BerriAI / litellm
github.com·3d ago
Developer Tools
Databricks Blog·Jul 28·5 min read
From prototype to production: High QPS for Databricks AI Search
TL;DR
Databricks AI Search now supports high queries per second (QPS) scaling, allowing users to easily provision infrastructure to meet their search needs with a single parameter. This simplifies the process of achieving production-level search performance without the complexity of managing nodes or load balancers.
✦ Why It Matters
Start using Databricks AI Search to simplify your search infrastructure management and scale efficiently today.
Key Takeaways
- Set target QPS to automate infrastructure provisioning.
- Update existing endpoints easily via SDK or API.
- Monitor scaling progress through the scaling_info field.
- Use service principal auth for optimized performance.
Full Summary
Search functionality is critical across various applications, from e-commerce to streaming services, where high query rates can significantly impact revenue. Databricks has introduced a feature that allows users to set a target QPS for their AI Search endpoints with a single parameter.
This eliminates the need for manual capacity planning, such as sizing nodes or managing replicas. Users can update existing endpoints via the SDK, REST API, or UI, and the system automatically provisions the necessary infrastructure.
The scaling process is monitored through the endpoint's scaling_info field, providing visibility into the transition state. This capability is essential for applications that require real-time search, recommendations, and entity resolution, ensuring low latency and high availability.