Storing and searching embeddings for RAG, semantic search and recommendations on self-hosted Qdrant, Milvus, Weaviate or PostgreSQL with pgvector, so document-derived vectors stay in the organization's infrastructure.
Why embeddings of clinical notes and records should be treated as PHI, keeping them in a HIPAA-controlled environment, encryption and access control per collection, audit logging and deleting vectors when source records change.
Vector search over client documents, research and policies in banks and insurers: data residency, entitlement-aware filtering so users only retrieve what they may see, backup and recovery under DORA and audit trails of queries.
For vendors storing each customer's embeddings: collection, partition or tenant-level isolation, noisy neighbours, per-tenant deletion when contracts end and what customers' security reviews ask about vector data.
Semantic search over agency records and case files inside a government boundary: hosting rules, classification of embedded content, records retention for derived data and access control tied to the agency identity provider.
Running vector search with no internet: local embedding models, importing images and Helm charts, storage requirements such as block devices rather than network file systems, and version-matched snapshot restores.
Keeping embeddings of European personal and business data under EU control instead of US-hosted vector services: whether vectors count as personal data, hosting choices and documenting the embedding model's data flows.
Honoring erasure requests when personal data has been chunked and embedded: tracing vectors back to source records, deleting points and payloads, snapshots and backups that still hold them, and evidence that deletion happened.
Bringing a vector database into SOC 2 scope: authentication and per-collection permissions, TLS, audit logging, backup and recovery for availability, and monitoring evidence auditors will sample.
When to add pgvector to the PostgreSQL you already run and when to deploy a dedicated vector database: index types and dimension limits, filtering behaviour, backup and HA you already have, and the scale at which teams split out.
Planning for very large collections: sharding and replication, disk-based and quantized indexes, object storage and write-ahead log dependencies, node counts for quorum and the operations team a distributed cluster needs.
Combining BM25 keyword search with vector similarity for enterprise and product search: which engines support both natively, ranking and reranking choices, and when an existing OpenSearch cluster is the simpler path.
Requirements to agree before choosing a vector store: scale and dimensions, filtering and hybrid search, multi-tenancy, authentication and RBAC, backups and restore rules, HA, embedding pipeline integration and licence terms.
Questions for partners proposing a vector database: how they size memory and disk, secure a database that ships without authentication, handle re-embedding and upgrades, and test restores, with answers that should worry you.
Cost components of running vector search yourself against managed vector services: memory-heavy nodes, replicas, storage, re-indexing, engineering and on-call, and how vector count, dimensions and query rate drive each.
Tell us what you're working on, whether it's a deployment, an audit, a security test or a cyber range. You'll speak with an engineer who can help you scope it.