# One platform for every signal, everywhere you run it > Enterprise-grade observability platform with 140x lower storage costs. Unified logs, metrics, and traces with SSO, RBAC, and SOC2. Scale effortlessly. Source: https://openobserve.ai/platform/ --- Logs, metrics, traces, frontend and synthetic monitoring in one Rust-built engine - auto-correlated across your whole stack. Cloud, your own bucket, on-prem or fully air-gapped. ## 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. ## Explore the OpenObserve Platform A modern, scalable architecture designed for high performance and low cost ## 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. ## Index 100%. Query it in seconds. Sampling is a cost workaround that costs you the incident. We index everything and stay fast - there is no hot tier, cold tier or re-indexing step. - **5 PB/day** largest single customer ingest - **95×** compression on ingested volume - **1 TB/day** on a single node ### Written in Rust Predictable memory, no GC pauses under load ### Columnar Parquet + high compression Scan the two columns you asked for, not the row ### Object storage native Stateless nodes, no replication to manage ### Federated search Query across regions and clouds without egress bills - [Try the capacity calculator →](/pricing/#cost-estimator-volume) ## 12,400 alerts. One incident. One root cause. With separate tools this outage would have paged four teams with thousands of alerts. Because every layer is in one engine, the AI SRE collapses it into a single incident with a timeline, impacted services, an executive summary and corrective actions. - Root cause across app, database, network and cloud - not just the layer you instrumented - Pivot between any signal mid-incident without writing a query - Workflows open and close the ServiceNow ticket automatically - [Explore AI SRE →](https://openobserve.ai/docs/enterprise-setup/sre-agent/) ## Questions evaluators actually ask What the OpenObserve platform includes and how it differs from a pieced-together stack. ### What does the OpenObserve platform include? Logs, metrics, traces, real user monitoring and session replay, dashboards with 19+ chart types, alerts, incidents, SLOs, pipelines, LLM observability, and an AI SRE agent - all in one binary with one query layer (SQL and PromQL) and one storage layer on object storage. Every signal is auto-correlated, so a metric spike pivots directly into the logs and traces behind it without switching tools or rewriting queries. The platform is OpenTelemetry-native for standardized collection, and the Enterprise edition adds SSO, RBAC, audit trails, and SOC 2 Type II compliance - free up to 50 GB/day of ingestion. Deployment options span fully managed cloud, bring-your-own-bucket, on-prem, and air-gapped environments, so the same platform runs wherever your compliance requirements need it to. ### How is OpenObserve different from Datadog, Grafana, or Dash0? OpenObserve is open source (AGPL-3.0), self-hostable or fully managed, and priced per GB ingested with unlimited users. Datadog is closed source with per-host, per-metric, and per-user billing that grows unpredictably as infrastructure scales. Grafana Cloud splits signals across separate backends with separate query languages, so correlation means stitching tools together. Dash0 is OpenTelemetry-native but closed source, cloud-only, and priced per signal. OpenObserve keeps logs, metrics, and traces in one Rust-built engine with one query layer, stores everything in open Parquet format on object storage you control, and delivers up to 140x lower storage cost than Elasticsearch-based stacks, so full retention replaces sampling. Full comparisons against every major vendor are on the comparison hub. ### Where does the 140x lower storage cost come from? OpenObserve writes data as Apache Parquet on object storage (S3, GCS, Azure Blob, MinIO) rather than maintaining inverted indexes. Columnar compression on telemetry yields up to 140x lower storage cost than Elasticsearch for the same data, which is measured in the published benchmarks. ### Is OpenObserve OpenTelemetry-native? Yes. OTLP is the primary ingestion path for logs, metrics, and traces, so any OpenTelemetry-instrumented application or collector works without a proprietary agent. Prometheus remote-write, Fluent Bit, Vector, syslog, and 100+ other integrations are supported as well. ### Can I deploy OpenObserve on my own infrastructure? Yes. Run the single binary, the Helm chart on Kubernetes, or the Self-Hosted Enterprise edition (free up to 50 GB/day with SSO, RBAC, and audit trail). OpenObserve Cloud is available in US and EU regions if you prefer a managed service. ## Ready to get started? Try OpenObserve today for more efficient and performant observability. - Get Started For Free - [Schedule Demo](/demo/)