Vector & Memory
Databases designed to store vectors (embeddings). This is the long-term memory for RAG applications.
| Rank | Model | Price | Summary |
|---|---|---|---|
|
1
|
Usage Based | The Standard. v2.0 introduces 'Dedicated Read Nodes' for high-QPS applications and 'Namespace Schemas' that enforce metadata strictness. It remains the only true serverless vector DB that scales to billions of records with zero cold-start latency. | |
|
2
|
Open Source | The Postgres Choice. Leverages 'StreamingDiskANN' (via pgvectorscale) to outperform dedicated vector DBs. It allows you to store 50M+ vectors on a standard Postgres instance for a fraction of the cost of cloud alternatives. | |
|
3
|
Freemium | The Agent Memory. Not just a database, but a 'Memory Layer'. It intelligently manages User, Session, and Global context, automatically effectively 'forgetting' irrelevant noise while reinforcing key facts for long-running agents. | |
|
4
|
Open Source/Paid | The Data Lake. Built on the Lance file format (v2.1), it allows for 'Zero-Copy' vector search directly from S3. It is the dominant choice for multimodal AI, handling images, video, and text in a single columnar store. | |
|
5
|
Serverless | The Agentic Hub. The first vector DB to launch an official 'MCP Server', allowing Claude and other agents to natively query it without custom tool definitions. Its 'Verba' RAG engine is the gold standard for out-of-the-box performance. | |
|
6
|
Open Source | The Scale King. The v2.5 update introduces 'JSON Shredding' for high-speed metadata filtering and 'Data Clustering' for massive throughput. It is the default choice for enterprises needing to store trillions of vectors. | |
|
7
|
Open Source | The Efficiency Engine. Famous for its 'Binary Quantization' that squeezes massive indexes into tiny RAM footprints. The new 'Tiered Multitenancy' allows SaaS providers to host millions of isolated user indexes cheaply. | |
|
8
|
Enterprise | The Context Graph. Moves beyond simple similarity to 'GraphRAG', linking vectors with explicit knowledge relationships. It prevents the 'Lost in the Middle' phenomenon by understanding the *connection* between chunks, not just their distance. | |
|
9
|
Open Source | The Developer Experience. While less feature-dense than Milvus, it remains the easiest to start with. Its 'DuckDB-less' migration in late 2025 has significantly improved its stability for local-first development. | |
|
10
|
Enterprise | The Legacy Modernized. The introduction of the 'ACORN-1' algorithm provides up to 5x faster filtered vector search. It is the safe harbor for enterprises that want vector capabilities without adding a new database vendor. |
Just the Highlights
Pinecone Serverless 2.0
The Standard. v2.0 introduces 'Dedicated Read Nodes' for high-QPS applications and 'Namespace Schemas' that enforce metadata strictness. It remains the only true serverless vector DB that scales to billions of records with zero cold-start latency.
Supabase Vector (pgvectorscale)
The Postgres Choice. Leverages 'StreamingDiskANN' (via pgvectorscale) to outperform dedicated vector DBs. It allows you to store 50M+ vectors on a standard Postgres instance for a fraction of the cost of cloud alternatives.
Mem0
The Agent Memory. Not just a database, but a 'Memory Layer'. It intelligently manages User, Session, and Global context, automatically effectively 'forgetting' irrelevant noise while reinforcing key facts for long-running agents.
LanceDB Enterprise
The Data Lake. Built on the Lance file format (v2.1), it allows for 'Zero-Copy' vector search directly from S3. It is the dominant choice for multimodal AI, handling images, video, and text in a single columnar store.
Weaviate Cloud (WCD)
The Agentic Hub. The first vector DB to launch an official 'MCP Server', allowing Claude and other agents to natively query it without custom tool definitions. Its 'Verba' RAG engine is the gold standard for out-of-the-box performance.
Milvus 2.5
The Scale King. The v2.5 update introduces 'JSON Shredding' for high-speed metadata filtering and 'Data Clustering' for massive throughput. It is the default choice for enterprises needing to store trillions of vectors.
Qdrant Hybrid Cloud
The Efficiency Engine. Famous for its 'Binary Quantization' that squeezes massive indexes into tiny RAM footprints. The new 'Tiered Multitenancy' allows SaaS providers to host millions of isolated user indexes cheaply.
Neo4j GraphRAG
The Context Graph. Moves beyond simple similarity to 'GraphRAG', linking vectors with explicit knowledge relationships. It prevents the 'Lost in the Middle' phenomenon by understanding the *connection* between chunks, not just their distance.
Chroma
The Developer Experience. While less feature-dense than Milvus, it remains the easiest to start with. Its 'DuckDB-less' migration in late 2025 has significantly improved its stability for local-first development.
Elasticsearch 9 (ACORN-1)
The Legacy Modernized. The introduction of the 'ACORN-1' algorithm provides up to 5x faster filtered vector search. It is the safe harbor for enterprises that want vector capabilities without adding a new database vendor.