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Beta: LLM Observability is now GA

For DevOps & SRE Teams

Stop firefighting.
Start preventing.

From alert to root cause in seconds without switching tools. aiAxonIQ delivers correlated telemetry streams across logs, traces, and metrics so your teams ship faster and maintain 99.99% reliability.

topology.aiaxoniq.net/devops/architecture-flow
OTLP 1.3 · ClickHouse 24.8
Real-Time Observability Fabric(DevOps & SRE Teams Deployment Profile)
In-Flight: 142,800 evt/sKernel Overhead: < 0.28%Latency: 4.2ms p99
1. Telemetry Sources100% OTel
Kubernetes & eBPF48 Nodes

Zero-overhead kernel probes for TCP, HTTP/2, and DNS latency without application restarts.

eBPF socket tracing · 0.2% CPU
Services & Envoy MeshOTLP/gRPC

Automatic span injection with W3C baggage propagation across polyglot microservice runtimes.

120+ active services · W3C TraceContext
LLM Agents & MCPGenAI OTel

Tracks token consumption, prompt drift, model invocation latency, and tool execution trees.

Claude Code / Cursor tool traces
2. Unified Engine CoreClickHouse 24.8
Columnar Storage Engine12.4x zstd

Single ClickHouse backend for logs, traces, metrics, and profiles with zero storage silos.

Compression: 12.4:1Rollups: Materialized Views
Trace-to-Log Correlator< 8ms P99

Indexes span IDs directly into log metadata, eliminating manual grep during production outages.

Join Latency: 4.8msIndex: MinMax Skipping
Statistical ML & AnomalyProphet + Z-Score

Continuous IQR & seasonal forecasting to alert on statistical anomalies before customers report errors.

Precision: 98.4%False Alarms: < 0.5%
3. Autonomous ActionClosed-Loop
Instant Incident RCARAG Runbooks

Correlates telemetry with git commit history and engineering runbooks to draft instant post-mortems.

Exact code commit pinpointed
Incident DispatcherMulti-Target

Routes rich incident cards with trace IDs, flamegraphs, and error frequencies directly into Slack and PagerDuty.

Slack, PagerDuty, OpsGenie, Webhook
Autonomous RemediationSealed Write

Human-confirmed operator actions: traffic shedding, canister isolation, and canary rollbacks.

Zero unsupervised production writes
Topology Inspector:ClickHouse 24.8 Columnar Storage Cluster
Click any node above to inspect pipeline telemetry specs
Architecture: DevOps & SRE Teams Production Topology
Zero Vendor Lock-In·Standard on all aiAxonIQ plans

Problem & Solution

The challenges every devops & sre teams faces

And how aiAxonIQ solves them with unified telemetry.

✕
The Problem

Alert Fatigue & Incident Storms

Hundreds of un-deduplicated alerts firing simultaneously across disparate monitoring tools. On-call engineers are inundated with false alarms, missing the true high-severity signals and burning out during nocturnal incidents.

aiAxonIQ Solution

Intelligent composite rules, dynamic cooldown windows, and label-based silences collapse cascading alert storms into a single grouped incident dossier, automatically enriched with correlated trace IDs, runtime logs, and metric baselines.

Explore Alerting
✕
The Problem

Slow Root Cause & Context Switching

Engineering teams lose 45+ minutes jumping between separate tabs for Jaeger tracing, Prometheus dashboards, and Elasticsearch logs, painstakingly copy-pasting trace IDs and guessing time-offset correlations.

aiAxonIQ Solution

Direct trace-to-log and trace-to-metric hyperlinks powered by unified ClickHouse storage. Clicking any span instantly reveals the exact log entries, container CPU spikes, and downstream network latency from that precise microsecond.

Explore APM & Tracing
✕
The Problem

Siloed Telemetry & High Ingest Costs

Running separate collector daemons for logs, metrics, and application traces wastes CPU overhead on production nodes while fragmented query languages prevent cross-signal correlation and drive surprise billing.

aiAxonIQ Solution

One single OpenTelemetry-native pipeline backed by Apache Kafka and columnar ClickHouse rollups. Stream all telemetry through open standards with sub-second query speeds and zero vendor lock-in.

Explore Log Management

Execution Flow

How it works in practice

Your improved workflow from ingestion to resolution, step by step.

1
Correlated Alert Fires
Composite detector triggers with trace ID, correlated error logs, and metric deviations attached.
2
One-Click Trace Analysis
Jump directly to the failing distributed span with deep waterfall execution timelines.
3
Instant Log & Metric Context
Inspect synchronized structured log lines and container metrics from the exact time window.
4
Verify Fix in Real Time
Stream live p99 latency and error-rate drop directly in production dashboards.

Why it works

Engineered for the 2:00 AM PagerDuty Incident

When critical production workloads degrade in the middle of the night, engineers need immediate answers, not a maze of disconnected dashboards. aiAxonIQ links every alert directly to the responsible trace, isolates downstream bottlenecks, and surfaces correlated error logs in under three clicks. Ingestion is decoupled through Apache Kafka with dead-letter queue resilience, ensuring telemetry is safely captured even during massive traffic spikes.

Trace-to-log correlation built inKafka-backed ingest, durable and replayableSub-second ClickHouse query aggregationsZero proprietary agent footprint