From Anomaly to
Root Cause in Minutes

InfraSage detects anomalies, pinpoints the root cause with evidence-cited AI, and recommends the fix for your team to approve — deployed entirely inside your own Kubernetes cluster. Your telemetry never leaves your VPC. One Helm chart to get started.

Self-Hosted Kubernetes Native OpenTelemetry GDPR · RBI · HIPAA Ready
infrasage (live)
# Anomaly detected on payment-service
14:32:01 ANOMALY payment-service latency rising above baseline
14:32:02 WATCHDOG anomaly confirmed — triggering RCA pipeline
14:32:03 RCA building incident dossier from traces, logs & metrics
14:32:05 RCA vector search — similar past incidents matched
14:32:08 SAGE root cause: connection pool exhaustion on db.insert
14:32:08 SAGE evidence cited · adversarially judged · published
14:32:09 RUNBOOK recommended: restart-payment-pods — awaiting approval
14:32:24 APPROVED by on-call — executing runbook
14:32:31 RESOLVED latency back to normal
14:32:31 KB knowledge article created — incident #1847
_

See InfraSage in Action

Real product views from the InfraSage console — from the live incident board to telemetry, notifications, and integrations.

Ingestion & Telemetry

Watch signals flow at scale

Validate ingestion health, event volume, error trends, and discovered signal types without leaving the platform.

OTel-native Signal coverage
InfraSage telemetry ingestion dashboard with event volume and signal coverage
Notifications

Cut through alert noise with context-rich updates

Bring health, incidents, and remediation outcomes into one actionable feed for the on-call team.

Actionable alerts Operator clarity
InfraSage notifications dashboard showing health updates and incident alerts
Integrations

Plug into the stack you already run

Connect cloud and Kubernetes environments quickly, verify health, and keep operators inside a single workflow.

AWS Kubernetes Ready in minutes
InfraSage integrations dashboard showing AWS and Kubernetes connections

The 3 AM Problem

Every SRE knows the drill. Your phone rings, alerts are firing, and you spend the rest of the night jumping across dashboards, logs, and Slack threads trying to figure out what broke.

Alert Fatigue

Hundreds of duplicate alerts for a single incident. Teams start ignoring notifications.

Slow Diagnosis

Long, manual investigation — jumping between Grafana, Kibana, Jaeger, and Slack threads to find what actually broke.

Tribal Knowledge

Only 2 people know why that service crashes on Tuesdays. When they leave, the knowledge leaves too.

Reactive Firefighting

Your team spends more time fighting fires than building. Incidents repeat without root-cause capture.

Downtime
burns revenue every minute it lasts
Toil
senior SREs lose hours per incident to manual diagnosis
Repeats
incidents recur without structured post-incident learning
Ramp-up
new on-call engineers take weeks to get productive

Before InfraSage

  • Alert fires at 3 AM
  • SSH into servers, grep logs
  • Check dashboards manually
  • Ping on-call in Slack
  • Find root cause after a long hunt
  • Manually restart pods
  • Write postmortem nobody reads
Resolution: slow & manual

With InfraSage

  • Anomaly auto-detected in real time
  • AI correlates traces + logs + metrics
  • Root cause cited and adversarially judged
  • Past incidents matched via vectors
  • Runbook recommended — you approve in Slack
  • Slack notified with full RCA report
  • Knowledge base updated automatically
Resolution: fast, with cited RCA

AIOps Without the Compliance Risk

Every SaaS observability tool processes your telemetry on their infrastructure. For fintech, healthtech, and regulated industries, that's not a feature — it's a liability.

EU & UK Fintech

GDPR & BaFin Compliant by Design

Telemetry pipelines carry PII — transaction IDs, user identifiers, customer event data. GDPR and BaFin don't give you a pass because it's "just metrics." InfraSage runs entirely inside your EU VPC. Zero data egress, ever.

  • GDPR Article 44 safe — no cross-border data transfer
  • BaFin-compatible data residency
  • No US-SaaS vendor risk in your infra pipeline
  • Auditor-friendly: no third-party telemetry access
India Fintech

RBI Data Localization & DPDP Ready

RBI mandates that payment data stays within India. The DPDP Act extends this broadly to personal data. InfraSage deploys to your own infrastructure — your observability data never crosses a border.

  • RBI-compliant data localization
  • DPDP Act safe — no third-party processor
  • Runs in your Indian cloud region or on-prem
  • No foreign SaaS vendor in your data pipeline
US Healthtech

HIPAA Clean — No BAA Required

PHI bleeds into telemetry. Log lines carry patient identifiers. Trace attributes carry session context. With InfraSage self-hosted, there's no third-party to sign a BAA with — because no third party ever sees your data.

