Datadog bills surprising you? OpenObserve is a free, open source observability platform replacing Datadog for logs, metrics, traces, dashboards, alerts, and RUM with 60–98% lower costs.
Learn how to implement LLM cost monitoring with OpenObserve. This hands-on guide covers token-level tracing, cost dashboards, per-user and per-model spend attribution, VRL-powered span enrichment, real-time alerting, and AI agent cost observability.
OpenTelemetry is free, but your observability backend is not. Learn practical strategies for observability cost reduction using sampling, filtering, retention, and backend architecture choices.
Learn how OpenTelemetry's GenAI Semantic Conventions bring production-grade observability to LLM workloads. A complete guide for DevOps and SRE teams covering traces, metrics, logs, and a hands-on RAG instrumentation walkthrough.
A complete guide to OpenTelemetry: what it is, how the Collector and OTLP work, and how to instrument your first service.
Learn how to add distributed tracing to LangChain and LlamaIndex apps using OpenLLMetry and the OpenTelemetry SDK, with traces flowing into OpenObserve.
What is an MCP gateway? Compare top options and learn how OpenObserve's native MCP server plugs into your AI agent stack for live observability data access.
Complete guide to AI anomaly detection in observability. Discover how machine learning algorithms detect unusual patterns, handle seasonality, and catch issues traditional thresholds miss.
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.
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.
Compare the top 10 AIOps platforms in 2026. AI-powered observability tools for autonomous operations, cost optimization, and intelligent incident response.
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.
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.
A comprehensive comparison of the top 10 observability platforms in 2026 highlighting their strengths, trade-offs, and use-cases.
A comprehensive comparison of the top 10 open source observability platforms in 2026 highlighting their strengths, trade-offs, and use-cases.
OpenObserve Kubernetes Operator brings observability as code to platform teams. Manage alerts, pipelines, and functions as Kubernetes resources with GitOps workflows.
Automatically extract patterns from millions of logs in seconds. Learn how OpenObserve's log pattern analysis helps SREs reduce incident investigation time from 30 minutes to under 5 minutes.
DataDog vs OpenObserve APM comparison: $120/day LLM charge, SQL trace dashboards, OTel native, service dependency mapping, and 60-90% cost savings with real data.
