Vector Database 6
All-in-one backend-as-a-service for building AI-native applications and workflows.
Powabase is an all-in-one backend-as-a-service (BaaS) platform specifically designed for AI-native applications. It integrates a dedicated Postgres database with pgvector, an out-of-the-box Retrieval-Augmented Generation (RAG) pipeline, AI agent runtimes, and drag-and-drop visual workflows into a single system. The platform is optimized for modern AI coding agents like Claude Code, Codex, and Cursor, allowing software development agencies and in-house IT teams to ship AI products or add AI automation to existing infrastructure quickly and cost-effectively without managing fragmented services.
Ragie is a managed RAG service for developers to build generative AI applications.
Ragie is a fully managed RAG-as-a-Service (Retrieval-Augmented Generation) built for developers to streamline the ingestion, chunking, and multimodal indexing of structured and unstructured data. It offers simple APIs and SDKs, seamless integration with data sources like Google Drive, Notion, and Confluence, and built-in advanced features like summary indexing, chunk reranking, and flexible vector filtering to help applications deliver state-of-the-art generative AI.
Fully managed, scalable vector database for enterprise AI applications.
Zilliz provides a fully managed vector database management system, Zilliz Cloud, powered by the open-source Milvus. It is designed for enterprise-grade AI applications, supporting billion-scale vector search, Retrieval Augmented Generation (RAG), and large language models. Zilliz aims to simplify the deployment and scaling of vector search applications by eliminating the need for complex infrastructure management.
Open-source RAG infrastructure for building production-ready AI chat and search applications.
Agentset is an open-source RAG (Retrieval-Augmented Generation) infrastructure designed to help developers build production-grade AI chat and search applications. It handles the complex aspects of RAG, including document extraction, chunking, hybrid search, and agentic reasoning, ensuring that AI apps remain reliable even with large-scale data and high user traffic. The platform is model-agnostic, allowing users to choose their own LLMs, vector databases, and embedding models. It supports over 22 file formats and multimodal data like images and tables, providing cited answers out of the box for sectors like medical AI, legal tech, and enterprise search.
AI-powered tool to find Edinburgh Fringe shows using text queries and summaries.
FringeSift is a tool that utilizes ChatGPT and Vector Databases to help users find Edinburgh Fringe shows in 2023. It allows users to search for shows using text queries and provides summaries of the results using ChatGPT.
MLOps platform as a PostgreSQL extension for building ML models inside the database.
PostgresML is a complete MLOps platform implemented as a PostgreSQL extension. It allows users to build fast, simple, and powerful machine learning models directly within their database. It offers GPU-powered Postgres databases for building AI applications, enabling tasks like vector embedding, real-time output generation, and more. PostgresML simplifies the AI stack by integrating various components like vector databases, embedding models, and LLMs into a single system.
