What problem are you trying to solve?

Focus on the what and why of the need you have, not the how you'd like it solved.

Teams struggle to proactively identify and fix performance issues in MongoDB workloads. Query inefficiencies, missing or suboptimal indexes, and slow troubleshooting often require deep manual analysis, which is time-consuming and error-prone. This leads to performance bottlenecks, higher operational overhead, and delayed issue resolution.

What would you like to see happen?

Describe the desired outcome or enhancement.

I’d like MongoDB Atlas to proactively analyze real workload behavior and surface actionable improvements—such as automatically optimized query rewrites, intelligent index recommendations, and guided support workflows that help resolve issues faster with minimal manual intervention.

Why is this important to you or your team?

Explain how the request adds value or solves a business need.

This would significantly improve developer productivity, reduce time spent on performance tuning and troubleshooting, and help teams operate databases more efficiently at scale. It also lowers the expertise barrier, enabling teams to achieve high performance without relying on deep database specialization.

What steps, if any, are you taking today to manage this problem?

Currently, we rely on manual query reviews, performance dashboards, logs, and trial-and-error indexing, along with support tickets when issues escalate. While effective, these approaches are reactive and resource-intensive, highlighting the need for more intelligent, automated assistance.