Embedding stores are databases optimized for storing, indexing, and retrieving high-dimensional embedding vectors efficiently. Purpose: Store embeddings from ML models (text, images, users), enable fast similarity search, power retrieval and recommendation. Core operations: Insert: Add embedding with metadata and ID. Search: Find k nearest neighbors to query embedding. Update/Delete: Manage embeddings over time. Relation to vector databases: Often synonymous. Embedding store emphasizes ML workflow, vector DB emphasizes database features. Index structures: HNSW (graph-based), IVF (inverted file), PQ (product quantization), flat (exact but slow). Scale considerations: Billions of embeddings require distributed systems, approximate search, and careful index tuning. Filtering: Many stores support metadata filtering combined with vector search (hybrid search). Popular options: Pinecone, Weaviate, Milvus, Qdrant, Chroma, pgvector. Integration with ML: Store embeddings from CLIP, sentence transformers, or custom models. Update as models change. Use cases: Semantic search, RAG retrieval, recommendation, deduplication, clustering. Foundational for modern AI applications.