AZONE
AI-powered · Zero-blindspots · Observability · Networking · Engineering
An AI Native Observability platform that unifies Metric, Log, and Trace collection on an open-source foundation and uses an internal LLM to automatically analyze incidents down to their root cause. Not a point solution, but unified observability that connects everything from data collection to AI-driven root cause analysis (RCA) — an AI Agent takes over the analysis humans used to do by hand, shortening MTTR.
Introduction Video
Product Overview
Unified Data Collection
Collect Metric, Log, and Trace in one place on Prometheus and OpenTelemetry
AI Root Cause Analysis (RCA)
An internal LLM automates analysis from anomaly detection through RCA report generation
Tenant-Based Isolation
Permission-scoped data isolation for a secure multi-tenant operating environment
Automatic Ontology Mapping
Automatically maps server locations and connectivity for accurate incident assessment
MSA and cloud migration are expanding observability blind spots. Siloed tools, slow manual correlation, false positives and misses from fixed thresholds, operations dependent on senior engineers — scattered tools never reach the root cause. AZONE unifies the flood of telemetry and lets AI take over the analysis.
Open-Source Collect · Store · Analyze Architecture
Collect
Prometheus (Metric) · OpenTelemetry (Log, Trace) — standards-based agent collection with no vendor lock-in
Store · LGTM
Loki, Grafana, Tempo, Mimir — unified Metric/Log/Trace storage with a single query layer
Long-Term Storage
MinIO Object Storage — S3-compatible long-term archival with cost-efficient retention
AI Analysis Engine
vLLM-based LLM on internal GPU servers combined with ML models — real-time anomaly detection and root cause analysis
Five Strengths Unique to AZONE
Unified Perspective
Unlike point solutions, Metric, Log, and Trace are analyzed in a single engine
Tenant-Based Isolation
Unlike shared infrastructure, permission-scoped data isolation comes standard
Automatic Ontology Mapping
Unlike manual configuration, server locations and connectivity are mapped automatically
Hallucination Elimination
Unlike guess-driven LLMs, hallucinations are suppressed by grounding in Ontology relationships
ML + LLM Hybrid
Unlike single-model approaches, fast ML detection is combined with deep LLM analysis
Key Features
Custom Dashboards
Drag-and-drop widget composition — automatically converts existing Grafana templates into AZONE dashboards, with reusable templates per team and service
Intelligent Anomaly Detection
Automatically learns Monday–Sunday day-of-week baselines for every Metric — real-time boundary-breach detection, with weekday and time-of-day patterns minimizing false positives
Natural-Language Related-Metric Search
Search in plain language, like 'memory-related metrics' — click a metric to see its meaning and rise/fall scenario explanations
Ontology Topology
Automatic service-relationship mapping plus manual domain-structure enrichment — trace failure propagation paths along the connections
Metric, Trace & Log Drill-Down
Per-label breakdown analysis, span-level call and query tracing, direct log-to-trace linking
Profile Drill-Down
Pyroscope-based CPU and memory analysis at the function level — Compare Flame Graphs before and after an incident
GPU Monitoring
Standards-based collection via OpenLit and DCGM Exporter — utilization, memory, temperature, and power plus SM, Tensor Core, and NVLink — correlating LLM inference latency with GPU load
Anomaly Reports · Automated RCA
Synthesizes anomalous metrics, error traces, and error logs to automatically derive the root cause — Generate RCA Report produces a report instantly, shareable as PDF or Markdown
Competitive Landscape — Unified + AI Native Positioning
| Dimension | Legacy Monitoring Solutions | AZONE |
|---|---|---|
| Observation scope | Partial observation centered on point solutions | Unified analysis across Metric, Log, and Trace |
| Anomaly detection | Fixed rules and threshold-based alerts | Day-of-week baseline learning + ML anomaly detection |
| Root cause analysis | Root cause analysis left to the engineer | Automated from anomaly detection through the RCA report |
| AI foundation | - | On-prem LLM on internal GPUs with vLLM · Ontology-grounded judgment |
Use Scenarios (5 USE CASES)
Real-Time Failure Prediction → Automated RCA
SSE prediction alert → anomaly detection report → RCA report (cause, propagation path, recommended actions) — completed within minutes
AI Semantic Search
Natural-language queries like 'resources related to payment latency' → recommended related resources and logs → drastically shorter initial diagnosis time
Topology Impact Analysis
Anomalous nodes highlighted on the digital-twin graph → trace upstream/downstream dependencies → prioritize the response
Deep Dive to Pinpoint the Root Metric
Metric decomposition and per-label queries reveal deviation from baseline → confirm the causal metric on numerical evidence
Continuous Profiling
When infrastructure metrics look normal but responses are slow — diagnose code- and function-level bottlenecks with flame-graph comparison
There are two adoption paths — customers who already run an LGTM/Prometheus/OTel stack can integrate immediately with no additional infrastructure (fast PoC), while others adopt in phases starting from building the observability foundation. Going forward, the primary consumer of telemetry will be AI Agents, not humans — AZONE is designed on the standard (OpenTelemetry) and on a store Agents can reason over well (Prometheus), leading the shift from Dashboard-centric to Agent-centric Observability.