  • No HIPAA BAA needed — no third-party exposure
  • PHI-safe telemetry pipeline end to end
  • Zero audit exposure to external vendors
  • On-prem, VPC, and hybrid deployable

InfraSage is the only AIOps platform built from the ground up to never see your data.

Talk to Us About Your Compliance Needs

How InfraSage Works

One platform, four stages, six capabilities. Explore each layer below.

1

Ingest

Receive traces, metrics, and logs via OpenTelemetry, Prometheus remote-write, JSON, and AWS CloudWatch. Every event is validated, with a dead-letter queue so nothing is silently lost. Stored in tiered ClickHouse with materialized views.

OTLP gRPC • ClickHouse • Redpanda
2

Detect

The Vectorizer continuously computes a behavioral embedding per service. The Watchdog compares against seasonal baselines using robust statistics (z-score / MAD) and Isolation Forest — plus a per-(service, metric) scorer and trend detection for slow-burn regressions.

Embeddings • z-score / MAD • Isolation Forest
3

Diagnose

The RCA Orchestrator builds a full incident dossier: recent spans, logs, metrics, topology, and vector-matched past incidents. Sage AI returns an evidence-cited root cause — adversarially judged, and honest enough to abstain when the evidence is insufficient.

Sage AI • Vector Search • Knowledge Base
4

Remediate

The Automation Engine recommends a curated runbook for the matched fault — and a human approves it in Slack before anything runs. Auto-rollback if metrics worsen; recovered alerts auto-resolve. Notifies Slack, PagerDuty, Jira.

Runbook KB • Approval Gates • Auto-resolve

Anomaly Detection

Temporal embeddings capture latency, error rate, throughput, and more. Seasonal baselines adapt to your traffic patterns. A robust z-score / MAD watchdog, a per-(service, metric) scorer, Isolation Forest, and OLS trend detection catch everything from single-endpoint regressions to slow-burn degradations — plus heartbeat checks for silent services.

EmbeddingsPer-service behavioral vectors
StatisticsRobust z-score / MAD + named-metric scorer
ML ModelsIsolation Forest + trend detection
LivenessSeasonal baselines + heartbeat checks

Root Cause Analysis

When an anomaly fires, the RCA Orchestrator builds a comprehensive incident dossier: recent spans with errors, correlated logs, metric deviations, service topology, and vector-matched past incidents. An LLM synthesizes it into an evidence-cited root cause — every claim pinned to telemetry, adversarially judged by a second model, and abstaining with INSUFFICIENT_EVIDENCE when the data won't support a confident answer.

AI EngineAnthropic · Bedrock · Gemini · local LLM
TrustEvidence-cited & adversarially judged
AbstentionSays INSUFFICIENT_EVIDENCE, never guesses
OriginDeterministic upstream cascade attribution

Approval-Gated Remediation

A curated, human-authored runbook KB recommends the fix for a matched fault — recommend-only by default, with a person approving in Slack before anything runs. Alert dedup absorbs storms into one incident, recovered alerts auto-resolve, and rollback is automatic if metrics worsen. Full audit trail for compliance.

DefaultRecommend-only — you stay in control
SafetyHuman-in-the-loop approval gates
NoiseAlert dedup & auto-resolve
RollbackAutomatic if metrics worsen

Service Topology

Auto-discovers service dependencies from trace data. No manual configuration needed. Computes blast radius when anomalies hit; know exactly which upstream and downstream services are affected before opening a dashboard.

DiscoveryAuto from distributed traces
Blast RadiusUpstream + downstream impact
VisualizationInteractive dependency graph
UpdateContinuous from live traffic

Knowledge Base

Every resolved incident automatically generates a knowledge article with root cause, resolution steps, and affected services. Articles are vectorized and fed back into future RCA pipelines. Reference counting tracks which articles are most useful.

GenerationAI-generated from resolved incidents
RetrievalVector similarity search
FeedbackInjected into future RCA context
TrackingReference counting per article

Multi-Tenant & RBAC

Team-scoped dashboards with 5 roles (super-admin, admin, operator, viewer, service-account) and 11 granular permissions. Service ownership model ensures teams only see and act on their services. API key + HMAC authentication.

