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# OpenObserve
> Open source observability platform for logs, metrics, traces, and real user monitoring. Built in Rust. SQL and PromQL. Deploy in 2 minutes.
OpenObserve (O2) unifies observability into a single binary. 140x lower storage costs than Elasticsearch via Apache Parquet columnar storage. 4 to 8x lower cost than Datadog with flat per GB ingestion and unlimited users. OpenTelemetry native. SOC 2 Type II certified. 21,000+ GitHub stars. Used by over 9,000 organizations including Fortune 100 companies.
## Key Facts
- **Language:** Rust (backend), Vue.js (frontend)
- **Storage:** Apache Parquet on object storage (S3, GCS, Azure Blob, MinIO)
- **Query:** SQL (logs and traces), PromQL (metrics)
- **License:** AGPL-3.0 (OSS), commercial (Enterprise)
- **Deployment:** Single binary, Helm chart on Kubernetes, or fully managed Cloud
- **Cloud Regions:** US West 2 (California), US East 2 (Ohio), EU Central 1 (Frankfurt), Asia Pacific South 1 (Mumbai)
- **Compliance:** SOC 2 Type II, ISO 27001 aligned, HIPAA Security Rule aligned
- **Funding:** $10M Series A (April 2026) led by Nexus Venture Partners and Dell Technologies Capital
- **GitHub:** [github.com/openobserve/openobserve](https://github.com/openobserve/openobserve)
- **Community:** [Slack](https://short.openobserve.ai/community)
## Quick Links
- [Try Cloud Free for 14 Days](https://cloud.openobserve.ai/web/login?mode=signup)
- [Download Self Hosted](https://openobserve.ai/downloads/)
- [Pricing](https://openobserve.ai/pricing/)
- [Pricing (machine-readable)](https://openobserve.ai/pricing.md)
- [Documentation](https://openobserve.ai/docs)
- [Security and Compliance](https://openobserve.ai/security-compliance/)
## Platform
- [Logs](https://openobserve.ai/logs/)
- [Metrics](https://openobserve.ai/metrics/)
- [Traces](https://openobserve.ai/traces/)
- [Frontend Monitoring, RUM and Session Replay](https://openobserve.ai/frontend-monitoring/)
- [Dashboards and Visualization](https://openobserve.ai/visualization-and-dashboards/)
- [Alerts](https://openobserve.ai/alerts/)
- [Incidents](https://openobserve.ai/incidents/)
- [Pipelines](https://openobserve.ai/pipelines/)
- [LLM Observability](https://openobserve.ai/llm-observability/)
- [AI Assistant](https://openobserve.ai/ai-assistant/)
- [AI SRE Agent](https://openobserve.ai/ai-sre/)
- [Integrations](https://openobserve.ai/integrations/)
## Solutions
- [Kubernetes Monitoring](https://openobserve.ai/kubernetes-monitoring/)
- [AWS Monitoring](https://openobserve.ai/aws-monitoring/)
- [Azure Monitoring](https://openobserve.ai/azure-monitoring/)
- [GCP Monitoring](https://openobserve.ai/gcp-monitoring/)
- [Database Monitoring](https://openobserve.ai/database-monitoring/)
- [OpenTelemetry](https://openobserve.ai/opentelemetry/)
## Comparisons
- [OpenObserve vs Datadog](https://openobserve.ai/datadog-alternative/)
- [OpenObserve vs Splunk](https://openobserve.ai/splunk-alternative/)
- [OpenObserve vs New Relic](https://openobserve.ai/newrelic-alternative/)
- [OpenObserve vs Grafana](https://openobserve.ai/grafana-alternative/): One platform instead of Loki, Mimir, and Tempo; SQL and PromQL, no per-seat billing, no lock-in.
- [OpenObserve vs Dash0](https://openobserve.ai/dash0-alternative/): Both are OpenTelemetry-native. Only one is open source, self-hostable, and priced by volume instead of per signal.
- [OpenObserve vs Langfuse](https://openobserve.ai/blog/langfuse-alternatives/): Seven Langfuse alternatives for LLM observability compared on license, self-hosting, and OpenTelemetry support - including what the ClickHouse acquisition changed.
