Bottom Line
Vertiva's foundation owns its own data plane, its own models, and its own retrieval — capable of ingesting millions of documents from any source, running inference and vector search at GPU scale, and deploying into shared SaaS or a customer's own enterprise cloud platform (BYOC).
Owning the Data Plane, Ingesting at Enterprise Volume
A distributed, governed pipeline (Kafka, Debezium CDC, Ray-on-Databricks, Delta Lakehouse) replaces the platform's single-node ingestion path, targeting millions of documents ingested automatically.
Owning Inference and Retrieval at GPU Scale
Dedicated GPU infrastructure, a model gateway with routing and failover, and a high-availability sharded Qdrant vector store let each customer select generation, embedding, and rerank models independently.
Every Core Service Is Held to the Same Enterprise Bar
Scalability: a horizontal scale-out path load-tested to a stated throughput target.
Resilience: graceful degradation, retries, circuit breakers, chaos-tested before shipping.
Migration safety: dual-run → backfill → parity-evaluation → cutover, always reversible.
Security & multi-tenancy: tenant isolation carried through every new component.
Cost: a cost model and per-tenant attribution for every new piece of infrastructure.
Flexible Deployment for Every Enterprise Environment
Shared multi-tenant SaaS, dedicated single-tenant, and customer-cloud (BYOC) all run through the same provisioning automation, so a deployment-model decision is a configuration choice, not an architecture change.