Zilliz Cloud On-Demand Search: Pay Only for the Compute You Use

Zilliz Cloud On-Demand Search is a new feature introduced to address specific workload needs that are not effectively met by existing Dedicated or Serverless options, particularly in scenarios involving sparse and bursty analytics. The feature emerged from a case study with an autonomous-driving customer whose analytics workload, requiring vector search on a 1 billion-row collection, was inefficiently serviced by both Dedicated and Serverless clusters due to their pricing and operational models. On-Demand Search offers a solution by billing per minute of actual compute usage, loading only the necessary data for each query, and providing workload isolation through separate compute resource groups. This approach reduces costs significantly for workloads with sporadic and unpredictable access patterns, bringing the monthly bill under $500 compared to the much higher costs of the other models. However, On-Demand is not suitable for high-QPS scenarios due to its reliance on IVF indexes and potential latency in cold-query situations. It is part of the Zilliz Vector Lakebase architecture, which supports various AI workloads by enabling different compute shapes to access the same data without copying or syncing, thus optimizing resource use and cost.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.