Operations | Monitoring | ITSM | DevOps | Cloud

A growing number of B2B and DTC brands are running the same calculation this year: what their ecommerce stack actually costs once every app subscription, integration fix, and developer hour gets added to the platform fee. The answer is pushing a broader look at ecommerce infrastructure itself, not just which platform sits underneath it.

DevOps teams face a critical architectural decision when building modern web applications: should they stick with traditional, monolithic systems or embrace headless architecture? This choice affects everything from deployment workflows to team collaboration, performance optimization, and long-term maintenance costs. Understanding the technical and operational implications of each approach helps teams make informed decisions that align with their specific requirements.

A model returns an answer with a confidence score of 0.94. The team ships it. Six months later someone asks why the system produced that specific answer, and nobody can reconstruct it. For years accuracy was the only number that mattered in machine learning. Get the error rate down, ship the model, move on. In regulated domains that is no longer enough. The harder question is whether you can defend a single decision after it has been made. Most systems were never built to answer that, and by the time someone asks, the information needed is already gone.

Today we are releasing Raygun APM Agent 3.0.14 for Windows, Linux, and Azure App Service. This release is the result of a substantial modernization of the Agent, profiler, installers, and release pipeline. It makes Raygun APM easier to deploy, reduces overhead in several critical paths, adds native Linux ARM64 support, and lets developers investigate APM data through Raygun API v3 and the Raygun MCP server. If you are upgrading from version 2.3.0, there is much more here than a version-number change.