Operations | Monitoring | ITSM | DevOps | Cloud

The UK’s Competition and Markets Authority (CMA) recently published its final verdict on the state of the cloud industry. While the tone may have softened since its initial findings, the conclusion was still damning: hyperscalers like AWS and Microsoft continue to unfairly dominate the cloud market through opaque, inflated pricing and technical lock-in strategies.

Two years ago, a “state-of-the-art” AI model could write decent copy or summarize a meeting transcript. Today, the top AI models can generate working code, analyze video in real time, and reason through complex scenarios. For SaaS teams, these changes represent a strategic crossroads. Choose the right model and you unlock new revenue streams, slash time-to-market, and wow your users.

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.

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.

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.

AI’s Operational Complexity Crisis is Real The AI revolution is transforming how we build and operate software, but it’s also creating a perfect storm of operational challenges that are keeping engineering teams up at night.

AI is surging into products. And the invoices are exploding with it. The key question is no longer, “How much did we spend?” It’s now: “Was it worth it?” That shift, from totals to value, is at the heart of FinOps. The FinOps community defines the practice as bringing financial accountability to the cloud, so teams make tradeoffs with clear business context. In plain English, measure value per dollar, then optimize the system and not just the bill.