OpenObserve is an open-source, unified observability platform for the AI era, officially positioned as high-performance unified observability, claiming storage costs can be as low as 140 times lower. It is designed for telemetry data such as logs, metrics, and traces, emphasizing unified collection, correlation, and analysis of signals across all layers in the AI era.
Purpose and positioning: OpenObserve aims to be a one-stop observability platform, allowing logs, metrics, and traces from different environments and sources to enter a unified correlated view. The official description emphasizes its open-source nature, scalability, and no vendor lock-in, allowing users to use their own buckets and collectors.
Main capabilities:
- Logs: PB-scale log retrieval, supporting ingestion and querying of massive log lines with familiar SQL, officially described as having no indexing tax and no unexpected storage fees.
- Metrics: Store and query Prometheus-compatible metrics, sharing the same engine as logs.
- Traces: Distributed tracing across services, with full trace context and automatic correlation with logs and metrics.
- Dashboards: Quickly build dashboards by dragging and dropping panels, and also support migrating existing Grafana panels.
- Alerts: Set real-time and scheduled alerts for logs, metrics, and traces, and route them to Slack, PagerDuty, email, etc.
- AI SRE: An always-on agent that can triage incidents, correlate root causes, and draft postmortems.
- AI assistant: Supports asking questions in natural language across logs, metrics, and traces, getting answers directly without writing a query language.
- LLM observability: Trace prompts, completions, token usage, and costs of LLM applications, for debugging quality and spend.
- SLO: Define and monitor service-level objectives.
- RUM: Real user monitoring, view session traces, Core Web Vitals, and errors, and correlate them with backend traces.
- Synthetic monitoring: Proactively monitor applications and services from the outside.
Technical characteristics: Written in Rust, based on the DataFusion engine, using columnar Parquet storage. The official description states that no indexes need to be built, so there is no duplicate payment; a single customer ingests up to 5 PB per day. The official description of its efficiency advantage is "retain one year of logs on 1/140 of the storage and 1/30 of the compute."
Usage: The official provides a getting started guide for "Getting Started with OpenObserve." Deploying the collector requires only one command; container logs, Kubernetes events, and cluster metrics can arrive together, for already running but not yet instrumentedProvides zero-code tracing for workloads. Dashboards and alert rules come with preset templates. Official support for native OpenTelemetry ingestion, as well as OTel and StatsD, with no rework required.
Comparison and applicable scenarios: The official site provides comparisons with similar products: 5–15 times faster performance compared to the Grafana stack, 8 times more cost-effective compared to Datadog, and 140 times more storage-efficient compared to Elastic. Suitable for teams that need low-cost, large-scale unified management of logs/metrics/traces, as well as organizations that need LLM and agent observability and AI-assisted troubleshooting. In case studies, it is mentioned that one team migrated from Datadog in less than an hour and reduced observability costs by 4 times; another team runs all production logs on OpenObserve.
Limitations and missing information: The official page does not provide specific pricing, plan details, platform deployment requirements, a complete list of supported data sources, or descriptions of feature limitations, and these are not currently available.
