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By Coralogix Team
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.
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By Coralogix Team
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.
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By Jonny Steiner
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.
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By Ofri Grushka
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.
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By Coralogix Team
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.
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By Chris Cooney
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.
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By Jonny Steiner
In high-throughput database environments, a latency spike is rarely a simple story. Modern data layers are distributed, stateful, and constantly changing as shards move, nodes rebalance, caches warm, queries evolve, and connections churn. In practice, spikes usually come from one of three places: For many SRE and Platform teams, the real challenge is disconnected tooling. As one engineering lead recently shared during a technical workshop: “It’s all disconnected.
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By Lily Waldorf
The first two weeks of Claude Code are exciting. The third week is when you realize you don’t have visibility into what it’s doing or what it’s costing you. You would not run a production service without metrics, logs, and dashboards or deploy an API without knowing its latency, error rate, or cost per request.
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By Micha Duman
Incident response has a well-known efficiency problem. The tools teams use to detect and investigate issues are often disconnected from the tools they use to manage and resolve them. Engineers spend a significant portion of each incident switching between platforms, assembling context that should already be at hand. Even when the data is available, correlating signals across user, app, infrastructure, and security events to pinpoint a root cause remains manual and slow.
Kotak811, the digital-first engine of Kotak Mahindra Bank, is a banking platform serving over 23 million users across India. Since its launch in 2017, Kotak811 has transformed into the bank’s primary growth driver, now accounting for 70% of all new customer acquisitions. The platform is widely recognized for offering a paperless, mobile-first experience, providing everything from instant zero-balance accounts to seamless UPI payments and investment tools.
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By Coralogix
If you're building agents trying to figure out the best way to actually make them successful in production, you're going to want to know about headless observability. Headless observability means an agent can access information about the health of your system through a CLI instead of clicking around dashboards. It's the data layer that going to unlock serious autonomy and allow you to scale with agentic workloads.
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By Coralogix
OpenTelemetry has turned observability pipelines into production infrastructure, but managing them at scale often creates a massive operational burden. In this demo, we show how Coralogix Fleet Management acts as the central control plane for your OTel ecosystem, providing the governance and orchestration required for modern DevOps. Stop the "manual marathon" of PRs and Helm upgrades. Move toward a safer, more predictable operating model where telemetry is consistent, audited, and scalable.
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By Coralogix
AI is only as useful as the context you give it. An autonomous observability agent can unlock serious value from your telemetry, but only when the foundation is right: good telemetry, a strong data layer, and efficient access to the data. Annie Freeman and Lewis Isaac had a lot to say about this at AWS Summit London this week! hashtag#Observability hashtag#AI hashtag#AWSSummitLondon hashtag#DevOps hashtag#OpenTelemetry.
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By Coralogix
What happens when 20,000 engineers descend on Amsterdam to talk about Kubernetes and AI? Welcome to Episode 1 of Live Laugh Logs, the podcast from Annie, Lewis and Andre from the Coralogix Developer Relations team where we will get together and recap everything going on in our worlds! We had an amazing time at KubeCon in Amsterdam and had loads of insights from the talks we went to around designing observability systems, all the AI tools being created and how to observe them, and using agent-generated code.
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By Coralogix
Stop the "Scavenger Hunt" during incidents. In this video, we walk through the new Coralogix Trace Drilldown, now GA for all customers. Learn how to move from high-level trace views to deep span insights in a single, unified workspace—without ever losing context. Whether you're investigating a latency spike or a failing microservice, the Trace Drilldown helps you answer "Where is the bottleneck?" from three different perspectives in one frame. What you’ll learn.
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By Coralogix
Transform millions of spans into a clear visual map. In this demo, we use Coralogix Trace Highlights to isolate a performance regression and pivot from 400k spans down to the exact root cause in just a few clicks.
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By Coralogix
In this video, we introduce Fleet Management and how it helps teams control their telemetry estate as it scales. See how you can centrally manage collectors and agents, standardize configurations across environments, and roll out updates confidently, reducing operational effort and risk.
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By Coralogix
Olly is Coralogix’s AI-native observability agent that makes observability data fast, accessible, and actionable—for everyone. Traditionally, teams have spent valuable time piecing together dashboards and writing queries to troubleshoot issues. Olly changes that by letting you ask real questions in natural language and delivering instant, intelligent answers from across your logs, metrics, and traces.
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By Coralogix
Are you struggling to define reliability targets? Teams nowadays are turning to Service Level Objectives (SLOs), reliability targets that can be used to define how much you can play around with your systems before users are affected too much. While they're a great way of defining reliability targets, they are difficult to manage. That's why we built the SLO Center. One place to define, track, zoom into, and stay on top of all your reliability targets and error budgets - so you can be sure when you can experiment, and when it's best to stay safe.
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By Coralogix
There are numerous types of logs in AWS, and the more applications and services you run in AWS, the more complex your logging needs are bound to be. Learn how to manage AWS log data that originates from various sources across every layer of the application stack, is varied in format, frequency, and importance.
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Coralogix helps software companies avoid getting lost in their log data by automatically figuring out their production problems:
- Know when your flows break: Coralogix maps your software flows, automatically detects production problems and delivers pinpoint insights.
- Make your Big Data small: Coralogix’s Loggregation automatically clusters your log data back into its original patterns so you can view hours of data in seconds.
- All your information at a glance: Use Coralogix or our hosted Kibana to query your data, view your live log stream, and define your dashboard widgets for maximum control over your data.
Our machine learning powered platform turns your cluttered log data into a meaningful set of templates and flows. View patterns and trends, and gain valuable insights to stay one step ahead at all times!