Roles5 built-in, fully customizable
Permissions11 granular capabilities
IsolationTeam-scoped service ownership
AuthAPI key + HMAC token signing
Telemetry Sources
OpenTelemetry
AWS CloudWatch
Kubernetes Events
Webhooks
Ingestion Layer
Ingestion Gateway
OTLP · Prometheus · JSON
Validation + dead-letter queue
Horizontally scalable
Storage
ClickHouse
raw_firehose (traces/logs/metrics)
aggregated_metrics + cardinality control
service_embeddings (behavioral vectors)
Analysis (Leader-Elected)
Vectorizer
Watchdog
RCA Orchestrator
Sage AI (Claude · Bedrock · Gemini · local)
Actions
Prometheus + Alertmanager
Automation Engine
Slack / PagerDuty / Jira
Scale
High-throughput ingestion
ClickHouse-backed; add replicas as you grow
Real-time
Anomaly detection
Continuous vectorization, not batch
Cited
Root cause analysis
Evidence-pinned, judged, and abstaining
Self-host
Your VPC only
Telemetry never leaves your cluster
REST API
Programmatic access
OpenAPI spec + operator CLI + RCA MCP server
Tiered
ClickHouse storage
Raw, aggregated, embeddings, incidents
otel-collector-config.yaml
# Point your OTEL Collector at InfraSage
exporters:
  otlphttp/infrasage:
    endpoint: "http://infrasage-gateway:8080"
    tls:
      insecure: true
    headers:
      X-API-Key: "${INFRASAGE_API_KEY}"

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [batch]
      exporters: [otlphttp/infrasage]
    metrics:
      receivers: [otlp]
      processors: [batch]
      exporters: [otlphttp/infrasage]
    logs:
      receivers: [otlp]
      processors: [batch]
      exporters: [otlphttp/infrasage]
runbooks/restart-payment-pods.yaml
id: "restart-payment-pods"
name: "Restart Payment Service Pods"
description: "Restart payment pods on connection pool exhaustion (approval-gated)"

triggers:
  - type: anomaly
    service_pattern: "payment-service"
    min_severity: warning
    cooldown_mins: 15

steps:
  - name: "Notify team"
    action: slack_notify
    params:
      channel: "#incidents"
      message: "Restarting payment-service pods (auto)"

  - name: "Approval gate"
    action: approval
    params:
      approvers: ["sre-team"]
      require_human: true

  - name: "Restart pods"
    action: kubernetes_restart
    params:
      namespace: "production"
      deployment: "payment-service"
      strategy: "rolling"

  - name: "Verify recovery"
    action: wait_healthy
    params:
      service: "payment-service"
      timeout: "3m"
RCA Output | payment-service incident #1847
// LLM-generated root cause analysis
{
  "incident_id": "inc-1847",
  "service": "payment-service",
  "verdict": "PUBLISHED",   // adversarially judged

  "root_cause": "Connection pool exhaustion on PostgreSQL.
    db.insert span latency rose well above baseline while the
    connection pool saturated under load. Upstream api-gateway is
    timing out and retrying, amplifying the problem.",

  "evidence": [
    "db.insert spans elevated above baseline [ev:1]",
    "logs: 'connection pool exhausted' [ev:2]",
    "error rate rising on payment-service [ev:3]"
  ],

  "blast_radius": [
    "api-gateway (upstream, retrying)",
    "order-service (downstream, blocked)"
  ],

  "recommended_actions": [
    "Immediate: Restart payment-service pods (approve in Slack)",
    "Short-term: Increase the connection pool size",
    "Long-term: Add connection-pool metrics to monitoring"
  ]
}
prometheus/alert-rules.yaml
groups:
  - name: infrasage-anomalies
    rules:
      - alert: ServiceAnomalyDetected
        expr: |
          infrasage_anomaly_weirdness_score
            > 0.7
          and
          infrasage_anomaly_consecutive_hits
            >= 3
        for: 1m
        labels:
          severity: warning
        annotations:
          summary: >-
            Anomaly on {{ $labels.service_id }}
            (score: {{ $value | printf "%.2f" }})
          runbook: "https://wiki/runbooks/anomaly"
API | Watchdog Summary
# Get current anomaly status for all services
$ curl -s http://infrasage:8080/api/v1/watchdog/summary \
    -H "X-API-Key: $API_KEY" | jq .

{
  "timestamp": "2025-01-15T14:32:15Z",
  "services": {
    "payment-service": {
      "status": "anomaly",
      "rca_status": "completed",
      "incident_id": "inc-1847"
    },
    "api-gateway": {
      "status": "healthy"
    }
  },
  "total_services": 12,
  "anomalies": 1
}
LanguageGo 1.23
DatabaseClickHouse
StreamingRedpanda (Kafka API)
TelemetryOpenTelemetry SDK
MLIsolation Forest · z-score/MAD · forecasting
LLMClaude · Bedrock · Gemini · local
MonitoringPrometheus + Grafana
OrchestrationKubernetes
HALeader election via K8s Lease
AuthAPI Key + HMAC tokens
LicenseCommercial — Self-Hosted
BinarySingle Go binary

Before vs. After InfraSage

Transformation metrics alongside your personalized savings estimate.

