High availability architecture has supported vector search testing and automated growth
What is our primary use case?
My main use case for TiDB Cloud is that I have used it for testing the vector search for my internal testing purposes, and I have also checked the high availability and auto scale features in TiDB Cloud.
In my internal testing, I have a scenario where I need to build a small application with vector search, and for that, I have deployed TiDB in the cloud using the vector search feature by feeding some text or content and training the model to fetch data with that vector search index.
Regarding my main use case, I find that although it is somewhat costly, TiDB Cloud is best for high availability use cases because we do not need to add the number of PD nodes or TiFlash nodes; it scales up according to our workload, and we do not need to worry about server crashes or resource over-utilization problems as it automatically scales up and down based on our workloads.
What is most valuable?
TiDB Cloud is best for high availability use cases, and we can also explore more AI features including vector search.
I would like to add that we can enhance security using TiDB Cloud with private and public endpoints, ensuring that our data remains secure, especially for high-security use cases involving very confidential data, making this feature one of the best.
TiDB Cloud has positively impacted my organization because using the vector search feature has made me explore more about it in TiDB, and I have tried to implement that in our organization, and I am currently working on it.
I have explored a feature such as text-to-query, which allows us to avoid writing raw queries; we can just input our own sentence, and the text-to-query feature converts the text into a query which can then be used in the database.
What needs improvement?
While TiDB Cloud is good for high availability, I think there are some bugs when using the TiFlash feature, sometimes in the TiKV component as well. In TiFlash, I have discovered that it is not suitable for high concurrency since increasing the query concurrency causes TiFlash to overload, sometimes resulting in query failures, while TiKV handles concurrency better but is more focused on row-based data for smaller amounts of data. It would be great if these issues in TiFlash with high concurrency are effectively managed.
According to user experience, I find TiDB Cloud to be more useful for bigger queries, but I also notice complications occurring at times.
When going through the logs, I sometimes find that there are no details regarding errors. I have noticed that while the TiDB documentation lists some error codes to troubleshoot issues, it lacks workarounds or solutions for those error codes, making it not very customer-friendly. I experienced this when I faced an error and could find the error code but not the solution.
For how long have I used the solution?
I have been using TiDB Cloud for more than one year.
What do I think about the stability of the solution?
TiDB Cloud is stable based on our deployments. If it is deployed in a cross-region or uses placement rules, it remains stable due to data storage in multiple availability zones.
What do I think about the scalability of the solution?
The scalability of TiDB Cloud is automatically configured, allowing it to scale out when workloads increase and automatically scale in when workloads decrease, which means we do not need to worry.
How are customer service and support?
When discussing customer support, I find that the TiDB community is very active compared to other databases. They quickly respond to questions or feature requests, and when I submitted bug reports, the community provided solutions within three business days, which I appreciate.
Which solution did I use previously and why did I switch?
Previously, I have used self-deployed TiDB, but I find TiDB Cloud to be better than the self-deployed clusters.
I previously explored TiDB in self-deployment, which led me to evaluate TiDB Cloud as well before starting with it.
What was our ROI?
I see a return on investment with TiDB Cloud as we reduce the time spent on maintaining and monitoring since everything is automated, including the auto-scaling feature when large workloads arise. Monitoring is deployed automatically, relieving us from manual monitoring responsibilities, which is a significant part concerning ROI.
What other advice do I have?
I would rate TiDB Cloud 10 out of 8; the two points I reduced are due to cost concerns and issues related to user-friendliness.
I take off those two points mainly because of the cost and the user-friendliness issues experienced.
My advice to others considering TiDB Cloud is to keep the cost in mind. If cost is not a concern, I believe TiDB Cloud is the best option among traditional databases due to its distributed architecture which supports high availability and auto-scaling. I also think the TiFlash engine is a good choice for analytical data loads, so I strongly suggest going with TiDB Cloud.
The only additional thought I have is regarding the cost since many customers are worried about it.
I think the questions asked for the reviews are good, but it would help if you waited longer for answers, as sometimes you move to the next question too quickly.
My overall rating for TiDB Cloud is 8 out of 10.
Which deployment model are you using for this solution?
Private Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Seamless Integration, Reliable and Scalable
What do you like best about the product?
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.
What do you dislike about the product?
Monitoring and performance tracking could be more user friendly. Even though the system is strong, having simpler, more focused insights would help new startups or hackathon teams learn faster.
What problems is the product solving and how is that benefiting you?
TiDB keeps user data and recommendations up-to-date and in sync, ensuring reliable nutrition advice. It handles structured health data safely, supports growth without slowing down, and is easy to maintain, letting us focus on AI orchestration and user experience.
Reliable and Scalable Database Solution
What do you like best about the product?
I find TiDB incredibly reliable, which is crucial for ServiceBridge when dealing with real services and financial transactions through an in-app wallet. The trustworthiness of TiDB ensures that our records and processes stay intact without any system issues. I also appreciate how seamlessly it grows without needing a complete overhaul, allowing our team to focus on enhancing the user experience rather than dealing with technical challenges. Plus, setting up the database with TiDB Cloud was simple and integrated easily with our existing tools.
What do you dislike about the product?
There are areas that could be improved. While the core system is stable, getting a good grasp of how performance behaves in a distributed SQL setup can be tricky, especially for teams used to working with traditional single node databases. Also, monitoring and performance insights could be made easier for smaller teams. Having a more intuitive way to see how queries are behaving and how the system scales would make it easier to learn and use.
What problems is the product solving and how is that benefiting you?
I use TiDB for reliable transaction management, ensuring accurate data during money transfers and confirmations. It grows seamlessly, preventing redesigns and technical challenges, allowing us to focus on enhancing user experience. TiDB is trustworthy, especially for handling real services and financial operations.
Revolutionized Resume Matching with Seamless Database Integration
What do you like best about the product?
I like that TiDB has native support for vectors along with full compatibility with SQL. It allows us to seamlessly use semantic similarity search while managing structured candidate data and compliance processes. Its built-in vector support means we can store embeddings and conduct cosine similarity searches directly with relational data without needing to maintain separate systems. I also value TiDB's scalability and serverless approach, which helps us handle an increasing number of resume uploads and recruiter searches without infrastructure setup or maintenance.
What do you dislike about the product?
One area that could use improvement is the need for more guidance and examples that specifically focus on optimizing vector search at a large scale. Although TiDB's built-in vector search support functions well, adjusting the performance of similarity searches and choosing the right indexing methods required some trial and error during development. More hands-on documentation that's relevant to real-world applications, like resume matching or recommendation systems, would help teams learn and apply best practices more quickly.
What problems is the product solving and how is that benefiting you?
TiDB solves the challenge of merging semantic search with structured hiring by supporting both relational data and vector searches in one scalable solution. I like its scalability, serverless approach, and SQL compatibility, which help manage resume uploads and searches without extra infrastructure setup.