multi tenancy

Enterprise RAG breaks on isolation, not on retrieval quality. The signal is enforcing tenant and document-level permissions server-side, at retrieval time, so the model can never surface data a user is not allowed to see. Here is the design that survives a security review.

Every RAG design says 'just filter by tenant_id.' Almost nobody can explain why that one line can quietly cut recall in half. The answer is in what the filter does to the ANN graph.

Limited GPUs, dozens of models, and every team demanding low latency for little money. The signal is whether you can shape that into a single governed serving fleet: continuous batching, KV cache, per-tenant quotas, and cost you can genuinely attribute.

Hand every team an LLM API key and your bill turns into a mystery capped by a six-figure surprise at month end. Attributing, capping, and optimizing spend is a platform feature. Here is how to build it.

Selling RAG as a product means wrangling messy ingestion, multi-tenant isolation, and per-customer index freshness, all behind a simple API. See the ingestion and query planes, how to keep tenants isolated, and how to hand customers eval and observability.

Five thousand businesses, five thousand assistants, one platform. The hard parts are tenant isolation you can defend, noisy neighbors, onboarding a tenant with zero data, and evaluating assistants you can never manually QA. Here is the control plane and the data plane.

Every AI product that runs generated code (notebooks, agents, code interpreters) ends up building this, and the interview turns on two numbers: how strong your isolation is and how fast a session starts. The answer is microVMs plus snapshot restore, and knowing why the VM boot is not the slow part.

Pinning one GPU per model leaves most of a fleet stranded on idle silicon. Sharing safely is a genuine systems problem with four distinct mechanisms, each suited to a different traffic shape. Here is how to choose.

An enterprise RAG that retrieves across everyone's documents is a breach in waiting. The fix belongs in the retrieval layer, not the prompt. Here is the design that survives a security review.

AI features create cross-tenant leak paths that classic SaaS isolation never had to reckon with: shared embeddings, shared caches, and fine-tunes that memorize. One leak ends an enterprise contract. Here is the isolation model.