Leading DevOps

  Cloud-native teams have long embraced chaos engineering, game days, and incident response to build resilient, scalable systems. We prepare for failure. We plan for it. We test it. But when it comes to cloud cost overruns? We often react —after the damage is done. It’s time to treat cost anomalies like operational incidents , because that’s exactly what they are: unplanned events that threaten system health—just in a different column of your dashboard. The Myth of Infinite Cloud = The Risk of Infinite Cost The promise of the cloud is elasticity. But elasticity without control is a budgetary time bomb. We wouldn’t let developers deploy to production without testing. So why are teams still allowed to: Launch GPU instances without a use case? Leave unused dev environments running for weeks? Exceed monthly budget targets without warning? It’s not about blame. It’s about systems thinking . Just like latency, throughput, and availability, cost is an operational signal...

 As software delivery accelerates and attack surfaces grow, traditional DevSecOps practices are being pushed to their limits. The integration of artificial intelligence (AI) into DevSecOps workflows is not just a trend—it’s a strategic imperative. AI is driving a seismic shift in how we manage code quality, automate security, respond to threats, and enable secure innovation at scale. In this post, we’ll explore the key ways AI is improving DevSecOps and why forward-thinking organizations are embedding it deeply into their pipelines. 1. Proactive Threat Detection and Response In modern CI/CD pipelines, code moves fast—sometimes too fast for human eyes to catch every vulnerability or misconfiguration. AI helps shift security left and right by: Analyzing code and dependencies with natural language processing and ML to detect hidden vulnerabilities, insecure APIs, or anomalous changes during commits. Real-time anomaly detection in production environments using AI-powered o...

Over the past few months of interviewing and researching dozens of companies—particularly small to mid-sized SaaS businesses—one pattern keeps emerging: the desire to stand up a Cloud Operations (Cloud Ops) organization. It makes sense on the surface. Cloud is now the infrastructure of choice, so naturally, someone needs to “own” it. But what’s unfolding in practice often misses the mark. Many companies are attempting to solve growing cloud complexity by taking all their DevOps, SRE, and platform engineering talent and consolidating them into a Cloud Ops team. The idea? Share them across product teams so no one gets overwhelmed. If that sounds familiar, it should. It’s the same centralization tactic used by traditional IT for decades. And it's creating the same problems. When Cloud Ops Becomes Old IT in Disguise Here’s the playbook we’re seeing: Move DevOps, SRE, and Ops into a central Cloud Ops team. Let them handle infrastructure, CI/CD, monitoring, and cloud securit...

  Other, alternatives like "Not right now" or "Can we stop working on x and focus on this?” While it's important to meet customer requests, allowing them to get everything they want can negatively impact our employees and take time away from items help all customers. Agile has streamlined the process of request and delivery, but it can also lead to wasting time on less important tasks. To protect our employees and our business, we should focus on the highest priority items that will improve our MESS. M-Maintain Efficient Operations E-Expand Customers or Revenue S-Save Expense S-Security Improvements