Getting Started with OpenObserve
A practical, attribute-by-attribute guide to OpenTelemetry's GenAI semantic conventions: the gen_ai.* namespace for spans, metrics, and events, stability levels, a worked instrumentation example, and common migration mistakes.
A complete comparison of the top LLM observability tools in 2026, open source and commercial, covering tracing, evaluation, cost tracking, and unified infrastructure monitoring for AI engineering teams.
LLM observability explained for engineers: what it is, why traditional APM misses LLM failures, the three layers (computational, semantic, agentic), the core signals to track in production, and how OpenTelemetry GenAI semantic conventions standardize instrumentation.
Compare the best log visualization tools in 2026: OpenObserve, Kibana, Grafana Loki, Datadog, and Splunk. Covers AI-assisted analysis, dashboard quality, and cost.
A comprehensive comparison of open source LLM observability tools in 2026: OpenObserve, Langfuse, Helicone, and Arize Phoenix. Covers prompt tracing, cost tracking, evaluation frameworks, and unified infrastructure monitoring.
Compare the top 10 AIOps platforms in 2026 — features, pricing, and use cases for autonomous operations, alert correlation, root cause analysis, and intelligent incident response.
"AI-first" is easy to say and hard to prove. At OpenObserve we ship two AI features to users - O2 Assistant and the AI SRE - and run our own engineering shop the same way, with DocGen writing our docs and the Council of Agents writing and healing our end-to-end tests. This is the story of moving both out of "a human runs this locally" and into CI, where they now fire on their own: giving agents a browser, an ingestion API, and a real running instance so they can build, seed, click, and verify the tedious work instead of asking a person to grind through it.
How to make AI agents observable in production: OTel instrumentation, telemetry backends, cost tracking, and trace analysis.
OpenObserve raises $10M Series A to launch Observability 3.0. Discover our AI-native platform featuring an autonomous AI SRE, LLM observability, and anomaly detection.
Learn what MCP servers are available for observability and how to connect AI assistants to logs, metrics, and traces. Includes platform comparison, real use cases, and a 2-minute setup guide.
A practical, code-first guide to instrumenting OpenAI with OpenTelemetry. Capture token usage, attribute costs by user and feature, build a cost dashboard, and alert on anomalies in OpenObserve.
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.
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.
Discover the essential LLM monitoring best practices to ensure reliability, safety, and performance in production. Learn how to track hallucinations, latency, costs, and more.
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.
Learn how to monitor autonomous AI agents in production using observability best practices. Track agent behavior, logs, traces, and performance with tools like OpenTelemetry to ensure reliability, transparency, and control at scale.
Learn how AI-assisted monitoring using MCP enhances observability with intelligent alerts, anomaly detection, and automated insights for faster incident response.
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.
AI Observability by OpenObserve
OpenTelemetry-native observability for agents and LLMs.
