Operations | Monitoring | ITSM | DevOps | Cloud

JC, the Director of Software Engineering, Cloud at LogicMonitor, shares how Cost Optimization enabled his team to shift to Cost-Intelligent Observability and tackle an unexpected and growing cloud bill. As engineers, we live and breathe performance. We obsess over latency, reliability, and uptime, the hallmarks of a healthy system. But there’s another metric that’s becoming just as critical: cost.

Back in May of 2021, containers had already won. Kubernetes adoption was surging. ECS and EKS were powerful. But for many teams, deploying a simple containerized web service still meant stitching together clusters, networking rules, scaling policies, load balancers, IAM roles, and CI/CD pipelines. It felt heavier than it should. Developers no longer wanted more orchestration power. They wanted less operational drag.

To watch the full walkthrough video on the Pepperdata self-service install, click the link here. Many organizations struggle to efficiently manage their cloud costs, and that arises from difficulties in managing Kubernetes resources. Of the $419 billion spent on cloud infrastructure in 2025 (Synergy Research Group), Flexera estimates that 27% of all of cloud spend is wasted due to overprovisioned resources.

Stop overpaying for Kubernetes observability. Learn how in-cluster monitoring and AI-driven troubleshooting with Qovery Observe can eliminate APM ingestion fees, reduce SRE bottlenecks, and make your cloud costs predictable.

Cloud adoption was supposed to simplify operations. Infrastructure would become programmable, scalability would become elastic, and distributed architectures would enable resilience at global scale. In practice, cloud has delivered extraordinary flexibility, but it has also introduced a level of operational complexity that traditional observability approaches were never designed to handle.

At a certain point, faster delivery stops feeling like progress and starts feeling like risk. When engineering teams scale from 10 to 50+ developers, the volume of infrastructure changes, database schemas, environment variables, and networking rules, no longer grows linearly. It scales exponentially. This is the scaling inflection point where manual governance breaks.

In today's cloud-native environments, managing resource waste and optimizing costs can feel like a constant battle. Operators, along with their fearless FinOps teams, spend countless hours hunting down unused resources, deciphering complex telemetry data, and manually implementing code or configuration changes to try to reduce cloud costs. But what if you could automate the entire process, from identifying waste to implementing the fix, all based on actual production telemetry?

Like most SaaS companies, CloudZero uses its own product. When we released cost per customer reporting, we tested it on ourselves first. And today, we use cost per customer reports regularly. Why? Because they help leadership answer board and renewal questions, including customer-level margins. Cost per customer is valuable and hard to get right. Multi-tenant systems and Kubernetes can hide the link between shared infrastructure (like EC2) and the customers using it.

Modern FP&A tools help SaaS businesses analyze financial performance in real time, forecast accurately, and align spend with business priorities — especially cloud spend, which can quickly spiral without visibility. Choosing the right FP&A tool for your business means understanding what you actually need it to do — whether that’s cloud cost visibility, more accurate forecasting, or tighter alignment between finance and engineering.