Operations | Monitoring | ITSM | DevOps | Cloud

A FinOps engineer's guide to governing custom metrics

This guest blog post is authored by Dieter Matzion, a seasoned cloud practitioner who has operated exclusively in public cloud environments since 2013, with experience at leading technology companies including Google, Netflix, Intuit, and Roku. Custom metrics play a crucial role in enabling teams to monitor their applications and businesses. The flexibility of these metrics allows engineers to measure what matters most to their domain.

Turning errors into product insight: How early-stage teams can connect engineering data to user impact

Early-stage engineering teams ship fast and learn in production. While speed is a competitive advantage, it can also lead to a high volume of noisy signals, like stack traces, timeouts, and dashboards full of red. Some of those problems can affect your users and revenue, but many don’t.

Real-Time Anomaly Detection For Cloud Cost Monitoring: Why It's The Future (And How It Works)

“Every engineering decision is a cost decision,” notes Ben Johnson, co-founder and CTO of Obsidian Security. That’s the reality of building modern SaaS products in the cloud. But as Ben points out, the answer isn’t to make engineers think long and hard about every dollar they spend. “You don’t want your team hesitating to solve risky technical problems because a choice might add $100 to the bill.

Why You Need "Always-On" Website Tracking This Holiday Season

Holiday shoppers are notoriously impatient, and in 2025, they’re increasingly impatient when it comes to slow websites. Keywords like “website downtime tracking” and “ecommerce site reliability” are often trending because businesses are realizing that slow is the new down. This holiday season, the goal is to safeguard your website against business-critical slowdowns without adding “manual monitoring” to your already busy plate.

DevEx matters for coding agents, too

The speed at which you can go from making a change in your code, to understanding if it actually works, has long been a popular topic of discussion (and often, humour) for engineers. This remains true in a world with AI. Developer experience isn't just important for humans anymore. Those agents we're all using hundreds of times a day? Feedback cycles matter just as much for them, if not more.