Operations | Monitoring | ITSM | DevOps | Cloud

AI adoption is accelerating across organizations, and spending often follows a similar pattern: rapid growth, multiple providers, and limited visibility into where costs originate. Each provider exposes billing data differently, with distinct schemas, dimensions, and interfaces. FinOps and engineering teams often spend significant time consolidating fragmented data, only to end up with partial attribution and limited context about who or what generated the AI spending.

The uptime & availability of our status pages hasn't been great these past few days. The root cause is a persistent and pretty aggressive DDoS attack targeted at our own status page, status.ohdear.app. As a result, the overload on our systems also affected all other status pages we host for clients. We're not yet at Github or Claude levels of uptime sadness, but this isn't acceptable to us. In this post, I'll share what's happening and what steps we've already taken.

AI coding assistants have made it possible for a single developer to build and ship a production application in a weekend. Claude Code, Cursor, GitHub Copilot, and similar tools can scaffold a Rails app, write the models, generate the views, wire up the API, and push to production before Monday. This is genuinely exciting. It is also genuinely dangerous if you do not have monitoring in place before you ship.

Last updated: May 2026 If your team is 2 to 20 developers and you do not have dedicated DevOps, SRE, or platform engineering, most APM tools were not built for you. They were built for the team that has you: a team with specialists who can tune dashboards, configure alerting pipelines, manage data retention policies, and explain the monitoring system to everyone else. You do not have that team. You have developers who also handle deploys, on-call, and debugging production issues between writing features.

Have you ever considered how you can utilize artificial intelligence (AI) without sacrificing control over your data and autonomy? As we continue to navigate the changes of AI in the 21st century, it is important to understand how decentralized AI infrastructure can empower individuals and organizations to harness the potential of AI while maintaining sovereignty over their data and decision-making processes.