Operations | Monitoring | ITSM | DevOps | Cloud

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.

Modern systems are complex, distributed, and fast-changing, so keeping them reliable requires more than watching dashboards. Observability vs. Monitoring explains how teams gain the deep insight needed to detect, diagnose, and resolve issues. Monitoring collects predefined metrics and alerts you to known problems, while observability provides rich, contextual telemetry to investigate unknown failures.

Software delivery is more complex than ever. Teams need speed, reliability, and scalability to stay competitive. Site Reliability Engineering (SRE), DevOps, and Platform Engineering are three key disciplines that address these challenges. Though these terms are often used together, they are not the same and share distinct differences. In this blog, we’ll discuss each term individually, compare SRE vs. DevOps vs. Platform Engineering, and also show how they work together.

No doubt that incidents are inevitable. However, it’s how you manage them (detect, respond to, and resolve) that matters. And a robust incident management process relies on data, not guesswork. Incident Management metrics like MTBF, MTTR, MTTF, and MTTA provide measurable insight into reliability, response time, and recovery performance. When used together, they help identify weaknesses, reduce downtime, and build more resilient systems.