Operations | Monitoring | ITSM | DevOps | Cloud

Update (March 2026): AWS has announced that AWS Proton will reach end of life on October 7, 2026. After that date, the Proton console, API, and pipeline management will be permanently unavailable. If you're currently using Proton, read our migration guide.

As a team, we have spent many years troubleshooting performance problems in production systems. Applications have become so complex that you need a standard methodology to understand performance. Our approach to this problem is called the Golden Signals. By measuring these signals and paying very close attention to these four key metrics, providers can simplify even the most complex systems into an understandable corpus of services and systems.

AI cost management is the practice of tracking, allocating, and optimizing the cloud infrastructure costs tied to building, running, and scaling AI workloads. It differs from traditional cloud cost optimization because AI infrastructure behaves differently at every layer of the stack. The biggest problem isn’t overspending. It’s that most organizations can’t see where their AI spending is going.

Taking a look back over the last ten years in enterprise technology, paradigm shifts are occurring more frequently. For example, the maturity of DevOps/Platform Engineering and Cloud Native infrastructure has occurred. The new frontier depending where you are in adoption is AI. As your adoption and maturity curve progress, operationalizing these paradigms become important.

Caching is critical to modern apps, helping you serve data more quickly and improve your app’s overall performance. Effective caches can result in better Core Web Vitals (CWV), improved search visibility, and a smoother user experience.

Your phone rings. A user is complaining that “the network was slow" or "had issues around 3pm." You run a speed test. Green across the board. No active alerts. Everything looks fine. So what do you tell them? If you don't have a continuous, time-stamped record of what your network was doing at 3pm, you can't tell them anything, not with confidence. You're stuck choosing between "I didn't see anything" and "I'll keep an eye on it," neither of which fixes the problem or satisfies the user.

On March 18th, we hosted a session focused on a challenge that continues to undermine even the most mature IT operations teams: ticket noise. It’s easy to dismiss noise as just “too many alerts”. But as we explored in the webinar, the real issue runs deeper. Ticket noise is a symptom of something more fundamental — a lack of correlation, context, and shared visibility across the stack.

Disclosure: This comparison is written by our product marketing team that works closely with IT operations and on-call workflows. While we build on-call management and incident alerting software ourselves, this guide is designed to help teams understand how different tools fit different operational needs. We believe there is no single “best” tool. Only the right fit for a given team.

Learn how to use the Model Context Protocol (MCP) to transform static runbooks into intelligent, real-time investigation tools for Kubernetes and cert-manager.

In the 2026 regulatory landscape, manual audits are a liability. This guide explores using GitOps to generate DORA-compliant audit trails through IaC, drift detection, and automated segregation of duties. Discover how the Qovery management layer turns compliance into an architectural output, reducing manual overhead for CTOs and Senior Engineers.