Operations | Monitoring | ITSM | DevOps | Cloud

Micro-frontend architectures, where independent teams build and deploy separate parts of a frontend application, introduce an observability challenge: Telemetry data is fragmented across services, making it difficult to determine which micro-frontend caused a performance degradation or error spike.

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.

StatusGator now includes a new Outage Reports tab on the service monitor detail page, giving users more visibility into recent service disruptions directly where they monitor services. Users can now quickly review recent outage activity for a specific monitored service without leaving the detail page.

Cloud infrastructure has never been more reliable in theory. In practice, the last six years of cloud outage history have delivered some of the most disruptive incidents on record. Not because cloud providers got worse, but because the systems built on top of them got larger, more interconnected, and more brittle in ways that don't show up until everything breaks at once.

Catching a cascading failure in the first 90 seconds is one of the better feelings in production engineering, and it almost always comes back to your log monitoring pipeline doing its job upstream of the alert. The teams that land there consistently treat log monitoring as a real-time detection layer in its own right, and the choices you make in that pipeline shape how every incident plays out for years.

Most monitoring setups have the same weak spot. Detection is easy. Decision-making is not. StatusCake is good at telling you that something might be wrong. What happens next is where things sometimes get messy. One alert goes straight to a chat room. Another wakes the wrong person. A third ends up getting missed because the site had a brief wobble and recovered before anyone looked. Hermes is useful in that gap.

A lot of what's useful in a high-volume log source is a count, a rate, or a measurement — 5xx responses per minute, p95 request duration, job retry rate. You don't need every line to track those. You need the metric. Log-based metrics is now in beta as part of AppSignal Labs.