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Beta: LLM Observability is now GA
Distributed Tracing

Microsecond-Accurate Distributed Tracing.
Zero Head-Sampling Blindspots.

OpenTelemetry-native distributed tracing across every microservice, database query, Kafka partition, and third-party API. Ingest 100% of production spans into ClickHouse columnar storage without destructive head-sampling or runaway billing.

Distributed tracingService mapSpan analyticsError trackingLatency profilingOTel native
telemetry.aiaxoniq.net/apm
STREAM LIVE
Trace #trace_91a0c842b109e200 OK
Latency: 24.2msSpans: 8 spansDepth: 4 levels
api-gateway · GET /api/v1/checkout24.2ms
auth-service · jwt.VerifyToken4.8ms
inventory-svc · stock.CheckAvailability8.4ms
payment-engine · stripe.ChargeIntent11.0ms

Deep Dive

Core Capabilities & Architecture

Explore how APM & Tracing instruments, processes, and correlates high-cardinality telemetry at scale.

Tracing

Follow Every Transaction Across Polyglot Services

Auto-instrument your applications with OpenTelemetry SDKs and capture granular execution traces immediately. Store 100% of trace spans with ClickHouse columnar compression instead of throwing away critical outlier data with probabilistic sampling.

  • Full OpenTelemetry auto-instrumentation for Go, Node.js, Python, Java, Rust, and .NET
  • Sub-millisecond span resolution with synchronized nanosecond precision timestamps
  • Context propagation across HTTP/2, gRPC, Kafka event streams, and background queues
  • 100% span retention in ClickHouse, zero probabilistic head-sampling blind spots
Interactive Engine
Trace Breakdown: #trace_91a0c842b109e24.2ms Total
api-gateway · HTTP POST /order24.2ms
auth-validator · jwt.Verify4.8ms
order-service · processOrder17.0ms
postgres · INSERT INTO orders8.5ms
ClickHouse 24.8 · OTLP 1.3 Native100% Ingestion Parity
Service Map

Dynamic Real-Time Architecture & Dependency Topology

The service dependency topology auto-generates continuously from live trace spans. No manual YAML diagrams or stale architecture docs: just an accurate, real-time map of every service edge and RPC boundary.

  • Real-time directed service dependency graph generated directly from trace span metadata
  • Continuous P50, P95, and P99 latency percentiles calculated per inter-service edge
  • Visual anomaly detection highlighting downstream bottlenecks and degraded error rates
  • Single-click deep dive into filtered span flamegraphs and correlated container logs
Interactive Engine
Trace Breakdown: #trace_91a0c842b109e24.2ms Total
api-gateway · HTTP POST /order24.2ms
auth-validator · jwt.Verify4.8ms
order-service · processOrder17.0ms
postgres · INSERT INTO orders8.5ms
ClickHouse 24.8 · OTLP 1.3 Native100% Ingestion Parity
Correlation

Single-Click Correlation: Trace to Log to Host Metric

Every distributed span automatically joins with logs emitted during its exact execution window and host infrastructure metrics. Eliminate manual grep and copy-pasting trace IDs across disconnected browser tabs.

  • W3C TraceContext automatically injected into log record attributes via OTel SDK
  • Click any flamegraph span to view correlated structured logs from that exact microsecond
  • Overlay CPU, memory, and network saturation timelines directly over trace spans
  • Configure alerts on high-cardinality span attributes including customer ID and error codes
Interactive Engine
Trace Breakdown: #trace_91a0c842b109e24.2ms Total
api-gateway · HTTP POST /order24.2ms
auth-validator · jwt.Verify4.8ms
order-service · processOrder17.0ms
postgres · INSERT INTO orders8.5ms
ClickHouse 24.8 · OTLP 1.3 Native100% Ingestion Parity

Built for every team that cares about reliability

One platform, tailored to how your team actually works.

DevOps teams

Deploy With Canary-Level Confidence

Compare P99 execution latency and error attribution across git revision tags in real time, isolating software regressions in minutes before broad rollout.

SRE teams

Sub-Second Distributed Root-Cause Pinpointing

Jump directly from an automated PagerDuty alert into the responsible distributed trace with hierarchical waterfall flamegraphs and synchronized container metrics.

Engineering leads

Enforce Quantitative Latency & SLO Budgets

Define rigorous P50/P95/P99 latency thresholds per API route and automatically trigger deployment rollbacks before customer SLA breaches occur.

Under the hood

Columnar OpenTelemetry Storage Engineered for 100% Retention

Spans arrive over standard OTLP over HTTP or gRPC, flow through a high-throughput Kafka streaming pipeline with dead-letter queue isolation, and land in a tuned ClickHouse columnar cluster. That architecture delivers 12.4:1 zstd compression, allowing teams to retain every single trace rather than relying on 1% sampling heuristics.

100% OTLP native over HTTP/gRPCKafka-backed ingestion with dead-letter queue isolation100% trace retention with 12.4x zstd columnar compression

Start tracing in 5 minutes

Your existing OpenTelemetry instrumentation works unchanged, so there is nothing to rewrite and nothing to migrate.