# Open-Source, Unified Observability for the AI Era > High-performance, unified observability for the AI era. 140x lower storage cost. Source: https://openobserve.ai/ --- High-performance, unified observability for the AI era. 140x lower storage cost. - [Get Started Free](https://cloud.openobserve.ai/web/login/) - [Talk to a Human](/demo/) ## Four reasons teams switch ### Built for scale, priced for reality Columnar Parquet storage delivers roughly 40x compression and up to 140x lower storage costs than Elasticsearch. Written in Rust on the DataFusion query engine, OpenObserve queries a petabyte of data in about 2 seconds. ### One platform, every signal Logs, metrics, traces, real user monitoring, and session replay live in a single tool with one query language. No stitching together five separate systems, no per-tool learning curve. ### Nothing hidden, nothing locked in The full codebase is open source under AGPL-3.0: audit it, extend it, self-host it. Built on open standards like OpenTelemetry so your data is never trapped in a proprietary format. ### Your infrastructure, your rules Run OpenObserve as a single binary, a Helm chart in Kubernetes, or fully managed on OpenObserve Cloud. Bring your own storage: local disk, S3, GCS, MinIO, or Azure Blob. ## Complete systemic context, auto-correlated ## Every signal, one platform ### Petabyte-scale log search Ingest and query trillions of log lines with familiar SQL: no indexing tax, no surprise storage bills. ### Metrics Store and query Prometheus-compatible metrics at any scale, with the same engine powering your logs. ### Distributed tracing Follow every request across services with full trace context, auto-correlated with logs and metrics. ### Build dashboards in minutes Drag-and-drop panels across every signal, or bring your existing Grafana boards straight over. ### Alerts Set real-time and scheduled alerts on logs, metrics and traces, and route notifications to Slack, PagerDuty, email and more. ### AI SRE agent An always-on agent that triages incidents, correlates root cause, and drafts the postmortem for you. ### AI assistant Ask natural-language questions across every log, metric, and trace. Get an answer, not another query language. ### LLM Observability Trace prompts, completions, token usage and cost across your LLM applications to debug quality and spend. ### SLOs Define and monitor service-level objectives to ensure consistent performance. ### Real user monitoring See exactly how real users experience your app: session traces, Core Web Vitals, and errors, correlated with backend traces. ### Synthetic monitoring Proactively monitor your applications and services from outside-in. ## One platform, every workload Correlate between environments and signals across every source with the OpenObserve built-in correlation engine. ### Full-stack visibility across every cluster Deploy the collector with one command. Container logs, cluster events and metrics arrive together, plus zero-code traces for workloads already running. ### Sixteen AWS services in one click A single CloudFormation stack deploys every service you tick, with parameters pre-filled. No per-service glue code, no three separate bills. ### GCP logs with no agent to run A Log Router sink pushes straight into OpenObserve over Pub/Sub. Nothing to install, nothing to keep patched. ### Azure telemetry without Log Analytics pricing One ARM template builds the whole Event Hub path. After that you point Activity Logs at it and stop paying per-GB to read your own data. ### Flow data at full fidelity, not sampled NetFlow, sFlow and SNMP kept whole. The packet you sampled away is always the one the incident needed. ### Query latency traced back to the statement Engine metrics and slow query logs collected through the OpenTelemetry Collector, sitting next to the application traces that triggered them. ### Native OTLP, no proprietary agent Point any OpenTelemetry exporter at OpenObserve, collector or SDK, HTTP or gRPC. Your instrumentation stays vendor-neutral, so switching cost stays near zero. ### Token spend and answer quality on one timeline One installer wires up the SDK and instrumentor. Every completion then carries cost, latency and eval outcome, so a quality regression is as visible as a latency spike. ### Real sessions stitched to backend traces A user reports a slow checkout. Open the session replay, then follow the same trace into the service that stalled. Synthetics catch it before they report it. ## Get started in one command ```bash docker run \ -p 5080:5080 \ -e ZO_ROOT_USER_EMAIL="root@example.com" \ -e ZO_ROOT_USER_PASSWORD="Complexpass#123" \ o2cr.ai/openobserve/openobserve:latest ``` Then open localhost:5080 and sign in with the credentials above. ## Measured against industry leaders Same telemetry, same workloads, one platform. Every number is OpenObserve against a named vendor - not an industry average. - **10x faster performance** vs Dynatrace — Dashboards and queries return in milliseconds, not minutes. - **8x more cost-effective** vs Datadog — Logs, metrics and traces on one bill instead of three. - **140x storage efficiency** vs Elastic — Keep a year of history without a year's worth of bill. - **30x compute efficiency** vs New Relic — Index everything. Stop sampling away the incident you need. - [Calculate your savings](/datadog_pricing_comparison/) - [See all comparisons](/comparison/) ## What teams say running OpenObserve Correlate between the environments and signals across various sources with OpenObserve built-in correlation engine. > OpenObserve helped us migrate from Datadog in under an hour... reducing observability costs by 4x. > — Debo, CEO, DevZero > Today, I think we run our entire production logs through OpenObserve... it's been a phenomenal journey so far. > — Shailesh Mangal, CTO, Decklar > The costs only seem to be skyrocketing... OpenObserve solves that by keeping the entire cost contained. > — Shashank Tiwari, CEO & Co-founder, Uno.ai