I use TiDB as a database for my real-time stock analytics platform, and it resolves quite a few problems I faced with a single large instance of Postgres. TiDB performs well under heavy load while monitoring a large number of stocks. I love the MySQL compatibility, which made the migration from Postgres to TiDB easy without needing to learn a new framework. The HTAP feature eliminates the need for dedicated ETL pipelines for analytics, and the strong consistency is crucial for accurate financial transactions. I also appreciate the separation of compute versus storage, allowing me to scale them independently as needed. The initial setup for testing the migration was easy, making the transition smoother for my team.

I use TiDB as the primary database for my LLM testing project, AIBenchFlow, because it stores large volumes of data efficiently. TiDB is excellent for scalability, performance, and consistency, which is crucial as my project runs thousands of tests concurrently. The MySQL compatibility was a huge plus since it made integration fast without needing to learn a new tech stack. I really like the distributed architecture, which makes scalability seamless, allowing TiDB to scale horizontally without much of my attention. The HTAP feature is fantastic because it lets me run transactions and analytics on the same system, eliminating the need for a separate pipeline just for analytics. The initial setup with TiDB Cloud was easy, and during the testing phase of the migration from MySQL, we were able to replicate the setup within a day.

I really appreciate how flexible TiDB is. It allows our system to grow without needing big changes to the overall structure. Even as we add new features like more agents, more analytics, and more personalization, the database doesn't slow things down or become a limiting factor. Another thing that works really well is how TiDB stays out of the way. There's no need to constantly tweak settings or worry about scaling as usage changes. This kind of easy to maintain reliability is really useful when the main focus is on AI orchestration and user experience, not on managing the database itself. The initial setup was also smooth and simple to add to our current cloud system, which makes it fit well into the larger ecosystem without causing any extra work or complexity.