It's the End of Observability as We Know It (and I Feel Fine)

Summary

In a really broad sense, the history of observability tools over the past couple of decades have been about a pretty simple concept: how do we make terabytes of heterogeneous telemetry data comprehensible to human beings? New Relic did this for the Rails revolution, Datadog did it for the rise of AWS, and Honeycomb led the way for OpenTelemetry. The loop has been the same in each case. New abstractions and techniques for software development and deployment gain traction, those abstractions make software more accessible by hiding complexity, and that complexity requires new ways to monitor and measure what’s happening. We build tools like dashboards, adaptive alerting, and dynamic sampling. All of these help us compress the sheer amount of stuff happening into something that’s comprehensible to our human intelligence. In AI, I see the death of this paradigm. It’s already real, it’s already here, and it’s going to fundamentally change the way we approach systems design and operation in the future. New to Honeycomb? Get your free account today. LLMs are just universal function approximators, but it turns out that those are really useful I’m going to tell you a story. It’s about this picture: If you’ve ever seen a Honeycomb demo, you’ve probably seen this image. We love it, because it’s not only a great way to show a real-world problem—it’s something that plays well to our core strengths of enabling investigatory loops. Those little peaks you see in the heatmap represent slow requests in a frontend service that rise over time before suddenly resetting. They represent a small percentage of your users experiencing poor performance—and we all know what this means in the real world: lost sales, poor experience, and general malaise at the continued enshittification of software. In a Honeycomb demo, we show you how easy it is to use our UI to understand what those spikes actually mean. You draw a box around them, and we run BubbleUp to detect anomalies by analyzing the trace ...