The Transformation

Metric Before With InfraSage Impact
Mean Time to DetectLost in alert noiseCaught in real timeFaster detection
Mean Time to DiagnoseA long manual huntCited RCA in minutesLess bridge time
Incident RecurrenceRepeats without captureCaptured in the KBFewer repeats
On-Call EscalationsEvery alert pages someoneOnly unresolved anomaliesFewer pages
SRE Firefighting TimeMost of the weekTime back to buildLess toil
Tribal Knowledge RiskIn Slack & memoryVersioned KB articlesZero bus-factor
New Eng Ramp-upWeeks of shadowingDay 1 with KB + RCAFaster onboarding
Runbook ExecutionManual, error-proneApproval-gated, auditedConsistent & safe
Postmortem CoverageOften skippedAuto-generated every incidentFull coverage
SLA Breach ExposureHigh (slow response)Faster, cited responseLower risk

Where the Value Comes From

  • Calmer 3 AM pages. Evidence-cited RCA points at the real upstream cause, so the bridge starts from facts instead of guesses.
  • Less toil, more building. Alert dedup and auto-resolve cut the noise — your team spends its week shipping, not firefighting.
  • Knowledge that compounds. Every resolved incident becomes a searchable KB article that feeds the next RCA.
  • No surprise observability bill. Self-hosted means no per-host, per-log, or per-trace metering — and your data never leaves your VPC.
Talk to Us About Your Numbers

InfraSage vs. Datadog & New Relic

At scale, Datadog's bill becomes your biggest infrastructure cost. And your telemetry lives in their cloud — which creates compliance exposure you can't fix with a BAA.

Capability Datadog / New Relic InfraSage
Data location Their US / EU SaaS cloud Inside your own VPC — always
AI root cause analysis Watchdog — limited, opaque output Sage AI — full LLM RCA with evidence trail
Guided remediation Not included Runbook recommendations with approval gates
Pricing model Per-host × per-log × per-trace Self-hosted — no per-event cost
Cost at scale Per-host × per-log metering adds up fast Your infrastructure cost only
GDPR / RBI / HIPAA BAA available, data still leaves Zero data egress — compliant by design
Deployment SaaS account + agent rollout One Helm chart — deploy into your cluster
Incident knowledge base Not included AI-generated per incident, searchable
Multi-tenant RBAC Available, complex pricing tiers Built-in roles & granular permissions — included

Most teams switch after their first Datadog renewal at scale. Let's talk before yours arrives.

Value for Every Role

InfraSage delivers clear outcomes for every stakeholder — from the CTO who owns the SLA to the engineer who gets the 3 AM page.

CTO / VP Engineering
Protect revenue. Demonstrate reliability.
  • Fewer customer-impacting incidents — and lower penalty exposure
  • Full audit trail of every remediation action satisfies compliance and post-incident review
  • Knowledge base eliminates bus-factor risk — no single person holds the system knowledge
  • Engineering velocity increases as SRE toil drops
  • Board-ready reliability metrics generated automatically from incident data
Fewer SLA breaches & penalty exposure
SRE Lead
Sleep through the night. Build, don't fight.
  • MTTR drops sharply — a cited root cause in minutes, not a long manual hunt
  • Alert fatigue eliminated — correlated events collapsed to one actionable incident
  • On-call burden drops — fewer escalations, faster resolution
  • Every resolved incident auto-generates a KB article — no more manual postmortems
  • Historical incident matching means repeated failures resolve before you open a terminal
Minutes to a cited root cause
Engineering Manager
Improve velocity. Retain your best engineers.
  • Feature velocity improves as engineers spend less time firefighting
  • Repeat incidents drop — the same root causes stop costing you twice
  • New engineers handle on-call from day one with KB + LLM-generated RCA as their guide
  • Engineer retention improves — "we don't have 3 AM on-call hell" is a real hiring pitch
  • Every incident is documented automatically — no more chasing engineers for postmortems
More engineering time reclaimed from toil
DevOps / Platform Engineer
GitOps-native. Zero new agents. Works with what you have.
  • Plugs into your existing OTEL Collector — zero agent installs, zero code changes
  • YAML runbooks live in Git — reviewable, versioned, and testable like any other code
  • Service topology auto-discovered from traces — no manual dependency mapping
  • Kubernetes-native actions — operates in your existing cluster, your IAM
  • Single Go binary with no external ML dependencies — simple to operate
Zero agents or code changes required to onboard

Your Path to Faster, Calmer Incidents

Adoption is gradual but compounding. Each milestone builds on the last.

