Operations | Monitoring | ITSM | DevOps | Cloud

There’s growing unease in the database world regarding delivering at speed, raising the question – just how do we keep up with the pace of change without losing control of the things that matter most? AI is rapidly transforming the mechanics of how code is written, reviewed, and optimized which in-turn, increases the risk of destabilization.

From the beginning, PagerDuty has been built by developers, for developers. Our mission has always been to help development teams build faster and resolve incidents more efficiently by meeting them where they work. Building on PagerDuty’s existing plugin for Spotify for Backstage, we are thrilled to announce the PagerDuty plugin for Spotify Portal for Backstage to continue bringing enterprise-grade incident management into even more developer workflows.

The network has transformed dramatically. Hybrid cloud architectures and AI-powered infrastructure seem to expand in scale and complexity every day. But legacy tools to monitor and observe these networks have not kept up.

Metrics are fundamental to experimentation for two reasons: They set the basis for evaluating ideas and interventions, and they can suggest where to look next. As such, many teams collect a wide variety of metrics, from application performance data to revenue trends. However, doing so often means manually knitting together data from multiple sources and formats. Even then, data silos can make it challenging to understand the full impact of experimental changes. In this post, we’ll explore.

Managed service providers (MSPs) are strong multitaskers, handling monitoring, documentation, security, infrastructure maintenance, support, and more for each of their clients. So clearly the need for a strong set of MSP tools is one that cannot be overlooked. In the current state of IT, clients expect swift response and seamless service delivery no matter the time of day, meaning, MSPs must invest in a toolkit that will enable them to deliver high-quality service 24/7.

In my conversations about managing large, complex networks, one topic is now constant. The issue isn't budgets or new technology; it's about personnel. Specifically, it's the increasing difficulty of finding and retaining skilled professionals. If you are feeling this pressure, you are not alone. The search for technical talent is a universal challenge.

In my last blog, I’ve discussed a common problem: metrics like mean time to resolution (MTTR) mean nothing to business leaders. Celebrating a faster fix for an outage that still cost the company thousands in lost sales is a conversation that goes nowhere. You might as well be speaking a different language.

When the internet goes down, our primary job is to help everyone get back up, as fast as possible. Of the almost half a million incidents we've helped our customers solve, there are some which stand out for both their scale and impact. One of these happened on Monday, October 20, when AWS had a widely covered major outage in their us-east-1 region, from 07:11 to 10:53 UTC. We’re hosted in multiple regions of Google Cloud and so the majority of our product was unaffected by the outage.

The 2025 DORA report just surveyed nearly 5,000 technology professionals and delivered a verdict that should reshape how you think about AI investment: AI doesn’t create organizational excellence; it amplifies what already exists. For teams with solid foundations, AI is a force multiplier. For teams with broken processes and dysfunctional systems, AI magnifies the chaos.