Your engineer arrives
at the right answer.
Noctuary sits alongside your existing observability stack and pre-builds the context for every incident. When the alert fires, your engineer already has a specific hypothesis and a cited evidence trail — not a blank terminal.
Context at ingest.
Not at query time.
When an alert fires today, your engineer starts cold. They spend 10–15 minutes hunting for what changed. The answer is almost always in the logs: a deploy 4 minutes before onset, a config change, a flag flip. It was always there. Nobody assembled it.
Current AI tools send raw telemetry to an LLM at query time and ask it to infer what changed. Noctuary does the correlation work continuously, at ingest, so when the alert fires the evidence is already assembled.
"Complement, not replacement. Noctuary installs alongside your existing stack. Your engineers keep Datadog. They just arrive at the incident with a specific hypothesis and a cited evidence trail — not a blank terminal."
enrichment
From OTLP logs to instant root cause
OTel logs stream into the Noctuary agent running in your infrastructure. No raw data leaves.
›Lightweight rule scoring routes each log line to the correct vendor plugin: postgres, k8s, argocd, and more.
›Vendor plugins parse structured context: SHAs, latencies, pod names, error codes — from the raw log body.
›Context events are linked across time and services. A deploy + deadlock + OOM is a chain, not three alerts.
›When PagerDuty fires, the pre-built evidence packet is attached. A hypothesis in seconds, not 15 minutes.
WASM plugins run inside your own infrastructure. Only structured ContextEvent objects are sent to Noctuary cloud. Raw log content, credentials, and PII never leave your environment.
Faster incident response for on-call engineering teams
Seconds, not minutes
Mean time to hypothesis drops from 10–15 minutes of manual investigation to under 10 seconds. The evidence is pre-assembled before the alert fires.
91% better root cause
Pre-enriched context outperforms query-time AI on partial failures, cross-service chains, and ambiguous causation — the hard cases that create 2am incidents.
85% fewer tokens
The LLM receives a structured evidence packet, not a raw log dump. Smaller, more precise inputs produce better outputs at a fraction of the inference cost.
Data stays in your cluster
WASM plugins run inside your own infrastructure. Only structured ContextEvents are transmitted. Raw logs, credentials, and PII never leave your environment.
Keeps what you have
Works alongside Datadog, Grafana, PagerDuty — not instead of them. No rip-and-replace. No justifying a new observability platform to procurement.
Reduce on-call fatigue
Engineers who arrive at incidents with a clear hypothesis sleep better. Context-first alerting is the difference between a 10-minute fix and a 2-hour war room.
How we compare
| Capability | Datadog | Dynatrace Davis | BigPanda / PagerDuty | Noctuary |
|---|---|---|---|---|
| When context is built | At alert (query time) | Continuously (topology map) | Not built, alerts grouped only | At ingest, before alert fires |
| Root cause accuracy | Baseline | Strong, best of incumbents | No RCA | 91% better than query-time |
| Raw logs leave your infra | Yes | Yes | Yes | No |
| Replaces existing stack | Often | Yes, full platform required | No | No, complements it |
| Entry price | $3k–10k+/mo | $50k–200k+/yr | $20k+/yr | $299/month |
vs. Datadog · Dynatrace · BigPanda — full comparison on desktop
See it working right now.
Noctuary is running against a live OpenTelemetry environment powered by the Astronomy Shop — a real microservices app generating spans, traces, and logs.
Log in and watch the agent correlate errors to services, surface root causes from traces, and build incident context automatically — no configuration required.
Run it on your last three incidents.
Give us a 30-day log dump and we'll show you what context would have been attached to your last three incidents — before you commit to anything.
No credit card required · Free for 30 days · $299/month after