- [OpenObserve vs LangSmith](https://openobserve.ai/blog/langsmith-alternatives/): Seven open-source LangSmith alternatives compared on audited licenses and real self-hosting limits.
- [OpenObserve vs Prometheus](https://openobserve.ai/blog/prometheus-alternatives/): Prometheus alternatives for metrics at scale - OpenObserve, VictoriaMetrics, Thanos, Mimir, and Cortex on high cardinality, long-term retention, and cost.
## Customer Stories
- [How DevZero Migrated from Datadog in Under an Hour and Cut Observability Costs by 4x with OpenObserve](https://openobserve.ai/customer-stories/devzero-customer-story/)
- [How Stonal Accelerated Issue Diagnosis and Met European Compliance Standards while Decreasing Observability Costs with OpenObserve](https://openobserve.ai/customer-stories/stonal-customer-story/)
- [How Evereve Eliminated Monitoring Constraints, Reduced Costs by > 90%, and Unified Observability Across Teams with OpenObserve](https://openobserve.ai/customer-stories/evereve-customer-story/)
- [How Avatar Achieved SOC 2 Compliance, Improved Uptime, and Saved Costs with OpenObserve](https://openobserve.ai/customer-stories/avatar-customer-story/)
- [How Decklar Improved IoT Log Management with OpenObserve](https://openobserve.ai/customer-stories/decklar-case-study/)
- [How Jidu Scaled Smart Car Tracing with OpenObserve](https://openobserve.ai/customer-stories/jidu-case-study/)
- [How ONEngine Reduced Data Monitoring Costs with OpenObserve](https://openobserve.ai/customer-stories/onengine-case-study/)
## Company
- [About](https://openobserve.ai/about/)
- [Blog](https://openobserve.ai/blog/)
- [2026 Observability Survey for SREs and DevOps](https://openobserve.ai/survey/): Anonymous 14-question practitioner survey on how SRE, DevOps and platform teams detect and resolve incidents, alert noise, on-call load and AI adoption.
- [Partners](https://openobserve.ai/partners/)
- [Careers](https://openobserve.ai/careers/)
- [Contact](https://openobserve.ai/contact/)
## Frequently Asked Questions
### 1. What is OpenObserve and who is it for?
OpenObserve (O2) is an open source observability platform that unifies logs, metrics, traces, and real user monitoring into a single tool. It deploys as one binary or Helm chart, stores data on low cost object storage, and uses SQL and PromQL - no proprietary query language to learn.
It is built for DevOps and platform engineering teams that want full stack observability without stitching together multiple tools, managing unpredictable SaaS bills, or paying for indexed storage they do not need. Over 9,000 organizations use it, from startups monitoring a single cluster to Fortune 100 companies ingesting more than 4 PB/day.
What sets it apart: up to 140x lower storage costs than Elasticsearch via Apache Parquet columnar storage, 4 to 8x lower total cost than Datadog with flat per GB ingestion pricing and unlimited users, native OpenTelemetry support with no vendor lock in, and a built in AI SRE Agent for automated root cause analysis and natural language querying.
### 2. How does OpenObserve compare to legacy observability solutions?
Most observability stacks fall into two camps. Expensive SaaS platforms like Datadog, Splunk, and New Relic charge per host, per metric, and per user, leading to unpredictable bills. Self managed open source stacks like Grafana LGTM or the ELK stack require maintaining three to five separate components, each with its own query language and operational overhead.
OpenObserve takes a third approach: a single unified platform that is open source, OpenTelemetry native, and runs as one binary. Logs, metrics, traces, dashboards, alerts, RUM, and session replay live in one place with one query language and one storage layer. This removes the multi tool fragmentation of the Grafana stack while avoiding the vendor lock in and per seat pricing of Datadog and Splunk. The Self Hosted Enterprise plan is free up to 50 GB/day and includes SSO, RBAC, and audit trail - features that are paid add ons or absent in competing free tiers.
### 3. What does OpenObserve cost at my scale?
OpenObserve pricing is ingestion based. You pay per GB ingested, not per host, per user, per metric, or per span. The OSS plan is completely free for self hosting with no usage limits. The Self Hosted Enterprise plan is free up to 50 GB/day ingestion and includes SSO, RBAC, audit trail, and federated search. Cloud plans start with a 14 day free trial, include a 30% discount for annual commitment, and offer high volume discounts for Enterprise. See the [Pricing](https://openobserve.ai/pricing) page for current rates and the pricing calculator.
