Operations | Monitoring | ITSM | DevOps | Cloud

When a SaaS company scales, something curious happens. The cloud bill grows. One team swears it’s Kubernetes. Another blames the Black Friday promo. But when you’re unsure whether that increase is tied to healthy SaaS growth or simply overspending, your margins are already at risk. That gap between what’s spent and what’s understood is where indirect costs live. Yet these costs rarely show up in dashboards. Well, until it’s too late.

In software engineering, we often talk about the “iron triangle” of constraints: time, resources, and features. You can rarely fix all three. At many companies, when scope creeps or resources get tight, the timeline is often the first element of the triangle to slip. At Canonical, we take a different approach. For us, time is the fixed constraint. This isn’t just about strict project management. It is a mechanism of trust.

Telecommunications networks are undergoing a cloud-native revolution. 5G promises ultra-fast connectivity and real-time services, but achieving those benefits requires an infrastructure that is agile, low-latency, and highly reliable. Kubernetes has emerged as a cornerstone for telecom operators to meet 5G demands.

FinOps insights for IT leaders often focus on cloud spend, but IT leaders know that real cost drivers extend across hybrid environments. Achieving clarity requires more than budget reports. It requires understanding how workloads behave over time, how performance and capacity shift, and where visibility gaps hide operational and financial risk. To support those efforts, we sat down with Tim Conley, creator of Galileo, to explore practical FinOps insights for IT leaders.

The financial service industry has spent years modernising their software delivery pipelines. Build and test cycles are fast, infrastructure is automated, and engineering capability is no longer the bottleneck. The slowdown now occurs at the end of the process: the last mile, where a change must prove it is safe before it can enter production. This final step is governed by a trust layer with people in it.

Start with distributed tracing to find which service is slow, then use continuous profiling to see why the code is slow, and finally apply high-cardinality analysis to identify which users or conditions trigger the problem. It's 2 AM. Your phone buzzes. Users are reporting timeouts. The metrics dashboard shows p99 latency spiking from 200ms to 4 seconds, but everything looks normal—CPU at 60%, memory stable, no error spikes. A quick pod restart helps briefly, then latency climbs right back up.

In my previous posts, we discussed the where and the how of managing your ML assets. We showed you how JFrog Artifactory acts as a powerful, universal model registry (the “where”) and how the FrogML SDK serves as the gateway to get your models and metadata into it (the “how”). Now, let’s talk about the why.

At some point in the last few decades, every enterprise convinced itself that the humble FAQ page was going to save IT. If you could just document everything (every how-to, every troubleshooting step, every tribal data nugget living in someone’s head) you could finally stop the ticket flood. The idea was for employees to self-service and avoid escalating to engineers while freely sharing knowledge across a de-siloed ecosystem. But of course, that’s not what actually happened.

Stop guessing on AI hardware. This DevOps guide details when to use TPUs vs. GPUs for optimal performance, cost, and framework compatibility in MLOps.