Cortex vs
Vector Databases
Vector databases like Pinecone, Weaviate, and Qdrant store embeddings and return results by similarity. Cortex builds a knowledge graph that extracts entities, maps relationships, and discovers communities from your documents — then combines graph traversal with vector search in a single query. Vector DBs find similar text; Cortex finds connected knowledge.
What Vector Databases Do Well
Vector databases are purpose-built for one thing: storing and searching high-dimensional embeddings at scale. They excel at this.
Similarity Search
Fast approximate nearest neighbor search across billions of vectors. Optimized for low-latency retrieval at massive scale.
Horizontal Scale
Designed to distribute vector storage across clusters. Add nodes to handle more embeddings without architectural changes.
Speed
Sub-millisecond query times for simple similarity lookups. Highly optimized indexing algorithms like HNSW and IVF.
What Vector Databases Can't Do
Similarity search finds text that looks like your query. It does not understand what the text means, who it mentions, or how concepts relate to each other.
No Relationship Discovery
Vector databases store isolated points in embedding space. They cannot discover that Person A manages Project B, or that Concept X depends on Concept Y.
No Entity Extraction
A vector DB stores the embedding of a text chunk. It does not know that the chunk mentions 'Sarah Chen' (a person) or 'Project Aurora' (a project).
No Cross-Document Reasoning
Each document is a separate set of vectors. If two documents mention the same entity, the vector DB has no mechanism to connect them.
No Structural Understanding
Vector similarity is purely geometric. It cannot distinguish between organizational hierarchy, temporal sequences, or causal relationships.
What Cortex Adds
Cortex is not a vector database replacement — it is a knowledge platform that includes vector search as one component of a much richer retrieval system.
Knowledge Graph
Entities and relationships extracted automatically from every document, forming a traversable graph of organizational knowledge.
GraphRAG
Community detection and graph-based retrieval that surfaces context traditional search misses — multi-hop reasoning across entity boundaries.
Hybrid Search
Vector, keyword, and graph retrieval combined in one query with cross-encoder re-ranking. Three retrieval methods, one API call.
Entity-Aware Retrieval
Search by entity type, relationship, or attribute — not just by text similarity. Find all projects managed by a specific person, for example.
Citations
Every result includes full source attribution: document, chunk, and entity provenance. Built for AI agents that need to show their work.
API-First Platform
60+ REST API endpoints covering documents, entities, search, Q&A, collections, and administration. MCP server for direct agent integration.
Comparison Table
| Capability | Vector Databases | Cortex |
|---|---|---|
| Primary Model | Embedding vectors | Knowledge graph + embeddings |
| Search Method | Approximate nearest neighbor (ANN) | Hybrid: vector + keyword + graph traversal |
| Entity Extraction | Not included — requires external pipeline | Automatic, built into ingestion |
| Relationship Mapping | Not supported | Automatic entity relationship discovery |
| Cross-Document Reasoning | Not possible — documents are isolated vectors | Graph traversal connects entities across documents |
| Citation Support | Basic metadata return | Full source citations with entity provenance |
| API Completeness | CRUD for vectors, search queries | 60+ endpoints: documents, entities, search, Q&A, collections |
| Built For | General-purpose similarity search | AI agent memory and organizational knowledge |
Frequently Asked Questions
Common questions about Cortex vs vector databases.
You can, but you likely won't need to. Cortex includes built-in vector search as part of its hybrid retrieval system. If you already have data in Pinecone or Weaviate, Cortex can ingest your source documents and build a richer knowledge representation on top.
More than similarity search
Give your AI agents knowledge that understands relationships, not just resemblance. Try the live demo or join the waitlist.