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…
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Architecture
Storage engines, index structures, and system design.
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,…
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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…
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How Vector Databases Scale: Sharding
Vector databases scale by splitting large collections of embeddings into smaller pieces called shards, placing those shards across…
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Hybrid Indexing Explained
Hybrid indexing means maintaining more than one index over the same underlying data so an AI database can…
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Hybrid Search Architecture
Hybrid search architecture combines keyword search and vector search so an AI application can retrieve results that match…
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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…
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Metadata Filtering Architecture in AI Databases
Metadata filtering lets an AI database search only the vectors that satisfy structured conditions, such as tenant, document…
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Metadata and Payloads in Vector Databases
Metadata, sometimes called payload data, is the structured information stored alongside a vector so an AI application can…
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How Vector Storage Works Under the Hood
Vector storage works by keeping embeddings, metadata, object identifiers, and search index structures in layouts that match how…
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