Milvus
Billion-scale vector search
The top alternatives to MongoDB Atlas Vector Search in the Vector Databases space, compared on features, pricing, and what they're best at.
MongoDB Atlas Vector Search is an integrated vector search capability within MongoDB's fully managed, multi-cloud data platform. With Atlas Vector Search, users don't need to sync data between operational and vector databases—saving time, reducing complexity, and preventing errors, as operational and vector data stay in one place. Users can easily combine vector queries with filters on metadata, graph lookups, aggregation pipelines, geospatial search, and lexical search for powerful hybrid search use cases within a single database. MongoDB's distributed architecture scales vector search independently from the core database, enabling true workload isolation and optimization for vector queries, resulting in superior performance at scale. Security and high availability are built in, with vector data stored directly in Atlas alongside operational data, ensuring workloads run with enterprise-grade security and availability. Founded in 2007 (as 10gen) and headquartered in New York, MongoDB serves thousands of organizations worldwide with over 5,500 employees.
Milvus
Billion-scale vector search
Pinecone
Fully managed serverless vector database
Qdrant
High-performance open-source vector search
Chroma
Lightweight embedded vector database
Supabase
pgvector hosting
Weaviate
Open-source vector database
Neo4j
Native graph database with Cypher query language
Elasticsearch
Redis Vector
ClickHouse
Analytics + vector
TigerGraph
Neon
Serverless Postgres
Vespa
LanceDB
Turbopuffer
SingleStore
ArangoDB
One platform for routing, observability, tracing, and evals across every LLM provider.