Imagine you run an online store. Everything was fine when the whole site lived as a single application on a single server. Then the business grew, and the team decided to split the system into separate pieces — a shopping cart here, a product catalog there, a payment service, a notifications service. Each piece lives on its own server and talks to the others over the network. This is what people call a microservices architecture.
Sounds sensible. But one day something goes wrong: payments freeze, users are furious, the support line is ringing off the hook. You open your monitoring dashboard — hundreds of graphs, thousands of log lines. The payment service seems fine. The catalog too. So where is the problem? Between them? In the database? In the network?
This is exactly the kind of moment DevOps engineers dread. And it is exactly the kind of moment Coroot was built for.
Coroot is an observability tool — a system that helps you understand what is happening inside a complex technical infrastructure. But unlike most of its competitors, Coroot does not just show you data — it explains what went wrong and why.
This article is written for everyone who wants to get to grips with the topic: DevOps engineers considering a new tool, as well as managers and product owners who need to understand what their engineering team is talking about.
What Is Observability and Why Does It Matter
Before diving into Coroot, it is worth understanding the concept it is built around.
Observability is the ability to understand the internal state of a system from what it outputs to the outside world. If a system is observable, its signals — metrics, logs, traces — let you diagnose any problem, even one nobody anticipated in advance.
Traditional monitoring works differently: you decide upfront what you want to track and set up specific checks. CPU above 90%? Alert. Service not responding? Alert. But what if the problem is not CPU or availability, but the fact that a specific request from service A to service B has started taking 300 milliseconds longer because of a missing index in the database? Standard monitoring will not catch that.
Observability solves this. It rests on three pillars: metrics (numerical measurements over time), logs (text records of events), and traces (chains of calls between services). Coroot collects all three types of data — but it does something critically important on top of that: it connects them and interprets them automatically.
How Coroot Works
Coroot is an open-source project. Its code is public, you can study it, deploy it yourself, and extend it if you like. The first version appeared in 2022, the project is actively developed, and it is already used in production environments around the world.
Coroot is built around several key components that work together.
The eBPF Agent: the Eyes of the System
The main technical differentiator in Coroot is the way it collects data. Most monitoring systems require you to modify your application: add a library, insert code to emit metrics, configure exporters. This is called instrumentation, and it takes time, requires coordination with developers, and becomes a project in its own right.
Coroot works differently. It uses eBPF (extended Berkeley Packet Filter) — a Linux kernel mechanism that lets you safely run small programs inside the operating system without touching the application itself. The Coroot agent is installed once per server and immediately starts seeing everything: which processes are running, how they communicate, how long each request takes, where latency spikes.
Think of it like installing smart scales for the entire house: you do not need to weigh every object individually — the system notices changes on its own.
The Metrics Store
Collected data has to live somewhere. Coroot uses Prometheus — the de facto standard in the monitoring world — or works with compatible storage backends. This means that if you already have Prometheus, Coroot connects to your existing infrastructure without friction.
The Analysis and Visualization Layer
Raw data is not the same as answers. Coroot takes metrics, traces, and logs, analyzes them together, and produces an interpretation: which service is behaving abnormally, what might have caused it, and how the different symptoms are connected.
The Coroot interface is centered on the concept of a service map — a visual graph where each service is a node and arrows between them show dependencies and the direction of traffic. The color of each arrow reflects health: green means all is well, red means there is a problem. In seconds you can see exactly where something went wrong.
How Coroot Finds Problems: Step by Step
To make this concrete, let us walk through a specific scenario. Users are complaining that checkout is slow.
Step 1. Anomaly detection. Coroot continuously tracks the response time of every service and every request type. When metrics deviate from the norm, the system flags it as an anomaly — automatically, with no manual threshold tuning. You see a notification: "Response time for the checkout service has increased 40% over the last 10 minutes."
Step 2. Request tracing. Coroot builds the call chain: the checkout service called the inventory service, which queried the database. It is visible that the database query is taking unexpectedly long — 1.2 seconds instead of the usual 50 milliseconds.
Step 3. Root cause analysis. Coroot looks at the database metrics at the same point in time: disk load is high, the number of waiting queries has grown, and a backup job started running in parallel. Coroot connects all of this into a single picture.
