Adaptive observability
Your observability, finally quiet
Static thresholds break when you deploy daily. Most pages are false alarms. You waste the first chunk of every incident figuring out where to look.
Adaptive anomaly detection learns normal behavior across deploys, feature flags, and traffic spikes-then correlates metrics, traces, and logs. One incident with full context-not five separate alerts you have to connect yourself.
Incident card
payments/charge
Active
Severity: High
P95 latency
+180ms
Observed
Error rate
0.3% -> 2.1%
+520% jump
WHERE
currency/convert.go:142 (currency-svc)
WHY
null path on fxSource=EU under burst
HOW
add nil-guard; bounded retries (max=2)
Incident management that learns normal behavior.
Adaptive anomalies auto-open an incident view with an AI runbook, unified context, and a lightweight ticket so responders can act fast.
Adaptive anomalies based on normal behavior
Machine-learned baselines watch every signal, correlate outliers, and automatically open the exact service, env, and cohort affected-before humans even log in.
When multiple metrics spike from the same root cause, you see one incident with full context-not separate alerts you need to mentally correlate.
- 1
Adaptive baselines
Seasonality- and deploy-aware learning replaces manual thresholds.
- 2
Signal fusion
Metrics, traces, and logs are fused so only true incidents are escalated.
- 3
Instant scoping
Environment, services, and impacted cohorts are tagged the moment the alert fires.
payments/charge
P1 -ActiveSLO burn 42%P95 latency +180ms -Errors 0.3% -> 2.1% -Began 10:14 CET
SLO window
Error budget remaining
AI Insights used
Incident overview
payments/charge · started 10:14 CET
Error rate
0.3% -> 2.1%
Latency (P95)
+180 ms
Impacted cohorts
VIP, EU
Summary
EU traffic spike exposed a null conversion path and triggered retries; correlated metrics, traces, and logs confirm user impact.
Code reference
currency/convert.go:142
Recommended steps
- Add nil guard
- Clamp retries to max=2
- Update runbook section 3
Explain incidents concretely so responders can move.
Each incident narrative combines your observability exhaust with code intelligence so on-call engineers see what broke, why it matters, and what to do-without spelunking in five tools.
Guided RCA
WHERE/WHY/HOW live in the workspace and can be shared via Slack alert links.
Time-aware timeline
Deploys, novel logs, and mitigations are auto-pinned so you can replay the incident story.
Runbook pairing
Suggested remediation steps can open PRs, tickets, or automation directly.
Understand who is hurting and how far it spreads.
Our dependency graph, cohort-aware metrics, and exemplar traces make impact obvious, so you can prioritize the right customers and rollback scope.
Blast radius - Service map
Auto-learned dependenciesGitHub context
Repository
org/payments
Branch
main
Linked PR
#482 - "Add JSON validation to currency inputs" - merged 10:09
Deploy tag
deploy-2025-11-07-10:12
Notifications
Slack alertsAlert sent: #oncall-payments
10:15 CET -includes link to this incident
Configure additional channels or users for future alerts.
Share the right context without switching tools.
Cata posts an alert to Slack with a deep link and ties incidents to the relevant GitHub context-so responders land in the exact view they need.
GitHub context
Slack alerts
Notify channels and users with a direct link back to the incident workspace.
Action in-app
Acknowledge, escalate, open runbooks, and generate PRs from the workspace.
How it works
Four steps, zero threshold tuning
Connect telemetry
OpenTelemetry metrics, traces, logs-burst-friendly ingest with zero manual thresholds.
Connect GitHub & Slack
Signal correlation turns multiple alerts into one incident. Responders see deploy tags, PR context, and a single Slack notification-not a storm of separate pages.
Learn
Multivariate baselines with seasonality and deploy awareness.
Detect & Explain
Anomaly -> plain-English WHERE/WHY/HOW + blast radius + suggested fix. Slack alerts include a deep link to the incident workspace.
Storage strategy
Relevant-first retention
Model weights and span invariants stay hot for instant detection. Raw logs, full traces, and metric history move to cold storage. Rehydrate on-demand for deep forensics-no loss of investigative depth, significantly lower storage costs.
Pricing
Professional coverage, procurement-ready
One predictable plan that bundles AI explanations, guided remediation, bursting, and the integrations enterprises expect.
Covers 8M events/month, GitHub + Slack alerts, unlimited viewers, and burst protection with automatic scaling.
Plan includes
Events & Data
- 8M events/month included (<= 1 MB each)
- Burst-friendly ingest up to 3x
- 14 days hot + modeling baselines retained
AI Engine
- Multivariate, seasonality-aware baselines
- Deploy & change correlation
- Incident narratives & remediation steps
AI Insights
- 25K explanations/visualizations per month
- Auto top-up with spend guardrails
- Shared across teams
Integrations & Controls
- Slack alerts (channel & user, link to incident)
- GitHub context
- OpenTelemetry ingest (metrics, traces, logs)
Audit-ready controls -Annual + usage-based bursting -Legal & security review packet ready
Custom deployments
Need higher limits, private regions, or on-prem?
Our enterprise architecture team adapts Cata to meet your residency, networking, and control requirements without slowing rollouts.
- 1Dedicated VPC or on-prem appliance with offline model updates.
- 2Signed procurement packet (DPA, threat model), 24/7 response.
FAQ
Common questions
Will adaptive baselines miss rare but important spikes?
No. If a single metric spikes but nothing else shows distress-no errors, no latency degradation, no trace anomalies-it's likely not actionable. Real incidents create signatures across multiple signals. The correlation engine elevates these because multiple independent pieces of evidence agree something is wrong.
Do I have to abandon my existing alert rules?
No. Keep them. They represent institutional knowledge about known failure modes. What changes: you no longer maintain duplicates across services or create new rules for every edge case. When multiple rules fire for the same root cause, you see one coherent incident-not separate alerts you have to mentally connect.
What about cost? Don't I need everything in hot storage?
We keep model weights and one span invariant per service hot for instant correlation. Everything else (raw logs, full traces, high-res metrics) moves to cold storage. Rehydrate on-demand when you need deep forensics. Same investigative depth, lower storage bill.
How does this work with feature flags and gradual rollouts?
The adaptive baselines are deploy-aware and understand traffic shifts. When you gradually roll out a feature flag that changes behavior for a subset of users, the system recognizes this as expected variation rather than an anomaly. It learns the new normal as traffic patterns shift.
What integrations do you support?
OpenTelemetry for metrics, traces, and logs (native OTLP ingest). GitHub for repository context and deploy correlation. Slack for alerting to channels and users with deep links back to the incident workspace. More integrations coming based on customer needs.
Team
Built by software engineers who've run 24/7 production systems
We've been on-call. We've debugged incidents at 3am. We built this for teams like ours.
Eli Warner
Rina Kobayashi
Diego Alvarez
See it in action
Book a 45-minute demo
Learn how Cata sets up observability in minutes—not weeks. Connect OpenTelemetry, GitHub, and Slack, then watch Cata learn your normal, detect real anomalies, and open an AI runbook with a single click.
Zero-threshold setup: point to your OTel endpoint, you’re done
GitHub + Slack connected: deep links to the exact incident view
Runbook review + pilot success plan
Demo agenda
45 min- Connect OpenTelemetry + GitHub + Slack in minutes
- Watch AI analyse events to detect incident
- See cost controls + relevant-first retention in action