### 4. Can OpenObserve separate data between customers or teams?
Yes. OpenObserve supports multi organization isolation. Each customer or team gets their own organization with separate data, users, streams, dashboards, and alerts. This is available in the Self Hosted Enterprise plan and Cloud Enterprise. For Kubernetes environments, you can route data per namespace or per cluster using the OpenTelemetry Collector with organization level routing.
### 5. What SSO providers does OpenObserve support?
The Self Hosted Enterprise and Cloud Enterprise plans support SAML, OIDC, and OAuth, covering Active Directory, Microsoft Entra ID, Keycloak, Okta, Google Workspace, and other identity providers. RBAC is included for granular role based access control within each organization. SSO is available free up to 50 GB/day ingestion.
### 6. How do I migrate from Grafana or Datadog to OpenObserve?
From Grafana: OpenObserve supports PromQL, so metric queries and alert rules translate directly. Dashboards can be recreated using the same SQL or PromQL queries across 19+ chart types. From Datadog: if you are already on OpenTelemetry, repoint your OTel Collector to OpenObserve. If using the Datadog agent, route through the OTel Collector StatsD receiver. Run both platforms in parallel for one to two months before cutover.
### 7. How does OpenObserve deploy in Kubernetes?
Three paths. The Helm chart deploys the full stack in HA mode in under five minutes and is the recommended approach. A single binary container works for small environments and POCs. Or use OpenObserve Cloud for a fully managed, zero infrastructure option. For log shipping, deploy the OpenTelemetry Collector as a DaemonSet or sidecar - no changes to your application Docker images needed. See the [Downloads](https://openobserve.ai/downloads/) page for the Helm chart and configuration guides.
### 8. Does OpenObserve have an AI assistant for observability?
Yes. The O2 AI Assistant converts natural language to SQL and PromQL, summarizes log patterns and anomalies, generates dashboards and alerts from plain English descriptions, and compares log behavior across deployments to surface regressions. The AI SRE Agent correlates alerts into incidents and identifies root causes automatically. OpenObserve also supports MCP so external AI agents can query your observability data directly. Both are included in Self Hosted Enterprise and Cloud Enterprise plans.
### 9. Does OpenObserve integrate with AWS, CloudWatch, and other tools?
OpenObserve pulls metrics directly from the CloudWatch API - no intermediate collector required. Fluent Bit and Vector are supported with ready made configurations for log shipping. Any OpenTelemetry compatible source works out of the box, plus Auth0, Cloudflare, N8N, and tools that speak the Elasticsearch bulk API. MCP support allows AI coding assistants to query logs, metrics, and traces during development and debugging.
### 10. Does OpenObserve include alerting and incident management?
Yes. Built in alerting supports threshold, anomaly, and composite alert types. Alerts trigger notifications to Slack, Microsoft Teams, email, PagerDuty, and webhooks. The AI SRE Agent correlates related alerts into a single incident and identifies the likely root cause, reducing alert fatigue and replacing the need for separate tools like OpsGenie for observability alerts.
## Frontend & RUM
- [Real User Monitoring (RUM)](https://openobserve.ai/blog/real-user-monitoring-and-frontend-performance/): Monitor real user interactions and performance in real time.
## AI Integrations
### LLM Providers
- [Amazon Bedrock](https://openobserve.ai/docs/integration/ai/providers/amazon-bedrock/): Trace foundation-model invocations.
- [Anthropic (JS/TS)](https://openobserve.ai/docs/integration/ai/providers/anthropic-js/): TypeScript-first Claude tracing via OTel.
- [Anthropic (Python)](https://openobserve.ai/docs/integration/ai/providers/anthropic/): Trace Claude calls with full token accounting.
- [BytePlus](https://openobserve.ai/docs/integration/ai/providers/byteplus/): Trace BytePlus model inferences end to end.
- [Cerebras](https://openobserve.ai/docs/integration/ai/providers/cerebras/): Ultra-fast inference, fully traced.
