Hello Databricks Community,

I am exploring the configuration and scaling of high QPS (Queries Per Second) endpoints for Databricks AI Search, as described in the official documentation. I have several questions and would appreciate insights from anyone who has experience with high-throughput search workloads:

1. What is the maximum QPS supported for AI Search endpoints, and how can I request higher QPS for my application?
2. How does Databricks provision infrastructure to meet the target QPS, and are there any guarantees for sustained throughput?
3. What factors influence the QPS limits for a given AI Search index (e.g., index size, data type, concurrency)?
4. How can I monitor and troubleshoot QPS-related issues, such as 429 (Too Many Requests) errors or latency degradation?
5. Are there recommended best practices for scaling AI Search endpoints to support real-time applications with high QPS requirements?
6. Can I dynamically adjust the target QPS after an endpoint is created, and what is the impact on performance and cost?
7. What are the differences between standard and high QPS endpoints, and how do I choose the right configuration for my workload?
8. Is there a way to estimate the required QPS for my application based on expected traffic and query complexity?
9. How does QPS scaling interact with index updates or sync operations—are there any limitations or considerations?
10. Are there any additional costs associated with requesting or maintaining high QPS endpoints?

If you have practical experience, tips, or documentation links, please share! I believe this discussion will help others planning to scale their AI Search workloads.

Thank you!