Observability, Reimagined for the AI Era
Observability 3.0 brings AI-native, unified infrastructure, application, and LLM observability together in a single platform.
Move from reactive firefighting to proactive, autonomous operations. 140x lower storage costs. Zero database management.
Legacy stacks weren't built for the AI era
Legacy observability solutions were designed for static infrastructure. They cannot manage the telemetry volume of modern AI workloads, forcing customers onto data diets that kill the context needed for root-cause analysis.
Siloed Tooling
Separate platforms for LLM observability, incident triage, and front-end monitoring, each with its own instrumentation, UI, and ops overhead.
Reactive, not proactive
Incidents are discovered by users before engineers, because legacy tools alerting only triggers when something is broken.
Escalating costs
High-volume LLM telemetry forces costs to scale exponentially. This often drives organizations toward aggressive sampling that compromises data fidelity.
One platform.
All of your telemetry. Autonomous intelligence.
OpenObserve replaces your entire observability stack with a single platform handling logs, metrics, traces, real user monitoring, and LLM telemetry in one interface. No separate databases, no data diets, no stitching tools together.
- Service Catalog
- Anomaly Detection
- O11y 4.0
One platform.
All of your telemetry. Autonomous intelligence.
OpenObserve replaces your entire observability stack with a single platform handling logs, metrics, traces, real user monitoring, and LLM telemetry in one interface. No separate databases, no data diets, no stitching tools together.
- Service Catalog
- Anomaly Detection
- O11y 4.0
Three interconnected AI capabilities
Each capability is powerful on its own. Together they create a self-healing, autonomous observability loop.
Anomaly detection
Early warning signals before an incident occurs. Proactive alerts in your existing workflow so your team can respond, not react.
Statistical baseline modeling
Correlated signal detection
Alert fatigue reduction
Works across all telemetry types
AI SRE Autonomous
An autonomous layer that analyzes telemetry context, identifies root cause, and recommends or takes corrective action automatically.\n\n
Root cause analysis at machine speed
Context-aware remediation
Incident summarization
Integrates with existing runbooks
LLM observability
Extend OpenObserve's telemetry pipeline to cover prompt monitoring, eval tracking, and generative AI performance.
Prompt & response monitoring
Token usage and cost tracking
Eval and quality tracking
Model performance benchmarking
The Tools You Need for Observability 3.0
Your roadmap for implementing and scaling modern observability.
IntegrationsUniversal Integrations
The whole picture
ComparisonNext-Gen Observability
Beyond the basics
PricingPredictable Pricing
No hidden feesBuilt for Scale
High performance
Architecture DiagramRadical Savings
Do the mathSavings CalculatorFAQs
Stop firefighting. Start shipping.
Move from reactive incident response to autonomous operations with Observability 3.0.
