TiDB for AI Memory: Vector Search, HTAP, and Horizontal Scaling in One Database

Most AI applications today run on a fragmented stack. Pinecone stores your vectors. PostgreSQL holds your user data. Redis caches frequently accessed information. Snowflake runs your analytics. You write synchronization logic, debug consistency issues, and pay for data transfer between systems. There is a different approach. TiDB consolidates this entire stack into one database that scales horizontally. This is not theoretical. This article shows you exactly how to build production-grade AI memo