Operations | Monitoring | ITSM | DevOps | Cloud

SAP customers are navigating a period of significant change. Of course, there’s the transition to Cloud ERP and the scheduled end of standard support options for ECC in 2027 – these are well known. Basis professionals will be familiar with changes in support for Solution Manager and its components including monitoring and change management. Landscape Management has been formally discontinued after 2027. The natural assumption is Cloud ALM fills the gap.

A healthcare technology company routes patient appointment notifications through ActiveMQ. A payment processing firm uses ActiveMQ to bridge its order management system to its payment gateway. A SaaS provider includes ActiveMQ in the architecture scope for its annual SOC 2 Type II audit.

Once you've published more than a handful of KPIs, you eventually need a way to summarize them. A total cost. An overall health status. An organization-wide SLA. Something that lets you answer the big questions without opening ten different dashboards. Summarizing those into a handful of KPIs usually feels straightforward. You add things together, average them, or collapse several statuses into one. The dashboard becomes easier to read, and nothing looks obviously wrong.

Most companies buy AI tools for developers and hope the impact shows up somewhere. A faster sprint. Fewer escaped bugs. Something. What they don’t have is a way to actually see it happening, which means adoption becomes a leap of faith instead of a measured bet. That’s the gap Kepler and GitKraken Insights close together, and it’s worth understanding as one story, not two separate product updates.

Someone on your team says "the Internet is slow." Twenty minutes later, IT finds out the Internet was never the problem. Maybe it was a laptop with a full RAM disk. Maybe it was an ISP outage two towns over that had nothing to do with your office. Misdiagnosing slow Internet wastes time. It sends you down the wrong fix path, like rebooting a router when the real issue is sitting on someone's desktop.

When our company pushed everyone to start using AI tools, I thought about what it would mean for my team. As a remote company, we are already challenged by the lack of organic human connection. Every connection is planned and takes effort, and now, AI adds another layer. People now spend part of their day collaborating with a tool rather than with a person, which can take away from the time we spend learning from each other.

AI is expensive because the model bill is only part of the cost. Three components set the floor: model subscriptions, per-token API pricing, and infrastructure. Three more make it move: adapting models to your business, catching and fixing errors, and rising energy and datacenter costs. Efficiency doesn't fix it, because cheaper AI gets used more, not less. Businesses are willing to spend on AI. Research from Deloitte found that in 2025, 85% of organizations increased their AI investments.

People run Codex on their own laptops. When Codex is signed in with a ChatGPT subscription, OpenAI’s own admin console shows who used it and how much: messages and credits. What it doesn’t show is what any of that usage was for, or how it compares to what your team spent on other AI tools. The CloudZero desktop agent for macOS installs on a Mac, sees the traffic from AI coding tools, and prices what those tools use.

Budget season is starting and most finance teams are finding the AI line is the most evasive line on the page. You lived through the year. AI spend came in higher than planned and moved in ways nobody could foresee or forecast. And when the board asked what it produced, the honest answer probably was “we’re working on it.”

TLDR: When you work in Harness AI Chat, your work doesn't stop at Harness. Your pipelines live here, but the change you actually need to make might be a YAML file in GitHub, a Jira ticket, or a Confluence doc. So we built an MCP Gateway inside Harness that lets AI Chat reach those third-party apps for you: safely, under Harness's own access controls and secrets, and without dropped sessions across our distributed fleet. This is the story of what we built and why.