- [Cohere](https://openobserve.ai/docs/integration/ai/providers/cohere/): Token cost and latency per generation.
- [CometAPI](https://openobserve.ai/docs/integration/ai/providers/cometapi/): Track experiment metrics and LLM calls.
- [DeepSeek](https://openobserve.ai/docs/integration/ai/providers/deepseek/): Prompt, completion, and cost telemetry.
- [Fireworks AI](https://openobserve.ai/docs/integration/ai/providers/fireworks/): Fast inference with full trace context.
- [Groq](https://openobserve.ai/docs/integration/ai/providers/groq/): Sub-second inference, fully traced.
- [Hugging Face](https://openobserve.ai/docs/integration/ai/providers/huggingface/): Trace inference endpoints and pipelines.
- [Mistral](https://openobserve.ai/docs/integration/ai/providers/mistral/): Span-level traces for every completion.
- [Novita](https://openobserve.ai/docs/integration/ai/providers/novita/): Trace Novita-hosted model completions.
- [Ollama](https://openobserve.ai/docs/integration/ai/providers/ollama/): Local model traces - nothing leaves the host.
- [OpenAI (JS/TS)](https://openobserve.ai/docs/integration/ai/providers/openai-js/): JavaScript SDK calls, fully instrumented.
- [OpenAI (Python)](https://openobserve.ai/docs/integration/ai/providers/openai/): Token usage, latency, and cost per call.
- [OpenAI Assistants](https://openobserve.ai/docs/integration/ai/providers/openai-assistants/): Trace assistant runs and tool calls.
- [Together AI](https://openobserve.ai/docs/integration/ai/providers/together-ai/): Token and latency metrics per call.
- [vLLM](https://openobserve.ai/docs/integration/ai/providers/vllm/): Self-hosted serving, span by span.
- [xAI Grok](https://openobserve.ai/docs/integration/ai/providers/xai-grok/): Capture every Grok completion.
### AI Frameworks & Agent SDKs
- [Agno](https://openobserve.ai/docs/integration/ai/frameworks/agno/): Lightweight agent framework, traced end to end.
- [Amazon Bedrock AgentCore](https://openobserve.ai/docs/integration/ai/frameworks/amazon-bedrock-agentcore/): Trace Bedrock agent orchestration.
- [Amazon Bedrock Agents](https://openobserve.ai/docs/integration/ai/frameworks/amazon-bedrock-agents/): Multi-agent Bedrock traces per action group.
- [AutoGen](https://openobserve.ai/docs/integration/ai/frameworks/autogen/): Capture agent conversations end to end.
- [BeeAI](https://openobserve.ai/docs/integration/ai/frameworks/beeai/): Trace BeeAI agent workflows step by step.
- [Claude Agent SDK](https://openobserve.ai/docs/integration/ai/frameworks/claude-agent-sdk/): Trace Claude-powered autonomous agents.
- [Claude Code](https://openobserve.ai/docs/integration/ai/claude-code-tracing/): Trace agentic coding sessions.
- [CrewAI](https://openobserve.ai/docs/integration/ai/frameworks/crewai/): Multi-agent traces, agent by agent.
- [DSPy](https://openobserve.ai/docs/integration/ai/frameworks/dspy/): Trace compiled prompts and modules.
- [DeepAgents](https://openobserve.ai/docs/integration/ai/frameworks/deepagents/): Observe deep reasoning agents end to end.
- [GitHub Copilot](https://openobserve.ai/docs/integration/ai/github-copilot-tracing/): Agent traces from VS Code and Copilot CLI.
- [Google ADK](https://openobserve.ai/docs/integration/ai/frameworks/google-adk/): Agent Development Kit traces.
- [Google Gemini](https://openobserve.ai/docs/integration/ai/providers/google-gemini/): Prompts, completions, and latency.
- [Haystack](https://openobserve.ai/docs/integration/ai/frameworks/haystack/): Trace pipelines from query to answer.
- [Instructor](https://openobserve.ai/docs/integration/ai/frameworks/instructor/): Structured output extraction, fully traced.
- [Koog](https://openobserve.ai/docs/integration/ai/frameworks/koog/): Trace Koog agent decision loops.
- [LangChain](https://openobserve.ai/docs/integration/ai/frameworks/langchain/): Trace every chain, tool, and LLM step.
