Unified observability platform for logs, metrics, and traces with AI-native insights and object storage native architecture
Stage
Not given
Not given
Founded
Not given
2022
Based in
US
Not given
Pricing model
Freemium
Freemium
Pricing
Startup: $39/month with 100 GB per signal (logs, traces, metrics), then $0.30/GB. Browser sessions: 5,000 included, then $0.002/session. Product events: 1M included, then $5/1M. AI usage: $10/month included, then billed per model by tokens used. 14-day free trial available. Enterprise plans availabl
OSS: Free (AGPLv3 licensed). Cloud: $0.39 per GB ingested with 14-day free trial. Enterprise: Starts at $15,000 per year.
Key features
Distributed tracing with service map
Log search with structured filtering
Metrics dashboards and visualization
Error tracking with issue grouping
Session replay for browsers and mobile apps
Kubernetes monitoring and Helm chart
Alerting with multiple destinations
AI agent integration via MCP server
Web analytics and product event tracking
SQL and natural language querying for telemetry data
AI-native analysis with anomaly detection and root cause analysis
Dashboards, alerts, and distributed tracing
Service maps and error page analysis
Role-based access control and OAuth authentication
Real-time alerting based on anomaly and forecasting
Retention policies and custom data management
PromQL query support
OpenTelemetry native ingestion
Multi-tenant cluster with audit logging
What makes it different
No proprietary agents - uses standard OpenTelemetry with no vendor lock-in
Open source with FSL-1.1 license converting to Apache 2.0 after two years
Single plan with predictable usage-based pricing and no per-seat or per-host fees
AI agent integration with Model Context Protocol for automation
80x lower cost than Datadog and Splunk with predictable ingestion-based pricing
Built entirely in Rust for reliability, scalability and performance
Full data sovereignty with open formats and complete user control
AI-assisted investigation of telemetry data with anomaly detection and forecasting
Maple
Parseable