Operations | Monitoring | ITSM | DevOps | Cloud

TCO policies have always been one of the most impactful cost levers in Coralogix. Route business-critical data to High, push monitoring data to Medium, archive compliance logs to Low. With the addition of DataPrime expressions (DPXL) – a subset of the DataPrime query language designed for inline filtering at ingest – that routing became even more precise, matching on any field in the event payload, not just application, subsystem, and severity.

It’s 20 minutes into a P0 incident, and you have already switched between four different tools, re-authenticated twice, and translated queries across three incompatible syntax languages. The root cause you are searching for. Well, that is still out there somewhere. The reality of investigative latency is that most engineering teams face navigation problems, not data problems. During high-pressure incidents, teams lose cognitive momentum due to context switching between disconnected telemetry silos.

There is a moment every engineer knows too well. Something is wrong in production. You have an alert, a vague symptom, and pressure to find the one signal that explains what changed. You open your logs and traces, and you immediately hit the same two problems: the dataset is huge, and the path from “I see something odd” to “I understand why” is full of tiny, exhausting steps. Meet new Explore, our redesigned investigation experience for logs, traces, and spans.

A well-instrumented service tells your on-call engineer which deploy broke checkout, which span ate the latency budget, and which line to revert before the support queue fills up. Getting there depends on how cleanly your application performance monitoring layer turns telemetry into answers. The sections ahead walk through how APM works, the metrics and components worth tracking, the cloud-native challenges at scale, and how to evaluate APM tooling against your real workload.

The fastest incident response teams treat coordination as a craft. Someone owns the call, drives the decisions, and keeps everyone moving in the same direction while the team puts the system back together. That person is the incident commander (IC), and getting the role right is what separates your 15-minute fix from a four-hour war room where nobody’s sure who’s making the call.

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.

OpenTelemetry made telemetry possible everywhere – turning observability pipelines into distributed production infrastructure. Distributed infrastructure requires a control plane for inventory, governance, and safe change. At 500 collectors across hybrid environments, operational overhead becomes a production risk. The moment telemetry pipelines become a distributed infrastructure, they inherit the operational problems of one.

In the world of observability, “cardinality” has become a heavy word. It is a ghost used to justify skyrocketing bills or degraded query performance. When cardinality rises, the advice is almost always the same: reduce it. Drop your labels, or reduce the dimensions. It is usually framed as “optimization.” Every label you add to a metric is a dimension of knowledge. Each one gives you a way to slice, compare, and explain the chaos of production.

This article is a high-level overview of the Coralogix CLI. For a deeper look at how it works in practice, read the full technical deep dive here. Agent-driven investigation sounds simple: read the alert, query the data, return the cause. In reality, most agents either overload their context window with raw logs or guess at queries and return incorrect results.

The new interface into production telemetry is a tool call, made from whichever agent runtime the operator happens to be using at that moment. A finance lead in Claude Code, a product manager in Cursor, an engineer in Codex. Three different jobs, three different agents, three different reasoning loops. The thing they have in common is the data layer underneath.