tier. Node classification, a message-passing GNN, link prediction, GAN-generated synthetic data, and online learning all run over the same store, so no sensitive data ever leaves the engine to reach a separate ML system. A fraud GNN can learn that “an account is risky because of the company it keeps,” backed by native lineage and the tamper-evident audit chain — explainable, auditable ML on data that never moves.

The interesting question was never “cloud or on-prem.” It was whether you could have the whole modern stack — retrieval and ML together — without the data ever leaving your walls. You can.

This is single-node, self-hosted deployment done well, not a managed cloud — Tessera's managed offering is roadmap, not shipping — and the in-database ML is Enterprise, with quality that depends on feeding it real, sufficiently large data. What it removes is the forced trade: the full hybrid-plus-ML stack, running exactly where regulated data has to stay.