Faddom Recognized by Gartner for AI-Augmented Cost Optimization

Why AI-Augmented Cost Optimization Matters

The 2026 Gartner Hype Cycle for Strategic Cost Management explores the emerging technologies shaping the future of modern infrastructure, and one of the newest categories is AI-Augmented Cost Optimization. This category reflects a growing shift toward using AI to analyze infrastructure data, identify optimization opportunities, and help organizations reduce costs while minimizing operational risk. 

This comes at a time when hybrid environments are growing in size and complexity. Organizations are under increasing pressure to optimize infrastructure spending without disrupting critical business services. While AI can quickly identify opportunities to rightsize, consolidate, or retire resources, those recommendations are only as reliable as the infrastructure data behind them. 

AI can recommend what to optimize, but only accurate dependency data can determine whether it’s safe to do so. 

What Makes Faddom Stand Out in IT Cost Optimization

AI-powered cost optimization is only as effective as the infrastructure data behind it. Here’s why Faddom stands out:

Identify Ghost Assets Before They Drain Your Budget:  Uncover forgotten servers, abandoned virtual machines, and unused applications that continue consuming resources. Faddom identifies what can be safely retired by showing exactly what is and isn’t connected.

Rightsize Infrastructure with Dependency Context: Cost optimization isn’t just about reducing resources; it’s about doing so safely. Faddom reveals how applications, servers, and services depend on one another, helping teams rightsize infrastructure and eliminate waste without impacting critical business operations.

Integration with Your ITSM Stack: Whether moving workloads to the cloud or consolidating data centers, Faddom maps application dependencies across your hybrid environment so you can migrate the right resources, avoid unnecessary infrastructure costs, and prevent costly surprises.

How Application Dependency Mapping Empowers IT Rightsizing and Cost Optimization

Traditional cost optimization tools focus on utilization metrics, inventory reports, and cloud spend analysis. While these tools can identify where money is being spent, they rarely provide the application context needed to understand the business impact of optimization decisions. Instead of just usage data, organizations need visibility into how every application, workload, and service is connected. 

Application dependency mapping fills that gap by continuously discovering infrastructure relationships across on-premises and cloud environments. Instead of relying on static inventories or periodic assessments, IT teams gain a living map of their environment that reveals critical dependencies and exposes opportunities for rightsizing and consolidation. As AI becomes a core part of infrastructure operations, this real-time dependency data enables more intelligent recommendations that go beyond resource consumption to reflect how infrastructure actually supports the business. The result is safer migrations, more effective rightsizing, and smarter cost optimization decisions backed by operational context rather than assumptions.