Namespaces, Collections, and Partitions in AI Databases
Namespaces, collections, and partitions are all ways to organize data in AI databases, but they operate at different…
Read moreKnowledge Base
Learn
Structured articles covering everything from core concepts to production operations.
Tenant Isolation: Logical vs Physical in AI Databases
Tenant isolation is the set of boundaries that keeps one customer, team, application, or workload from accessing or…
Read more
Multi-Tenancy Architecture Patterns for AI Databases
Multi-tenancy in an AI database means designing one retrieval system to safely serve many tenants, such as customers,…
Read more
Replication and High Availability in AI Databases
Replication helps an AI database stay available, handle more read traffic, and recover from node failures by keeping…
Read more
How Vector Databases Scale: Sharding
Vector databases scale by splitting large collections of embeddings into smaller pieces called shards, placing those shards across…
Read more
Hybrid Indexing Explained
Hybrid indexing means maintaining more than one index over the same underlying data so an AI database can…
Read more
Hybrid Search Architecture
Hybrid search architecture combines keyword search and vector search so an AI application can retrieve results that match…
Read more
Pre-Filtering vs Post-Filtering vs In-Graph Filtering in Vector Search
Pre-filtering, post-filtering, and in-graph filtering are three ways to combine vector similarity search with metadata constraints. Pre-filtering applies…
Read more
Metadata Filtering Architecture in AI Databases
Metadata filtering lets an AI database search only the vectors that satisfy structured conditions, such as tenant, document…
Read more
Metadata and Payloads in Vector Databases
Metadata, sometimes called payload data, is the structured information stored alongside a vector so an AI application can…
Read more