Hybrid search vector database - AI tools

KDB.AI is a scalable vector database designed for building production-grade AI applications, offering high uptime and low latency search capabilities.

• Freemium

Hyperspace is an elastic compatible cloud-native search database that combines vector and lexical search capabilities, delivering 10x faster performance through custom computing power.

• Freemium

SvectorDB is a cost-effective serverless vector database specifically designed for AWS that offers pay-per-use pricing and seamless scaling from prototype to production.

• Usage Based

Weaviate is an open-source vector database that enables developers to build AI-native applications with improved search capabilities, reduced hallucination, and enhanced data security. It supports hybrid search, RAG, and generative feedback loops.

• Freemium
• From 25$

Milvus is an open-source vector database designed for GenAI applications. It offers high-speed searches and scales to billions of vectors with minimal performance loss.

• Freemium

VectorDB is a lightweight Python library for efficient text storage and retrieval using vector search. Ideal for low-latency applications requiring semantic search capabilities.

• Free

CodableAI is a comprehensive vector database platform offering advanced APIs for embedding generation, vector search, and database management. It enables businesses to create real-time AI applications with capabilities for processing up to 80K tokens and managing up to 50 million vectors.

• Contact for Pricing

Zilliz Cloud is a fully-managed vector database service built on Milvus, designed for speed, scalability, and high performance in AI applications.

• Freemium
• From 99$

Chroma is an open-source database solution designed specifically for AI applications, offering integrated embeddings, vector search, document storage, and multi-modal retrieval capabilities.

• Free

InfiniFlow is a high-performance database solution designed specifically for LLM applications, offering hybrid search capabilities across dense embedding, sparse embedding, tensor, and full-text data with millisecond-level latency.

• Other