# 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?mode=signup) - [Talk to a Human](/demo/) ## Four reasons teams switch to OpenObserve Every claim is measured against a named product - not an industry average. ### Keep a year of logs on 1/140th the storage and 1/30th the compute Columnar Parquet storage, written in Rust on the DataFusion engine. There is no index to build, so there is nothing to pay for twice. ### Every layer of the stack, correlated for you Servers, network, firewalls, databases, infrastructure, applications and AI/LLM workloads all land in one store - and the built-in correlation engine links their signals before anyone opens a dashboard. ### 10,000+ enterprise deployments, 21,000+ stars, zero black boxes Open source is not a licence badge - it is a list of things you no longer have to take on trust. ### Open standards in. Standard object storage out. Ship data with whatever you already run, query it in SQL and PromQL, and keep it as open Parquet in a bucket you own. Nothing in that path is proprietary - leaving costs you a config change. ## 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. - **5x to 15x faster performance** vs Grafana stack - 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. - [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 Ray, 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