In the first post of this series, we introduced how Azure Advisor helps Azure Cosmos DB users uncover opportunities to optimize efficiency and make smarter decisions.

This follow-up dives deeper into one of the most important categories of guidance: performance. If you’ve ever dealt with skewed workloads, unexpected RU consumption, or queries that don’t scale as expected, Azure Advisor surfaces exactly the insights you need to diagnose and improve them.

In this post, you’ll learn what performance recommendations Advisor provides, why they matter, and how you can use them to enhance the speed, reliability, and scalability of your workloads.

Exploring Azure Advisor Performance Recommendations

Below are some of the key performance related recommendations Advisor provides for Azure Cosmos DB, along with guidance you can use immediately.

• Use hierarchical partition keys for optimal distribution: Poor partitioning is one of the most common causes of hot partitions, throttling (429 errors), and uneven RU consumption. Advisor detects when your current partition key may lead to workload imbalance and recommends improvements. By adopting hierarchical partition keys, your data is evenly distributed across physical partitions, which improves both performance and reliability.
• Optimize your Azure Cosmos DB indexing policy: Azure Advisor frequently flags containers still using the default indexing policy, which indexes every property. While convenient, default indexing increases RU costs, especially for large or write-heavy documents. Optimizing your indexing policy helps:
  • Lower RU consumption for write operations
  • Improve query performance
  • Reduce storage overhead
• Improve query efficiency with Advisor insights: Azure Advisor also surfaces opportunities to optimize costly or inefficient queries. Combined with tools like Query Advisor, developers can restructure predicates, avoid cross-partition‑partition fan‑out queries, and minimize RU consumption for large result sets.
• Monitor throughput and avoid throttling: Advisor flags performance issues caused by under‑provisioned or misconfigured throughput. Recommendations often focus on:
  • Increasing RU/s to avoid sustained 429s
  • Adjusting autoscale settings
  • Reviewing partition-level consumption metrics

Conclusion

Azure Advisor takes the guesswork out of optimizing performance in Azure Cosmos DB. By surfacing insights around partitioning, indexing, query efficiency, and RU consumption, it acts as an always‑on guide that helps you identify bottlenecks before they impact your application. Whether you’re fine‑tuning an existing workload or scaling a high‑growth system, these recommendations give you a practical path to improving throughput, reducing latency, and ensuring your app performs reliably under load.

As you continue using Azure Cosmos DB, make Azure Advisor part of your regular operational workflow. A quick review of its recommendations can reveal opportunities to boost performance and lower costs with minimal effort, and often, immediate impact.

Coming Next

In the next post in this series, we’ll move beyond performance and explore cost optimization recommendations from Azure Advisor. You’ll learn how Advisor identifies over‑provisioned workloads, unused resources, and opportunities to optimize RU consumption, and how you can turn those insights into meaningful savings without sacrificing performance.

About Azure Cosmos DB

Azure Cosmos DB is a fully managed and serverless NoSQL and vector database for modern app development, including AI applications. With its SLA-backed speed and availability as well as instant dynamic scalability, it is ideal for real-time NoSQL and MongoDB applications that require high performance and distributed computing over massive volumes of NoSQL and vector data. To stay in the loop on Azure Cosmos DB updates, follow us on X, YouTube, and LinkedIn.