Getting Started with OpenObserve
Explore latest insights and updates
Explore how OpenObserve detects anomalies in logs, metrics, and traces to help SREs identify issues early and take action before incidents escalate.
Learn how to implement distributed tracing in a Java Spring Boot microservices application using the OpenTelemetry Java Agent and OpenObserve. Covers zero-code auto-instrumentation, JVM metrics, cross-service trace propagation, flamegraphs, and Gantt charts , with working source code and curl examples.
Learn how AI-assisted monitoring using MCP enhances observability with intelligent alerts, anomaly detection, and automated insights for faster incident response.
Learn how to implement structured logging in production. Improve debugging, searchability, and observability with best practices and real-world examples.
Discover how AI incident management transforms production operations by reducing MTTR by 90%, automating root cause analysis, and cutting alert noise by 80%. Learn how log clustering, trace correlation, and LLM-powered RCA work
Learn how to measure and dramatically reduce Mean Time to Resolution (MTTR) using AI-powered observability. Discover the four phases that inflate MTTR and how modern teams achieve faster incident resolution with intelligent detection, triage, diagnosis, and remediation
Discover how AIOps transforms IT operations with AI-powered anomaly detection, event correlation, and automated remediation. Learn the core capabilities, use cases, and how observability data drives intelligent operations.
We rewrote the XDrain log pattern extraction algorithm in Rust, achieving 40x performance improvements over Python. Learn how we used prefix trees, systematic sampling, and memory-bounded LRU caches to process 361,000 logs/sec in real-time.
Learn what the Prometheus cardinality bomb is, why high-cardinality metrics break your monitoring, and how to detect, prevent, and fix it effectively.
AI Assistant and LLM Observability are now live on OpenObserve Cloud. v0.70.0 brings a rebuilt Service Graph, visual query builder, Incident Timeline, and more.
Learn how to use the OpenTelemetry Collector Contrib distribution to collect, process, and export telemetry data. This guide covers architecture, key components, configuration examples, and practical deployment tips.
A complete guide to full stack observability - covering frontend, backend, infrastructure, traces, logs, metrics, and OpenTelemetry for DevOps and SRE teams.
Looking for a DataDog alternative for Real User Monitoring? Compare OpenObserve vs DataDog for RUM capabilities: session replay with privacy masking, Core Web Vitals tracking (LCP, INP, CLS), error detection, and SQL-based user analytics.
Learn how incident correlation transforms observability by connecting logs, metrics, traces, and alerts into actionable insights. Discover why modern engineering teams rely on correlated telemetry to reduce MTTR and eliminate blind spots in distributed systems.
Discover how OpenObserve built the "Council of Sub Agents" - eight specialized AI agents powered by Claude Code that automate end-to-end testing. Learn how we reduced feature analysis time from 60 minutes to 5 minutes, eliminated 85% of flaky tests, grew test coverage from 380 to 700+ tests, and caught a production bug before customers reported it. This deep dive reveals the architecture, real-world impact, and lessons learned from building an autonomous QA team that doesn't just automate testing - it amplifies quality.
Learn how to use OpenObserve Insights for interactive log and trace analysis. Identify root causes in 60 seconds with dimension analysis. Real examples, step-by-step guides, and troubleshooting tips.
Cloud native was promised to be simple, yet observability has become a massive tax on both budgets and engineering time. Our new CRO, Shani Shoham, shares why he’s joining OpenObserve to break the cycle of expensive complexity and operational toil.
Hands-on comparison of DataDog vs OpenObserve pipelines. Explore real-time vs scheduled processing, VRL-based transformations, multi-destination routing, and cost-efficient, unified observability pipelines without extra infrastructure.
Does cheaper observability mean worse capabilities? We tested this assumption by sending identical telemetry from a 16-service OpenTelemetry demo to both DataDog and OpenObserve simultaneously. The results: DataDog costs $174/day vs OpenObserve's $3/day-98% cost savings without sacrificing visibility. Evereve achieved similar results: 90% cost reduction, platform consolidation, and unlimited instrumentation after migrating from DataDog.
Open source DataDog alternative IAM: SSO, RBAC, multi-tenancy included. Action-based permissions across 28+ modules. Compare $0.50/GB vs per-host pricing.
Hands-on comparison of DataDog and OpenObserve for alerting and monitoring. DataDog's proprietary monitor syntax and per-custom-metric charges vs OpenObserve's SQL-based unified alerts, composite conditions with joins, and Jinja2-templated notifications. Real test data shows custom metric pricing impact on alert coverage versus flat-rate pricing with comprehensive alerting capabilities.
