@techwith_ram: Your AI stack has a database problem You need a vector DB for embeddings. A graph DB for relationships. An application …

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Summary

HelixDB is a new open-source database built in Rust that combines vector, graph, and other data models into a single engine, backed by Y Combinator. It aims to replace separate vector, graph, and application databases for AI stacks, offering native vector search, graph traversal, and MCP support.

Your AI stack has a database problem You need a vector DB for embeddings. A graph DB for relationships. An application DB for structured data. An application layer to stitch them together. → Pinecone Standard: $70 a month → Neo4j AuraDB Professional: $65 a month → Postgres RDS: $50+ a month → Engineering time to wire it all together: priceless Three databases. Three schemas. Three failure points. And a RAG pipeline that still hallucinates because your chunks have no context about each other. Then two college dropouts in London shipped one database that replaces all three. It is called HelixDB. Built from scratch in Rust. Open-source. AGPL-3.0 licensed. Self-hosted. Backed by Y Combinator. Not three databases duct-taped together with application code. One engine where graph and vector live in the same data model. Your embedding knows its neighbors. Your neighbors know their embeddings. Everything connected, everything queryable, one system. The founders are George Curtis and Xavier Cochran. They started HelixDB in college after struggling with the complexity of graph databases. No credentials. No prior exits. They attracted developers from X and engineers at United Healthcare before they ever left campus. Then they dropped out, moved to San Francisco, and got into Y Combinator. 2 founders. 6 people. One database that replaces three. Here is what it does: → Graph + vector in a single engine: no join between two systems, ever → HelixQL, a strongly typed compiled query language — safer than Cypher, faster than Gremlin → Built-in vector search, keyword search, and graph traversal: power any RAG pipeline → Auto-embed with one function call: no pre-processing pipeline before ingestion → Built-in MCP support: your AI agents walk the graph, no query generation needed → KV, document, and relational data supported alongside graph and vector → Private-by-default: authenticated query access out of the box → SDKs in TypeScript, Python, and Go: one install, one client → Helix Enterprise on S3-compatible object storage — stateless nodes, horizontal scale ~4,000 stars. AGPL-3.0 licensed. Billions of queries executed. Y Combinator W25. Generally available as of 2026. Used by indie hackers and Fortune 500 teams.
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