Telemetry IQ

Introducing Mills, the world's first agentic telemetry management platform. Cuts observability costs by 60–80%, improves data quality, and operates your entire telemetry lifecycle autonomously.

Every hand-written monitor is a guess about the system, frozen on the day someone wrote it. We read 15 public postmortems to see what teams actually fix after an incident, and why coverage keeps drifting anyway. Plus the four principles any replacement has to meet, and a decay check you can run this week.

AIOps groups the alerts you already have. An AI SRE investigates once an alert fires. Neither sees the problems nobody wrote a rule for. Here is what each approach catches, one regression walked through all three, and why autonomous operations need something that keeps curating the monitors.

The Datadog bill rarely grows because prices went up; it grows because your services emit more billable telemetry every quarter and nothing upstream governs it. A meter-by-meter breakdown of what drives Datadog cost — logs, custom metrics, APM spans, hosts, and retention — and the upstream control surface for each.

Kubernetes generates more telemetry than the workloads it runs, and the bill lands on the platform team. A guide to what to instrument, where to collect, how to enforce policy across services you don't own, and how to bend the cost curve before the next renewal.

The receivers and exporters are plumbing; the processors are policy. A practical look at the OpenTelemetry Collector decisions that determine your bill, your signal quality, and whether on-call can find the trace they need at 3am.

Grepr frames the telemetry pipeline choice as automation versus manual work. We think that gets it backwards. The most important thing a pipeline gives a team isn't the removal of decisions, it's control and visibility over the decisions that get made. Here is how Sawmills thinks about that compared to Grepr.

Active series doubling in Prometheus? Learn how cardinality grows, how to find the offending label, and where to cut it fast.

Most teams try to fix their observability bill at the backend, after they have already paid to ingest, index, and store everything. The leverage is upstream, in the telemetry pipeline. A platform-engineer reference on what a pipeline actually is, where the cost and reliability decisions live, why agent-plus-gateway is the topology that holds, and why a pipeline that works on day one quietly stops working six months later.

Picking a logging tool is the least interesting decision in Kubernetes log aggregation. What determines whether it holds at scale is the DaemonSet-to-gateway architecture underneath it and who owns the policy for what gets collected. A platform-team breakdown of the pipeline, where it breaks at 5,000 pods, and why the backend choice is downstream of the pipeline.