I like how well TiDB handles both structured data and vector search in one place. It makes it easy to store shelter details and vector embeddings in the same database, allowing me to do semantic matching and apply filters like capacity and distance in a single query. The integration of relational and vector data management in one system significantly simplifies development and maintenance. TiDB Serverless is also great for its scalability and ease of setup, enabling me to quickly build and test systems without handling infrastructure setup. Despite constant updates from AI agents, the performance remains steady, making it reliable for real-time situations like disaster response. Review collected by and hosted on G2.com.

One area where TiDB could improve is by offering more detailed documentation and examples focused on vector search scenarios. Although the core features worked well, I initially needed to experiment with query structures, similarity thresholds, and indexing approaches to get accurate shelter matching results. More hands-on guides or best practices for real-world AI applications would make it easier for people to adopt and use TiDB effectively. Also, having better built-in observability for vector queries, like clearer performance metrics or explanations of similarity scores, would help with tuning and troubleshooting. Since my project involved multiple AI agents and real-time updates, greater transparency into how vector queries perform would make optimization easier. Review collected by and hosted on G2.com.

I used TiDB as the main database for my AI-powered product support platform, and it did a great job of handling real-time user queries and storing data reliably. TiDB's compatibility with MySQL made integration with my Flask backend easy using standard SQL queries. I really appreciated TiDB's HTAP feature because it let me handle both transactional and analytical tasks within the same database. With TiFlash replication, I was able to run analytical queries on tasks and user data without slowing down the real-time performance of my applications. Its distributed structure made it easy to expand the system as more products, users, and documents were added. TiDB offered strong consistency and reliability, ensuring that user interactions were handled accurately, and its distributed setup helped with scalability and high availability. Review collected by and hosted on G2.com.

One challenge I faced was tuning queries for analytical workloads with TiFlash, which needed careful planning of the schema and proper indexing to achieve the best performance. Because my platform handles both transactional and analytical queries, I had to learn how replication and query execution work, which required some trial and error. Having better examples and guidelines for HTAP scenarios, especially for analytics dashboards and task monitoring, would help developers improve performance more quickly. Another challenge was keeping track of and fixing how queries performed in both transactional and analytical tasks. Even though TiDB has helpful tools, it took a while to figure out which queries were using TiKV or TiFlash and then improve their performance. Having simpler dashboards and more straightforward information about how queries run would help developers spot and fix performance problems faster. Also, more easy-to-use examples for integrating TiDB with frameworks like Flask would make it easier for new users to get started. Review collected by and hosted on G2.com.

I use TiDB Serverless as the main transactional database for my fraud detection platform because TiDB offers fast read and write operations that are crucial for real-time decision-making in fraud detection. I appreciate how TiDB helps scale real-time transaction processing without losing consistency or needing a complicated database setup, allowing us to handle transactions efficiently. I really enjoy TiDB's horizontal scalability combined with the ease of using SQL, as it allows us to manage various tasks smoothly without changing our data structure or switching to a new model. I also like the strong consistency TiDB offers, ensuring all transaction details, risk evaluations, and alert logs are saved together, which gives us trust in the system's reliability. Review collected by and hosted on G2.com.

One thing that could be better is the process of getting new terms up to speed when moving from a single node database like SQLite or regular MySQL to TiDB. Even though TiDB works with MySQL, it takes extra learning to understand how queries work in a distributed system, how to set up indexes, and how to tune performance for a distributed setup. Review collected by and hosted on G2.com.

I use TiDB as the main database for my project, Zesty, because it's reliable, scalable, and fast. I like that it handles scalability well; as the workload increases with growing user data, TiDB doesn't slow down, which is crucial for a growing AI platform. It also makes management easier by automatically dealing with complex tasks like database sharding and distribution. I really enjoy that TiDB scales easily without needing to change how my application works and handles heavy traffic and real-time data efficiently. Its strong consistency and dependable performance are also highly appreciated. TiDB feels like using a regular SQL database but runs as a strong distributed system, allowing me to use standard MySQL-like queries, which makes development feel easier and more familiar. Additionally, TiDB handles both transactional and analytical tasks without needing separate systems, which suits my needs perfectly. Review collected by and hosted on G2.com.

At first, understanding the distributed architecture took some time. More real-world examples of SaaS and AI in the documentation would be helpful. Performance tuning feels a bit complicated for beginners. Making some small changes to the onboarding process would improve the experience. It was hard to grasp how performance tuning works in a distributed system that includes TiDB, TiKV, and PD. There were a lot of metrics and dashboards, which made things a bit confusing for someone just starting out. A straightforward, step-by-step guide on performance tuning for typical SaaS or AI workloads would make it much easier for new users to get started. Review collected by and hosted on G2.com.

I like that TiDB combines strong transactional consistency with horizontal scalability while supporting analytical queries in the same system. This is especially valuable for Branchat where we manage complex, evolving conversation structures and need fast insights from historical data. TiDB's MySQL compatibility, reliability, and reduced operational overhead make it easy to adopt and maintain, allowing us to scale confidently without redesigning our architecture. The initial setup was straightforward and smooth. Its MySQL compatibility made it easy to integrate with our existing development workflow, and the documentation helped us to get up and running quickly. Review collected by and hosted on G2.com.

If there's one area for improvement, it would be simplifying advanced configuration and tuning for newer users, especially around performance optimization at scale. Some distributed system concepts have a learning curve. Review collected by and hosted on G2.com.

The simplicity of using TiDB is the best thing about it! Review collected by and hosted on G2.com.

Initially it was a bit difficult to navigate the platform and find where exactly the Database and other functionality is located. The UX can be improved by having a more refined on-board experience Review collected by and hosted on G2.com.

I have used TiDB for high-speed caching and really enjoy its RAG service using vector DB inference features. The high-speed inference and easy SQL capabilities are great. I particularly like TiDB's AI helper, which has been a huge help in writing proper schemas. I also enjoy the clustering aspect of TiDB. Everything worked well for me, and I found the initial setup to be very easy. Review collected by and hosted on G2.com.

Nothing Review collected by and hosted on G2.com.

TiDB makes it easy to scale horizontally without complex sharding. It supports both transactional and analytical workloads (HTAP) out of the box, and its MySQL compatibility means minimal changes to existing apps. The Kubernetes operator and built-in observability tools make operations smooth and efficient. Review collected by and hosted on G2.com.

TiDB has a learning curve, especially around its distributed architecture and tuning performance for complex queries. TiFlash setup and resource planning can be tricky at scale. Also, some advanced MySQL features aren’t fully supported yet, which can limit compatibility in certain edge cases. Review collected by and hosted on G2.com.

I like how easy and efficient it is, and the fact that it is MySQL-compatible which eliminates the need to learn another query language before I could use it, makes it my first-choice database.

I also love the promptness of their customer services, they're always there to support you if you run into any challenge.

Lastly, TiDB is that database that gives you assurance that your data is safe and always available up to 99.9999% uptime (I have never had any downtime in over 2 years of using it). Review collected by and hosted on G2.com.

I dislike the fact that is does not yet support of GEO spatial data type. Review collected by and hosted on G2.com.

It is free and easy to implemntation and speedy sql database Review collected by and hosted on G2.com.

I haven't found anything significant that I would consider a real dislike. as i was use multiple features in it. Review collected by and hosted on G2.com.