Operations | Monitoring | ITSM | DevOps | Cloud

For years, the observability industry has been obsessed with one thing: data volume. We've built incredible pipelines, optimized agents, and scaled storage to handle petabytes of logs, metrics, and traces. The promise was simple: collect more data, get more visibility. But we've hit a wall.

Generative AI is becoming a core part of modern applications, making it essential to monitor and manage how these services are used. That’s why, today, we’re excited to introduce the Anthropic integration for Grafana Cloud, a new solution that lets you connect directly to the Anthropic Usage and Cost API from within Grafana Cloud.

Data-driven decisions are critical. And to support high-stakes decision-making – from fraud detection in credit card transactions to demand forecasting in retail – organizations are increasingly relying on complex models. According to McKinsey, 78% of organizations report using AI in at least one business function, highlighting just how embedded AI and ML models have become in operational and strategic decision-making.

A growing number of engineering organizations have adopted or are trialing agentic AI-based coding tools and LLMs in an effort to increase their teams’ development velocity. If you’re a developer, this means you’ve likely had to try out different agentic tools and models and determine how to best incorporate them into your existing workflows.

Atlassian’s Bitbucket Cloud has tightly integrated CI/CD capabilities via its Bitbucket Pipelines feature set. However, some of our Bitbucket Cloud and Bitbucket Data Center customers still use Jenkins for CI/CD. In this blog, I present a practical walkthrough of the benefits of Bitbucket Pipelines over a tool like Jenkins in the context of two key stats from our recent State of DevEx 2025 report.

Flow data has always held immense potential, but was often inaccessible because it lacked context and speed. Kentik removes that friction by automatically enriching flow with human-readable context, making it a daily driver for everyone, not just specialists.

Real User Monitoring (RUM) measures how real users interact with your application in production. Unlike synthetic monitoring, which relies on scripted tests, RUM collects data from actual sessions. This means performance is observed across different devices, networks, and usage patterns. The result is a clear view of how the application behaves under real conditions, where latency is introduced, which features take longer to load, and at what points users drop off.

Search isn’t what it used to be. The way people discover information online is shifting. Instead of clicking through search results, many now ask AI answer engines like ChatGPT and Perplexity to do the research for them. In March 2025, 13.1% of Google desktop searches featured AI Overviews— doubling from over 6% in January, according to Semrush analysis of 10+ million queries.

GraphQL provides a powerful approach to building APIs, and Absinthe is the leading GraphQL implementation for Elixir applications. While GraphQL offers many benefits, it can introduce a set of errors and performance bottlenecks that might be challenging to track and debug. In this article, you’ll learn how to use AppSignal to monitor, debug, and resolve errors in your Absinthe-based GraphQL API.

Cybersecurity leaders across the public sector are facing a moment of reckoning. Whether at the Department of Defense, a federal agency, or a public university, IT teams are under pressure to defend sprawling infrastructure, detect fast-moving threats, and prove compliance across multiple frameworks—all with fewer resources and tighter timelines. This challenge has accelerated interest in Zero Trust Architecture (ZTA), a paradigm shift in how we think about security.