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.

Modern applications evolve continuously, but database platforms primarily react to usage rather than understanding application intent, evolution, and decision-making context.

Today, MongoDB Atlas provides excellent observability and scalability, but developers still rely heavily on experience, guesswork, and post-incident learning to make critical production decisions—such as schema changes, scaling strategies, workload isolation, and cost optimization.

The core problem is that Atlas knows what is happening, but not why it is happening or what is likely to happen next as applications grow and change.

What would you like to see happen?

Describe the desired outcome or enhancement.

I would like Atlas to evolve from a managed database into an intelligent, context-aware platform that actively supports production decisions.

This includes capabilities such as:

• Understanding the intent behind different workloads (user-facing, analytics, background jobs)

• Treating schema evolution as a first-class lifecycle with historical awareness

• Proactively guiding architecture and scaling decisions based on real workload patterns

• Helping teams safely test resilience and understand cost impact at a feature level

The desired outcome is an Atlas experience that not only reports metrics, but helps developers confidently answer:
“Is this the right decision for my application right now and in the future?”

Why is this important to you or your team?

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

For fast-moving teams, the biggest risks are not database outages, but slow, incorrect, or delayed decisions around schema changes, scaling, and cost management.

Having Atlas act as a trusted decision-support layer would:

• Reduce production risk during growth

• Shorten the learning curve for less-experienced teams

• Enable safer experimentation and faster iteration

• Directly connect database behavior to product and business outcomes

This is especially valuable for startups, student-led projects, and small teams where deep database expertise is limited but growth pressure is high.

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

Currently, these challenges are managed manually through:

• Trial-and-error schema migrations

• Over-provisioning infrastructure to avoid risk

• External monitoring and custom dashboards

• Post-incident analysis rather than proactive prevention

• Relying on prior experience instead of platform guidance

While workable, these approaches are inefficient, reactive, and difficult to scale consistently across teams.