Operations | Monitoring | ITSM | DevOps | Cloud

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.

Managing the cost of foundation models is a critical challenge as AI adoption surges, particularly for teams using powerful models like Anthropic's Claude Opus and Claude Sonnet. Growing teams generate larger prompt volumes and escalating model complexity, making it difficult to have clear visibility, accountability, and control of cloud AI spending.

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.

If your organization is anything like ours, then you’ve probably embraced using large language models like Claude. Just last week, we gave all Honeycomb employees access to Claude. Now, developers can generate AI-assisted code, product managers can perform analysis on customer usage trends, marketers can test messaging, sales can do customer discovery and we are shipping AI-powered features to improve user experience.

Automation has transformed incident response within individual teams. Diagnostic scripts, runbooks, and alert systems help engineers troubleshoot and resolve issues more efficiently. Translating those gains across the organization remains a challenge. Most automations are built in silos and not designed to work together. The result: disconnected workflows, inconsistent outcomes, and too much manual effort, leaving teams with less time for the strategic work that drives innovation and resilience.