Step 4. Diagnosis. The interface shows an explanation: "The checkout service is experiencing slowdowns due to high database load. Likely cause — a competing backup task." This is no longer just data; it is a concrete hint for the engineer.
Step 5. Resolution and verification. After the engineer reschedules the backup to run at night, Coroot records the normalization of all metrics and closes the incident.
The whole journey from detection to diagnosis takes minutes instead of hours.
Coroot vs. the Competition: What Sets It Apart
The observability market has no shortage of tools. Here is how Coroot compares to the best-known alternatives.
The key advantage of Coroot over paid SaaS solutions is data control and freedom from vendor lock-in. All data stays on your own infrastructure. This is especially important for companies with strict security and compliance requirements — finance, healthcare, the public sector.
Compared to the classic Grafana + Prometheus stack, Coroot wins on time-to-value and depth of analysis: Grafana itself cannot automatically find the root cause of an incident — that requires manually building rules, dashboards, and alerts, which demands serious expertise.
Limitations and Risks Worth Knowing
Linux and eBPF dependency. eBPF is only available on Linux kernel version 4.14 and above. If your infrastructure runs on Windows or very old Linux versions, Coroot will not work. Most modern cloud environments meet this requirement, but legacy systems can be a blocker.
Host overhead. The eBPF agent runs at the kernel level and consumes server resources — CPU and memory. On a typical production node this is roughly 1–3% CPU and 100–300 MB of RAM. For high-throughput systems handling tens of thousands of requests per second, the performance impact should be measured separately.
Metrics storage. The longer you want to keep historical data, the more disk space you need. For a large infrastructure with hundreds of services, storage can run into terabytes. Capacity planning is a mandatory step before deployment.
Non-standard protocols. Coroot handles standard protocols well: HTTP, gRPC, PostgreSQL, MySQL, Redis, Kafka. If your services communicate over a custom binary protocol, automatic tracing may be incomplete.
Learning curve. Even though Coroot is significantly simpler than assembling an observability stack from scratch, using it to its full potential still requires a working knowledge of DevOps and Kubernetes. A complete beginner will not configure it in an hour.
Five Scenarios Where Coroot Genuinely Helps
Scenario 1: Slow Database Query
The team notices the API is responding slowly. Coroot shows that the 95th percentile latency (the slowest 5% of requests) has risen from 200 ms to 800 ms. The trace points to a specific SQL query missing an index. The developer adds the index — problem solved in 20 minutes instead of hours of searching.
Scenario 2: Cascading Failure Across Microservices
One of 30 services goes down and drags a chain of others with it: dependent services start timing out, blocking threads, consuming memory. Coroot immediately displays a map with red arrows — it is clear that there is a single root cause and everyone else is a victim. The team fixes the source, not the symptoms.
Scenario 3: Degradation After a Deploy
Developers ship a new version of a service. Users are not yet complaining, but Coroot has noticed: errors climbed from 0.1% to 1.8% and memory consumption is up 40%. The team spots this before it becomes widespread and rolls back the release proactively.
Scenario 4: Scaling Planning
Before a major marketing campaign, the CTO wants to know whether the infrastructure can handle five times the traffic. Coroot shows historical resource consumption data and pinpoints bottlenecks — the team knows exactly which services to scale and which will cope without changes.
Scenario 5: A 3 AM Incident
The on-call engineer gets an alert in the middle of the night. With Coroot, they open a single screen and within two minutes they know: the problem is in a specific service, the cause is connection pool exhaustion, the fix is a pool restart. The incident is closed in seven minutes without waking the whole team.
How to Deploy Coroot: the Key Steps
Deploying Coroot is simpler than it looks. Let us walk through the path for a typical Kubernetes environment.
Infrastructure preparation. For a production deployment, Coroot needs a dedicated server or Kubernetes namespace with enough disk for metrics storage. A cloud VPS is a natural fit here — for example, on the Serverspace platform — which can be scaled flexibly as the volume of data grows.
