10 Best Vector Databases Tools in 2026

Weaviate is an open-source vector database for AI applications. Features hybrid search, dynamic indexing, and multi-tenancy for building semantic search and RAG systems.

Qdrant is an open-source vector similarity search engine. Features horizontal scaling, filtering, and high availability for production AI applications.

Milvus stores and searches vectors at scale. Open-source vector database for AI applications—similarity search infrastructure. The performance handles scale. The open-source model provides flexibility. The integration is straightforward. AI applications needing vector search choose Milvus for scalable similarity search.

Pinecone is a managed vector database for machine learning applications. Build semantic search, recommendations, and RAG applications with high-performance similarity search.

LlamaIndex is a data framework for building LLM applications that need access to your data. Connect large language models to private data sources like documents, databases, and APIs. Build RAG applications with sophisticated retrieval strategies. Agents can query multiple data sources and take actions. Integrates with all major LLM providers. The framework that makes your AI apps actually useful by connecting them to your data.

Zilliz provides managed Milvus for vector search. Cloud vector database—similarity search without infrastructure management. The Milvus foundation is solid. The management is handled. The scaling is automatic. Teams wanting managed vector database use Zilliz for hosted Milvus.

Chroma is a vector database built for AI applications. Store embeddings, query by similarity, and power retrieval-augmented generation with a database designed for how LLMs actually work. The API is simple. Local mode requires no setup. Scaling happens when you need it. The focus is making vector search accessible. Developers building AI applications that need vector storage choose Chroma for an approachable database that handles embeddings natively.

Forefront enables developers to leverage open-source AI models by providing a platform for fine-tuning, evaluating, and deploying them. It aims to offer the control and transparency often lacking in closed-source AI solutions, allowing users to customize models with their private data for higher accuracy and specific use cases. The platform simplifies the process of managing AI data, providing a single source of truth for training, validation, and evaluation datasets. Forefront is designed for developers, researchers, startups, and enterprises, abstracting away infrastructure complexities like API servers, GPUs, and scaling. It offers serverless endpoints for inference, easy integration via API, and tools for performance validation and evaluation. Users can also export their fine-tuned models for self-hosting or deployment with other providers. The product addresses common pain points in AI development such as deprecated models, inconsistent performance, and lack of data ownership. It promotes building a "data moat" by allowing users to pipe production data into ready-to-fine-tune datasets. Forefront is private by design, ensuring no logging of requests and no use of user data for model training, with enterprise options for secure cloud deployment.

LanceDB provides vector database with serverless simplicity. Store embeddings, query by similarity—vector search that fits modern development patterns. The API is straightforward. The performance is good. The integration is simple. Developers building AI applications use LanceDB for approachable vector storage.

ShapedQL is an end-to-end relevance engine that provides real-time personalization for search, recommendation feeds, and AI agent memory. It allows users to connect their data, train machine learning models, and query text, user, or session context to retrieve relevant results in milliseconds. The platform compiles SQL queries into optimized, multi-stage ranking pipelines, enabling hybrid search, hard constraints, ML model scoring, and reordering for diversity and exploration. ShapedQL is designed for product and engineering teams looking to enhance user engagement and drive revenue through personalized experiences. It offers a three-layer architecture with a query layer for real-time retrieval and ranking, an intelligence layer for ML models and embeddings, and a data layer with over 30 connectors for batch and streaming data. The platform boasts rapid experimentation capabilities, allowing teams to deploy and test new ranking models in days rather than months, and is built for enterprise scale with high reliability and security compliance. Key use cases include personalized content feeds ("For you" feeds), hybrid search and discovery, contextual memory for AI agents, similar item recommendations, personalized email content, and AI assistant recommendations. ShapedQL aims to replace traditional document retrieval systems with an engine that treats user context as a first-class input, offering faster deployment, instant updates, and the ability to learn from behavior automatically.