This is for AI product teams who currently deploy and maintain five separate systems—Postgres, a vector DB, a graph DB, a cache, and a search engine—along with glue code, dual writes, sync drift, and cross-service joins in the app layer. NodeDB replaces all of them with one connection string, eliminating five bills, five failure modes, and the ops burden. For example, a team building a recommendation engine no longer needs to manage Postgres for users, Pinecone for embeddings, Neo4j for social graphs, Redis for sessions, and Elasticsearch for product search separately.

This is for AI product teams who currently deploy and maintain five separate systems—Postgres, a vector DB, a graph DB, a cache, and a search engine—along with glue code, dual writes, sync drift, and cross-service joins in the app layer. NodeDB replaces all of them with one connection string, eliminating five bills, five failure modes, and the ops burden. For example, a team building a recommendation engine no longer needs to manage Postgres for users, Pinecone for embeddings, Neo4j for social graphs, Redis for sessions, and Elasticsearch for product search separately.