Operations | Monitoring | ITSM | DevOps | Cloud

Welcome to the Honeycomb Changelog B-Sides, Vol. 1, Fall 2026 Hear about important new features that didn't get fanfare!

In this video, Emily Allen, Director, Finance & Accounting at CloudZero, explains why AI capitalization is still unclear for most finance teams, and how CloudZero gives them a repeatable approach. As AI writes more of the code, finance needs to know which AI spend went to qualifying development work and which should be expensed. CloudZero records which project each AI coding session served, what kind of work it was, and what it cost, so finance has the evidence behind every decision.

In this video, Brady Lenahan, VP of Sales at CloudZero, shares what he’s hearing from customers when the cap hits before the month ends: employees who’ve built their workflows around AI stop working productively and wait for next month. Some go golfing, some clean the garage. Either way, finance loses both the productivity and the visibility. There’s a better option than capping AI spend you can’t see.

AI Budgeting for Finance: Last Year vs. Now In this video, Emily Allen, Director, Finance & Accounting at CloudZero, explains how AI planning changed in a single year. Instead of starting from a vendor commit or the current run rate, she’s treating AI spend as its own category and analyzing it bottom-up. That opens a new question for finance: “What does good look like?” With a year of data, finance teams can start benchmarking AI spend over time and check whether what they approved actually paid off.

We gave $10k worth of data to five different observability platforms. Which agent finds the true answer? In the first episode of our benchmarks series, Chris and Annie walk through how an agent performed on Coralogix, Datadog, New Relic, Dynatrace and Grafana Cloud across 10 injected incidents. They also explain why sampled data makes agents "hallucinate the gap". Subscribe to our channel for more benchmarks and insights into observability and AI.

Learn what a unit test is, how AI coding assistants can write them for you, and the three key conditions every unit test should check to ensure software quality. Learn more: speedscale.com.

Agentic IT operations (Agentic ITOps) deploy autonomous AI agents that can perceive, reason, plan, and act to investigate incidents, coordinate people, and improve with every incident they touch. Think of it as the next step in a progression, where monitoring surfaced the symptoms, AIOps separated signal from noise, and Agentic ITOps takes action on what it finds.

The nature of cyber security is changing. In this video, I do a brief recap of a few of the compromises of the past couple weeks as well as highlight how our role as engineers is changing. Attackers have an easier time than ever in watching repos for changes to exploit 0-day vulnerabilities. But, it isn't all doom and gloom as long as we put in the effort. AI is creating a bit of an arms race between attackers and maintainers of sites and services, but there are actually more powerful tools available to defenders through exclusive programs like Daybreak, Glasswing, etc.

How do you trace a dropped conversion or a checkout failure to its technical root cause? See how full-stack observability in Grafana Cloud connects business impact to system performance across web and mobile clients, applications, infrastructure, and databases. In this session, Principal Software Engineer Jim Gehrett and Staff Product Manager Sarah Constant demonstrate how to start with a business problem and work backward through the technology stack to investigate its cause.

How does Uber use continuous profiling to improve performance and resource efficiency across several million CPU cores? In this Grafana Cloud case study, Senior Researcher Chris Zhang from Uber’s Frontier Tech Labs explains how the team uses Grafana Cloud Profiles and Grafana Assistant to identify expensive code and scale code optimization across its production systems.