You can find the slice of users who are broken — one tenant, one plan, one build — using only published OpenTelemetry packages: @opentelemetry/web-common for session ids, a ~15-line span processor for your own attributes, and W3C baggage to carry the session into your backend spans. No RUM vendor, no proprietary agent, no schema to register.
We run a real six-service bank that moves money continuously. When we stopped its ledger, the first alert fired in 20 seconds and the diagnosis named the dead service by name — not because a model guessed, but because the evidence was gathered first and the model was only asked to read it.
You can add full distributed tracing, metrics, and logs to a Python microservices app without touching application code — install the OpenTelemetry distro, bootstrap the instrumentors, and run each service under the opentelemetry-instrument wrapper. Here is exactly how we did it for Fathom, a nine-service bank, plus the one gotcha that keeps logs from showing up.
The cheapest way to run observability at scale is to stop paying hot-index prices for cold data: control cardinality, sample at the tail, land telemetry in object storage, and let AI do the first pass of triage. Here is the playbook, with the math.
AI incident triage is the use of AI agents to do the first phase of incident response automatically: cluster alerts, correlate telemetry with deploys, rank probable root causes, and draft the incident summary before a human is paged. Here is how it works and how to evaluate it.
A fair comparison of Datadog alternatives for teams whose observability bill is scaling faster than their traffic: Grafana Cloud, Honeycomb, New Relic, self-hosted stacks, and Rocketgraph — by pricing model, strengths, and watch-outs.
Rocketgraph is an observability platform that delivers metrics, logs, and traces at scale on object-storage economics, with AI agents that triage issues automatically. This is the technical explainer: how the architecture works, why it stays cheap, and what the AI actually does.