Stuff we think matters

Regular monitoring practices can emphasize application response time, but queue time is also often an early and important warning sign. If it rises, you’ll quickly see downstream effects: tail latency, timeouts, and error spikes. This means that this metric can give you a head start tackling app issues before they become user problems. In this post, we’ll discuss queue time, how things can go off track, and practical steps to turn it around.

Throughput is a powerful performance metric, but it is essentially meaningless in isolation. Once correlated with other application data, you start to get to the heart of what is driving performance changes in your application.

Everything looks green and happy on your dashboard, so why are your users upset? Dive into the world of percentiles and how to pull clarity from the metric noise to better understand the health of your application.

Scout Senior Developer Quinn walks us through adding FastMCP instrumentation to our Python agent. The Scout MCP server enables Scout users to query their Scout agent about application performance so that the agent can make decisions and fix issues based on that data.

Sometimes bugs just need to be fixed ASAP Having AI fix them with monitoring data straight from production is very fast and satisfying You should use Scout for errors and MCP

Compare Scout and New Relic APM: pricing, framework support, and the case against per-user observability pricing. Unlimited users on every Scout plan.

Rechain saw their performance decline as their customer base scaled up. Graduating to Scout helped them get a handle on their issues and fix them before their users even saw them.

News from the Scout team. Introducing Scout's MCP Server for AI-native monitoring.

We’re excited to introduce the Scout Monitoring MCP Server — a new way to bring AI-native monitoring directly into your coding assistant. Instead of flipping between dashboards and logs, the MCP server surfaces performance data, errors, and slow endpoints right inside your AI tool.

The CASA organizations connect trained volunteer advocates with children in the foster care system. Ruby for Good is a nonprofit, volunteer-driven organization that connects developers with organizations like CASA to create technology to serve the greater good. In this case, the result is a multi-tenant codebase that currently serves multiple active CASA organizations, with the potential to scale across court systems nationwide.

AI-native monitoring is designed for teams shipping AI-generated code. It brings errors, metrics, and performance data into your LLM, where you already work. Learn how this new approach helps developers tame black-box AI code, cut tool sprawl, and stay in flow — making apps built with AI-generated code easier to debug, scale, and trust.

What happens when issues aren't obvious? Good monitoring can keep you in the know and a step ahead of your users, preventing broken trust and lost revenue.