AionDB

AionDB

Fast multimodal embedded database for RAG apps

ayoub4kk
@ayoub4kk
Published on Jun 20, 2026
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ArcadeDB
Duckdb

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About AionDB

AionDB is a fast multimodal embedded database built in Rust for AI apps, RAG systems, and backend products that need SQL, graph, and vector search in one engine. Instead of syncing data across PostgreSQL, SurrealDB, Neo4j, and a separate vector database, AionDB keeps your relational records, graph relationships, and vector embeddings in one PostgreSQL-wire database. 🧠 One engine for SQL + graph + vector 🐘 Works through PostgreSQL-compatible tooling 🔎 Built for graph-heavy and hybrid data workloads 📐 Includes vector columns, distance functions, and HNSW index DDL 💸 Free for companies under $2M annual revenue ⚡ Faster than SurrealDB on graph-heavy benchmarks In current local benchmark snapshots, AionDB is significantly faster than SurrealDB on several graph-heavy workloads: 🚀 4.8x faster on graph edge filtering AionDB handles relationship filtering much faster when querying connected data. ⚡ 2.2x faster on bidirectional graph queries Useful for workloads like recommendations, entity relationships, dependency maps, and knowledge graphs. 🔥 113x faster on multi-count graph scans AionDB is built to handle graph-shaped queries directly over structured data. This makes AionDB a strong fit for RAG apps, AI agents, knowledge graphs, search systems, analytics tools, and backend products that need more than plain SQL. Product Insights AionDB is a source-available multimodal embedded database for developers building AI, RAG, graph, vector search, and backend infrastructure. - 🧠 SQL + graph + vector in one Rust engine - 🐘 PostgreSQL wire protocol - 🔎 Graph labels and edge labels over ordinary tables - 📐 Vector search with distance functions and HNSW index DDL - ⚡ Faster than SurrealDB on selected graph-heavy benchmarks - 🐳 Docker Compose quickstart - 🛠 Embedded Rust API - 💸 Free for companies under $2M annual revenue Ideal for: developers, backend engineers, AI engineers, database builders, and technical founders who want one embedded multimodal database for RAG, graph workloads, vector search, and PostgreSQL-compatible apps.

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