Operations | Monitoring | ITSM | DevOps | Cloud

We're thrilled to share that we have a new SharePoint plugin! Watch the video below to see how simple it can be to bring business context into your dashboards.

The Problem: AI workloads are dynamic, unpredictable, and expensive. Data prep can choke your pipeline, training jobs hog GPUs without awareness, and inference, the most latency-sensitive phase, is notoriously hard to scale efficiently. Worse, traditional infrastructure tools treat GPU as a static commodity, ignoring model intent, workload shape, and sharing capabilities.

In enterprise boardrooms today, what was once an arms race to adopt more tools and chase every new capability has now crystallized into a single mandate, “Make the platform work harder without spending more.” The industry has reached a saturation point. The buyers who once greenlit expansions now demand efficiency. And the ones who built the stack? They’re rethinking it entirely. It’s no wonder platformization is taking off.

I wanted to share a story of a recent engagement with a law firm to highlight the strategic importance of compliance in today’s legal sector. It started with a single email. A mid-sized law firm received a regulator’s request for evidence following a client complaint. The issue wasn’t malpractice; it was a missed filing deadline caused by a system slowdown. The firm had no audit trail to prove the delay was technical, not procedural.

If you caught our recent Introducing Honeycomb MCP: Your AI Agent’s New Superpower webinar, you know it was a lively mix of big ideas, demos, and a few laughs about the messy, fast-moving world of AI. Hosted by Austin Parker, Morgante Pell, and James Bland from AWS, the conversation explored how Honeycomb’s new Model Context Protocol (MCP) is changing the way developers and AI agents interact with data.

In early October, Red Hat disclosed a breach of a GitLab system used by its Consulting division. Threat actors claim to have exfiltrated hundreds of gigabytes of project data — and while investigations are still underway, reports suggest consulting engagement artifacts may have been impacted. For the organizations involved, the concern isn’t limited to reputational damage.

ManageEngine vs. Jira Service Management: Which is best? With numerous options available, it can be challenging to determine which IT Service Management (ITSM) solution best aligns with your specific needs. In this article, we’ll closely examine and compare ManageEngine and Jira Service Management, two of the industry's leading service desk platforms.

As machines and systems grow more connected and complex, the traditional toolbox for managing them feels increasingly outdated. Engineers and operators need new approaches that match the realities of software-driven products and data-intensive environments. Digital twins provide that leap forward. By creating a virtual model of a physical asset and continuously feeding it with real-time data, digital twins reveal both current performance and likely future outcomes.