Installation via Helm. The most common approach is a Helm chart, the standard deployment mechanism for Kubernetes:
helm install coroot coroot/coroot --namespace coroot --create-namespace
This single command deploys all components: the data-collection agent, the metrics store, and the web interface.
Storage configuration. By default, Coroot keeps data inside the cluster. For production, it is recommended to configure external storage — ClickHouse for traces and Prometheus or a compatible solution for metrics.
Verifying agent status. After installation, confirm that the eBPF agent is running on every cluster node. In the Coroot interface this is immediately visible: all hosts appear in the Infrastructure section, each showing its agent status.
Setting up alerts. Coroot can send notifications to Slack, PagerDuty, Telegram, and other systems. This is configured in the Integrations section — just provide a webhook URL or token.
Once these steps are done, Coroot is collecting data. The first insights appear within minutes of startup.
Common Mistakes When Working with Coroot
Mistake 1: Underestimating storage volume. Teams often start with minimal disks and discover a week later that space has run out and historical data has been deleted. Rule of thumb: budget roughly 2–5 GB per service per month under standard load, and plan for at least twice that as headroom.
Mistake 2: Installing in production without testing. The eBPF agent is safe, but any new component in production carries risk. The right approach: deploy Coroot in a staging environment first, measure resource consumption against your actual workload, and only then move to production.
Mistake 3: Ignoring the service map. Many teams use Coroot like a regular monitoring tool — they only look at metrics and alerts. But the real power of the tool is in the dependency map. Skip that section and half the value goes unused.
Mistake 4: Setting alerts that are too sensitive. If you configure notifications for the slightest deviations, the team starts receiving dozens of alerts a day and gradually stops reacting — this is called alert fatigue. Start with broad thresholds and narrow them down based on real incidents.
Mistake 5: No owner for the tool. Coroot is a living system that requires periodic attention: version updates, storage optimization, onboarding new services. If nobody in particular is responsible for it, the tool gradually falls behind and loses its value.
Conclusion: What to Take Away
Coroot is an observability tool with automatic diagnostics that works without modifying application code. It collects behavioral data about services via eBPF, builds a dependency map, and helps teams find the root cause of problems faster than traditional approaches allow.
Its main strengths: open-source, no need to instrument applications, an accessible interface, and built-in root-cause analysis. Its main constraints: Linux only, additional server overhead, and the need to plan for storage upfront.
If your team works with microservices or Kubernetes and spends a lot of time investigating incidents — Coroot is worth trying. Start with a test environment, deploy via Helm, explore the service map, and see what the system surfaces on its own.
Next step: visit coroot.com, browse the documentation, and spin up a demo environment. It takes less than an hour, and your understanding of your infrastructure will change immediately.
FAQ
Is Coroot free?
Yes, the Community edition is fully open-source and free to use in a self-hosted deployment. There is a paid Enterprise edition with additional features — AI-powered root-cause analysis, SSO, RBAC, 24×7 support, and capacity planning — priced at $1 per monitored CPU core per month.
Do I need to change my application code to use Coroot?
No. This is one of the key advantages: the eBPF agent collects data at the operating system level, requiring no changes to your applications.
Which environments does Coroot support?
The primary environment is Kubernetes. Docker Compose and bare-metal servers running Linux are also supported. Windows is not supported.
Does Coroot replace Grafana and Prometheus?
Not necessarily. Coroot can sit on top of an existing Prometheus stack and complement it with auto-diagnostic capabilities. If you already have Grafana dashboards set up, you do not have to abandon them.
How complex is it to roll out Coroot in a large company?
Technically — relatively straightforward, especially if the infrastructure is already on Kubernetes. Organizationally it is trickier: you need to negotiate cluster-node access for the agent installation and designate someone as the owner of the tool.
Can Coroot be used for compliance and audit purposes?
Coroot itself is not a compliance tool, but since all data stays on your own infrastructure, you have full control over storage, access, and deletion — which matters for GDPR, SOC 2, and other regulatory frameworks.
What if Coroot is not seeing some of my services?
Check that the eBPF agent is running on all cluster nodes. If a service uses a non-standard protocol, its traffic may not be recognized automatically — in that case, consult the documentation on manually adding data sources.