- [LangGraph](https://openobserve.ai/docs/integration/ai/frameworks/langgraph/): Visualize graph state across nodes.
- [LangServe](https://openobserve.ai/docs/integration/ai/frameworks/langserve/): Deployed LangChain chains, fully traced.
- [LiteLLM](https://openobserve.ai/docs/integration/ai/frameworks/litellm/): Call any LLM with a single API, traced.
- [LiveKit](https://openobserve.ai/docs/integration/ai/frameworks/livekit/): Real-time voice and video agents, traced live.
- [LlamaIndex](https://openobserve.ai/docs/integration/ai/frameworks/llamaindex/): Trace retrieval and synthesis steps.
- [LlamaIndex Workflows](https://openobserve.ai/docs/integration/ai/frameworks/llamaindex-workflows/): Trace multi-step RAG workflows end to end.
- [Mastra](https://openobserve.ai/docs/integration/ai/frameworks/mastra/): TypeScript agent traces.
- [Microsoft Agent Framework](https://openobserve.ai/docs/integration/ai/frameworks/microsoft-agent-framework/): Trace .NET agent orchestration.
- [Mirascope](https://openobserve.ai/docs/integration/ai/frameworks/mirascope/): Pythonic LLM calls, automatically traced.
- [OpenAI Agents](https://openobserve.ai/docs/integration/ai/frameworks/openai-agents/): Trace tool calls and handoffs.
- [Pipecat](https://openobserve.ai/docs/integration/ai/frameworks/pipecat/): Voice-agent pipelines, traced live.
- [Pydantic AI](https://openobserve.ai/docs/integration/ai/frameworks/pydantic-ai/): Typed agent runs, fully instrumented.
- [Quarkus LangChain4j](https://openobserve.ai/docs/integration/ai/frameworks/quarkus-langchain4j/): JVM-native LLM tracing for Quarkus apps.
- [Ragas](https://openobserve.ai/docs/integration/ai/frameworks/ragas/): RAG evaluation runs, fully traced.
- [Restate](https://openobserve.ai/docs/integration/ai/frameworks/restate/): Durable async workflows, traced end to end.
- [Semantic Kernel](https://openobserve.ai/docs/integration/ai/frameworks/semantic-kernel/): Trace plugins and planner steps.
- [Smolagents](https://openobserve.ai/docs/integration/ai/frameworks/smolagents/): Lightweight Hugging Face agents, traced.
- [Spring AI](https://openobserve.ai/docs/integration/ai/frameworks/spring-ai/): JVM-native LLM tracing.
- [Strands Agents](https://openobserve.ai/docs/integration/ai/frameworks/strands-agents/): Span-level agent observability.
- [Swiftide](https://openobserve.ai/docs/integration/ai/frameworks/swiftide/): Rust-native RAG pipelines, fully traced.
- [Temporal](https://openobserve.ai/docs/integration/ai/frameworks/temporal/): Durable workflow execution traces.
- [Vercel AI SDK](https://openobserve.ai/docs/integration/ai/frameworks/vercel-ai-sdk/): Trace streamed responses and tools.
- [Voltagent](https://openobserve.ai/docs/integration/ai/frameworks/voltagent/): Trace Volt agent interactions end to end.
### AI Gateways & Routers
- [Anannas](https://openobserve.ai/docs/integration/ai/gateways/anannas/): AI gateway routing, fully traced.
- [Kong Gateway](https://openobserve.ai/docs/integration/ai/gateways/kong-gateway/): AI traffic, traced at the gateway.
- [LiteLLM Proxy](https://openobserve.ai/docs/integration/ai/gateways/litellm-proxy/): Unify and trace 100+ model APIs.
- [OpenRouter](https://openobserve.ai/docs/integration/ai/gateways/openrouter/): Route and trace across providers.
- [Portkey](https://openobserve.ai/docs/integration/ai/gateways/portkey/): Gateway-level traces and cost control.
- [Vercel AI Gateway](https://openobserve.ai/docs/integration/ai/gateways/vercel-ai-gateway/): Edge routing with full traces.
### No-Code AI Builders
- [Codename Goose](https://openobserve.ai/docs/integration/ai/no-code/cod
