Product Overview
Release notes live under sidebar Release Notes.
In One Sentence
AI-native OpenTelemetry APM — ingest standard telemetry first, then let AI understand your system.
Two Standout Highlights
| OpenTelemetry APM | AI-native | |
|---|---|---|
| Positioning | Standard, reliable data foundation | Intelligent brain that reads telemetry directly |
| Value | See traces, metrics, topology, and alerts | Query, inspect, and diagnose through conversation |
Three Pillars of OpenTelemetry APM
① Full-featured
Built on OpenTelemetry standard ingestion, covering the full application performance monitoring lifecycle:
- Troubleshooting — traffic-light service status to spot anomalies at a glance
- Distributed tracing — full call chains; slow requests and errors are easy to find
- Service metrics — QPS, latency, error rate, JVM, and other core metrics
- Service topology — auto-generated call graphs to understand system architecture quickly
② Alerting fundamentals
Covers the basic loop for anomaly detection:
- Flexible threshold and change-detection rules
- Scheduled evaluation of core service metrics
- Alert event records for review and analysis
③ Minimal architecture
Say goodbye to bloated APM deployments:
Only 3 core components (ingest + storage + platform). One Docker command gets you running. No complex middleware stack — very low operational cost.
Three AI Highlights
① AI-native, not a bolt-on chat box
LLM capabilities are natively integrated with OpenTelemetry APM data. AI queries traces, metrics, topology, and alerts directly — instead of guessing without context.
② Rich capabilities
| Capability | What it does |
|---|---|
| Natural language query | Ask for metrics, traces, topology, and alerts in plain language |
| Service inspection | Automatically find anomalies without preset thresholds |
| Incident analysis | Synthesize multi-source data and deliver diagnostic conclusions |
| MCP openness | External agents can call platform capabilities |
③ Advanced AI architecture · multi-agent collaboration
- AI Brain understands intent and dispatches the right expert
- Digital experts each focus on query, inspection, or analysis
- Complex questions can trigger parallel multi-expert collaboration — like having an ops team on call
Why DataBuff
| Dimension | Traditional APM | DataBuff |
|---|---|---|
| AI | None or bolt-on | AI-native, reads telemetry directly |
| Deployment | Many components, heavy resources | 3 components, minimal deployment |
| Troubleshooting | Manual chart digging | Conversational intelligent analysis |
Use Cases
- You want to deploy APM quickly without maintaining a heavy platform
- You want dev/ops teams to use conversation instead of dashboards
- You need open-source, self-hosted AI ops capabilities
- You are evaluating AI-native OpenTelemetry APM for your stack