Why Coroot is the Swiss Army Knife of observability

Estimated reading time: 4 minutes

The open source observability platform has emerged as a way to make sense of an increasingly bewildering volume of logs, metrics, and traces.

As cloud-native architectures grow more complex, traditional monitoring tools aren’t able to provide engineering leaders with the sort of actionable insights they crave. Enter Coroot, an open source observability platform, which has emerged as a compelling option for gathering deep insights into system performance, reliability, and efficiency.

Founded in 2022 by former Headhunter group colleagues Nikolay Sivko and Anton Petruhin, and joined by Percona founder Peter Zaitsev in 2024, Coroot aims to correlate logs, metrics, and traces. This unified platform would mean no more switching between multiple dashboards, the ability to gain real-time insights into service dependencies and performance bottlenecks, and reduce operational overhead by using a single observability stack. 

It also goes one step further by adding AI-driven troubleshooting, eBPF-based CPU profiling, and a unique focus on real-time comparative analysis. As is the trend of the day, Coroot positions itself as a personal assistant for engineering leaders, able to identify the root cause of outages and suggest possible fixes.

The observability landscape today

Traditional observability solutions revolve around three core pillars: metrics, logs, and traces. Many organizations have adopted an observability stack of Prometheus for metrics, OpenTelemetry for traces, Loki for logs, and a wide variety of tools for analysis and response. 

While these components provide valuable insights, they often lead to data silos, forcing engineers to piece together fragmented information. Additionally, many platforms require extensive configuration and manual correlation of data points, making it difficult to derive actionable insights quickly.

What sets Coroot apart

1. Unified observability with minimal configuration

Handling a high-volume of logs can be overwhelming, especially in microservices environments where log noise is a real issue. Coroot employs intelligent log grouping and anomaly detection to streamline analysis.

2. Comparative analysis and performance benchmarking

Coroot introduces side-by-side performance comparison – a crucial feature for understanding regressions and optimizations. Engineers can compare real-time and historical data across different versions, deployments, or regions to pinpoint performance deviations.

For instance, after a new deployment, Coroot automatically highlights changes in latency, error rates, and resource consumption compared to the previous version. This enables rapid identification of performance regressions without requiring extensive manual investigation.

3. Automated dependency mapping

Understanding service dependencies is crucial in distributed architectures. Coroot automatically maps dependencies between microservices, databases, and external APIs, enabling engineers to visualize traffic flow, pinpoint latency spikes, and detect cascading failures.

4. Cost-efficient storage

One major drawback of traditional observability solutions is cost. Storing and querying large amounts of observability data can become prohibitively expensive, especially for startups and enterprises managing high-scale infrastructure. Coroot tackles this with an efficient storage model that enables more cost effective on-premises deployment.

5. eBPF-based profiling

Traditional instrumentation requires modifying application code to include observability hooks, which is an error-prone and time-consuming process. Instead, Coroot leverages eBPF (Extended Berkeley Packet Filter) to provide zero-code instrumentation, allowing developers to monitor system performance and network activity without altering their codebases. Its zero-code eBPF telemetry, prebuilt system analyses, and context-aware alerting remove the complexity traditionally associated with monitoring, allowing engineers to focus on reliability, without deep observability expertise.

Coroot’s ‘coroot-node-agent’, a Prometheus exporter, collects container metrics using eBPF to automate several key monitoring tasks. It discovers running containers by tracing ‘task_newtask’ and ‘sched_process_exit’ tracepoints and detects TCP connections and LISTEN sockets through ‘sys_connect’ and ‘inet_sock_set_state’. To monitor application-layer protocol requests, it tracks ‘sys_write/writev/sendto’ and ‘sys_read/readv/recvfrom’, enabling visibility into HTTP, Postgres, Redis, and other protocols.

6. Kubernetes-native support 

Kubernetes has become the de facto standard for modern cloud-native applications, but it also brings a whole host of observability challenges with it. Coroot addresses these with built-in Kubernetes integrations, offering developers service topology visualization to track dependencies across pods and services, auto-discovery of new workloads, and resource consumption analysis to optimize scaling decisions.

7. AI-powered troubleshooting

One of Coroot’s standout features is its integration of AI-driven anomaly detection and root cause analysis. Instead of merely presenting raw telemetry data, Coroot employs machine learning algorithms to identify unusual system behavior, suggest potential causes, and provide actionable remediation steps.

For example, if a microservice experiences increased latency, Coroot’s AI engine analyzes historical performance patterns, traces dependencies, and highlights specific code changes, infrastructure shifts, or third-party API failures contributing to the issue.

AI for smarter troubleshooting

Large language models have the potential to transform observability by shifting it from a reactive discipline to a proactive one. Coroot’s is looking to leverage these modern technologies to improve on:

1. Advanced anomaly detection – AI models continuously analyze telemetry data, detecting deviations from normal behavior. Unlike static threshold-based alerts, AI-powered detection adapts to evolving workloads and seasonal traffic patterns.
2. Root Cause Analysis (RCA) – When an issue occurs, Coroot correlates CPU usage, memory consumption, request latency, and error logs to pinpoint the root cause.
3. Automated recommendations – Instead of merely surfacing anomalies, Coroot suggests actionable remediation steps. For example, if a database query is slowing down response times, Coroot highlights inefficient SQL queries and recommends indexing strategies.
4. Predictive analytics – Coroot leverages historical data to forecast potential failures, enabling teams to prevent incidents before they escalate. For example, it can predict when disk space will run out or when a particular service will hit resource limits.

The future of observability with Coroot

As AI and machine learning continue to advance, the role of observability platforms like Coroot need to expand beyond monitoring into intelligent, self-healing systems.

By integrating AI-driven insights, low-overhead profiling, and automated dependency mapping, Coroot is setting a new standard for developer-friendly, cost-effective, and high-performance observability. However, it currently lacks a fully managed cloud offering and support for custom dashboards, making it an evolving competitor to established solutions like New Relic and Datadog.

For engineers and SREs tired of sifting through endless logs and fragmented metrics, Coroot offers a refreshingly intuitive yet powerful approach to modern observability.

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