Operations | Monitoring | ITSM | DevOps | Cloud

AIOps platforms have gained traction because teams are dealing with more alerts, data, and monitoring tools than they can realistically manage. The goal isn’t to replace people, but to reduce the manual work that comes with sorting through alerts, figuring out what’s actually wrong, and figuring out who should respond. Some tools focus on event correlation. Others lean into automation, anomaly detection, or service-level visibility.

Time series data, such as IoT sensor readings or stock market ticks, flow in fast, often at a rate of millions of points per second. Querying this data, especially years of historical records, can be slow and painful if using a nonspecialized database rather than a time series database like InfluxDB.

If your users are spread across the globe but your servers are sitting in Virginia, you’ll probably hear complaints about slow load times, especially from places like Australia. CDNs fix this by caching static assets closer to where your users are. Azure CDN does exactly that, and it fits well if you're already using Azure services. You can hook it up to Blob Storage, App Services, or your origin. This guide covers how to set it up, what to expect, and how to know it’s working.

Sometimes your API call takes a few seconds longer than expected. Or users start reporting slow page loads. One of the most common reasons? Network latency.

We’ve been steadily building something powerful into GitKraken: AI that understands your code and your context. In recent releases, GitKraken AI has already helped you: Now, in version 11.2, it’s tackling one of the most frustrating parts of your day: merge conflicts.

What if you were to tell Kubernetes Monitoring what you wanted, and the system configured collectors based on your choices? We wondered that as well—wondered enough to create Alloy Operator and its Helm chart for version 3.0 of the Kubernetes Monitoring Helm chart. We’re excited to share that the new Kubernetes Monitoring Helm chart is now available, and it introduces a dynamic way of setting up your telemetry data collection with Alloy Operator.

Trends in tech come and go, but certain underlying primitives stick around forever. In software, two such primitives are virtual machines and containers. Virtualization paved the way for the cloud to become massive. Data centers would likely never have been commercially viable without it. While still relatively new, containerization has already made a serious mark on the software engineering world.

In Part 1, we explored how aligning network visibility with IT service context empowers faster, smarter incident response. But what does this actually look like? Here in Part 2, we’ll go deeper into the challenges of traditional monitoring approaches, and how teams should look to move from fragmented alerts to unified insights – because when ITOps and NetOps can both see the “what” & “why” of the problem, actions become instinct.

This guide aims to help your team shift testing left, simulate real user behavior, and catch critical issues early as part of CI/CD, prevent regressions from reaching production by automating tests as part of your CI/CD and aborting deployments that contain issues. Synthetic monitoring is a great way to check important flows in production and make sure everything is working the way it’s supposed to.

Tired of trying to guess if that half-baked LLM suggestion is really going to fix the issue with your code? Meet Seer—our new AI agent that taps into all the issue context from Sentry and your codebase to not just guess, but root cause gnarly issues and propose merge-ready fixes specific to your application. Code gen tools are great fun—and useful. But even a recent Microsoft study confirmed what you already know: AI struggles with debugging.