Vector Databases (September 2026)

1

Chroma

Top pickFree plan

trychroma.com

Best for Developers building LLM and RAG applications

Chroma is an open-source embedding database designed for building applications with large language models. It stores, indexes, and queries vector embeddings alongside associated metadata and documents, supporting similarity search for retrieval-augmented generation and semantic search use cases. Chroma can run locally for development or be deployed on a server for production workloads, with client libraries for Python and JavaScript. It integrates with common LLM and embedding frameworks. The tool suits developers and small engineering teams building AI-powered applications who want a lightweight, developer-friendly vector store without extensive infrastructure setup.

• Embedding storage and retrieval
• Similarity search
• Python and JavaScript clients

2

Deep Lake

activeloop.ai

Best for ML teams building multi-modal AI applications·Free trial

Deep Lake, developed by Activeloop, is a database designed for AI workloads that combines vector search with storage for unstructured data such as images, audio, video, and text. It supports embedding storage alongside raw data, enabling retrieval-augmented generation and multi-modal search within a single system. Deep Lake integrates with common machine learning and LLM frameworks and can be queried directly from Python. It is aimed at machine learning teams building applications that require dataset versioning, visualization, and vector retrieval combined in one platform.

• Vector search
• Multi-modal data storage
• Dataset versioning

3

Rockset

rockset.com

Best for Engineering teams building real-time search apps·Free trial

Rockset is a real-time analytics database that supports vector search alongside SQL-based queries on structured, semi-structured, and unstructured data. It uses a converged indexing approach to enable low-latency search, aggregations, and joins on continuously updating data streams from sources like Kafka, DynamoDB, and MongoDB. Vector embeddings can be combined with metadata filtering and full-text search in a single query. Rockset is suited for engineering teams building real-time applications, such as personalization, recommendation, or anomaly detection systems, that require both analytical queries and vector similarity search on fresh data.

• Real-time vector search
• SQL-based analytics queries
• Converged indexing on live data

4

Typesense

typesense.org

Best for Developers building search and RAG applications·Free trial

Typesense is an open-source search engine that supports both keyword and vector search, allowing developers to build fast, typo-tolerant search experiences with semantic capabilities. It can be self-hosted or used via a managed cloud offering, and integrates vector search alongside traditional filtering, faceting, and ranking features. Typesense is often used to power site search, product discovery, and AI-assisted retrieval-augmented generation applications. It suits engineering teams looking for a lightweight, developer-friendly alternative to larger search platforms, with straightforward APIs and client libraries for common programming languages.

• Hybrid keyword and vector search
• Typo tolerance and faceting
• Self-hosted or managed cloud

5

Elasticsearch

Free plan

elastic.co

Best for Engineering teams needing hybrid text and vector search

Elasticsearch is a distributed search and analytics engine developed by Elastic that supports vector search alongside traditional full-text and structured queries. It offers dense vector fields, approximate k-nearest neighbor (kNN) search using HNSW, and hybrid search combining lexical and semantic relevance. Available self-managed or via Elastic Cloud, it suits engineering teams building search, recommendation, or retrieval-augmented generation applications that need to combine vector similarity with existing text and metadata search infrastructure at scale.

• Approximate kNN vector search
• Hybrid lexical-semantic search
• Self-managed or cloud deployment

6

Marqo

marqo.ai

Best for Engineering teams building semantic search apps·Free trial

Marqo is an open-source vector search engine that combines embedding generation, storage, and retrieval into a single system. Rather than requiring users to generate embeddings separately before indexing, Marqo handles vectorization internally, supporting text and image inputs. It offers APIs for building semantic search, recommendation, and retrieval-augmented generation applications, and can be deployed via managed cloud hosting or self-hosted using Docker and Kubernetes. Marqo suits engineering teams building search or AI applications who want to reduce the complexity of managing separate embedding pipelines and vector storage infrastructure.

• Built-in embedding generation
• Text and image search
• Self-hosted or managed deployment

7

OpenSearch

Free plan

opensearch.org

Best for Teams needing combined search and vector retrieval

OpenSearch is an open-source search and analytics suite that includes vector database capabilities through its k-NN plugin, supporting approximate nearest neighbor search alongside traditional full-text and analytics queries. It allows teams to combine keyword, structured, and vector search within a single engine, making it suitable for building semantic search, recommendation, and retrieval-augmented generation applications. OpenSearch can be self-hosted or used via managed services from various cloud providers, and suits engineering teams already using or considering the OpenSearch/Elasticsearch ecosystem for combined search and vector workloads.

• Approximate k-NN vector search
• Hybrid keyword and vector queries
• Self-hosted or managed deployment

8

Vespa

Free plan

vespa.ai

Best for Engineering teams building large-scale search or RAG systems

Vespa is an open-source platform for building applications that require low-latency computation over large datasets, combining vector search, lexical search, and structured data queries in a single engine. It supports approximate nearest neighbor search, hybrid retrieval, and real-time ranking with machine-learned models, and can scale across clusters for high-throughput workloads. Vespa is suited to engineering teams building search, recommendation, or retrieval-augmented generation systems that need to combine vector similarity with filtering and business logic at scale.

• Hybrid vector and lexical search
• Real-time machine-learned ranking
• Distributed horizontal scaling

9

LanceDB

Free plan

lancedb.com

Best for ML and AI developers building search or RAG apps

LanceDB is an open-source vector database built for AI applications that require search over embeddings alongside structured metadata. It is based on the Lance columnar format, designed for fast random access and efficient storage of vectors, images, and other multimodal data. LanceDB can run embedded within an application or be deployed as a managed service, supporting use cases like retrieval-augmented generation, recommendation systems, and semantic search. It suits developers and ML teams building AI products who need a lightweight, serverless-friendly vector store without managing separate infrastructure.

• Vector similarity search
• Embedded or serverless deployment
• Multimodal data storage