What do you like best about the product?

Densify has brought a level of clarity and control to our cloud resource optimization that frankly feels like a superpower. Its machine learning–driven recommendations are not only accurate but also actionable, making it easy to transition from insight to execution without the usual friction. The platform excels at contextualizing optimization decisions — especially in containerized environments — and supports a proactive FinOps culture rather than a reactive one. It’s rare to find a solution that engineers actually trust and finance teams appreciate. Densify hits that sweet spot.

What do you dislike about the product?

If there’s one enhancement I’d love to see, it would be the ability to save custom account filter presets. While the current filtering system allows you to select specific accounts to view associated resources — which is definitely helpful — being able to group accounts into named sets (e.g., “Team A Accounts”, “Prod Environments”) and quickly apply those groupings as reusable filters would make the platform even more efficient for users managing complex, multi-account environments. It’s a small quality-of-life improvement, but one that would save time and reduce repetitive manual filtering.

What problems is the product solving and how is that benefiting you?

Before adopting Densify, our resource optimization process was largely manual. We used Datadog to monitor utilization, but analyzing that data and identifying the right instance types required a lot of time, context-switching, and operator judgment. It worked — but it wasn’t scalable or efficient.

Densify changed that dynamic completely. It automates the analysis, highlights optimization opportunities with clarity, and delivers data-backed recommendations we can trust. The result has been a significant reduction in manual effort, more consistent decision-making, and better visibility into where and how we can optimize.

Perhaps most importantly, Densify has helped bring structure to what was previously an ad-hoc process. We can now generate reports that clearly justify optimization decisions, making it easier to communicate with stakeholders and drive alignment around resource planning — all based on objective data, not just intuition.