Day 0 Connect & Go Live

Point your existing OTEL Collector at InfraSage. Zero agents, zero code changes in your services. Anomaly detection is live within minutes of your first telemetry arriving.

OTel pipeline connected Baseline training begins Service topology auto-mapped
Week 1 First Incident Caught

Baseline is established for most services. The first real anomaly is detected, diagnosed by the LLM, and resolved — with a recommended runbook your team approves. Your first knowledge article is auto-generated.

First anomaly auto-detected First LLM-generated RCA First KB article written
Month 1 Compounding Intelligence

The knowledge base now holds a growing set of resolved incidents. RCA suggestions reference similar past incidents. The first repeated incident is resolved fast, and the team notices the drop in on-call noise.

KB articles accumulating First repeat incident resolved fast On-call noise dropping
Month 3 Clear Business Impact

MTTR is consistently low and on-call escalations keep falling. SREs spend more time on capacity planning and reliability improvements than firefighting. Engineering managers can present incident trends to leadership with confidence.

MTTR consistently low On-call escalations down sharply SRE toil sharply reduced
Month 6+ Proactive Operations

The team has shifted from reactive firefighting to proactive reliability engineering. The knowledge base is a living asset used in onboarding. New engineers handle on-call from week one. Leadership sees clear SLA improvement and reduced operational cost.

New hires on-call from week 1 SLA breach events near zero Team fully proactive on reliability

AI-Generated Incident Intelligence

Every anomaly InfraSage detects becomes a knowledge article. These are real findings from our live demo environment — each one evidence-cited and adversarially judged before it publishes.

LiveDemo environment
RealIncidents analyzed
CitedEvery root cause
JudgedBefore it publishes
HIGH
evidence-cited
payment-service

Chaos Latency Injection Detected on Payment Pipeline

HTTP response times inflated sharply, DB query durations spiked, and cache hit ratio collapsed.

CRITICAL
evidence-cited
order-service

CPU Saturation Causing Cascading Latency Degradation

CPU saturated, HTTP latency elevated, DB queries slowed, and cache hit ratio collapsed.

HIGH
evidence-cited
user-service

Cache Collapse Causing DB Overload and Latency Spike

HTTP latency spiked, DB query durations rose, and a large share of requests failed.

MEDIUM
evidence-cited
auth-service

Silent Degradation: Slow DB Queries and Low Cache Hit Ratio

HTTP latency elevated, memory creeping up, DB queries slow, and cache hit ratio degraded.

Every article goes from anomaly detection to an evidence-cited root cause to recommended remediation steps. No war rooms — just answers your team can act on.

Deploy on Your Own Cluster

From zero to anomaly detection on infrastructure you control. All you need is a Kubernetes cluster.

1

Prerequisites

A running Kubernetes cluster with kubectl configured.

# Verify cluster access
$ kubectl cluster-info
2

Deploy Infrastructure

Deploy ClickHouse (telemetry storage) and Redpanda (event streaming).

$ kubectl apply -f deployments/kubernetes/01-clickhouse.yaml
$ kubectl apply -f deployments/kubernetes/02-redpanda.yaml
3

Deploy InfraSage

Deploy the core engine, Prometheus, Alertmanager, and Grafana dashboards.

$ kubectl apply -f deployments/kubernetes/03-infrasage.yaml
$ kubectl apply -f deployments/kubernetes/04-prometheus.yaml
$ kubectl logs -l app=infrasage-aiops | grep "became leader"
4

Point Your OTEL Collector

Configure your existing OpenTelemetry Collector. No code changes in your services.

exporters:
  otlphttp/infrasage:
    endpoint: "http://infrasage-gateway:8080"
    headers:
      X-API-Key: "your-api-key"
5

Configure & Access

Set up Slack, PagerDuty, and access Grafana. InfraSage starts learning baselines immediately.

$ kubectl set env deployment/infrasage-aiops \
    SLACK_WEBHOOK_URL="https://hooks.slack.com/..." \
    ANTHROPIC_API_KEY="sk-ant-..."
Built for your infrastructure
OTel
Native ingest
Helm
One-chart deploy
BYOC
Your own cloud
Works With Your Stack
OpenTelemetry
CloudWatch
Kubernetes
Prometheus
Grafana
Slack
PagerDuty
Jira
ClickHouse
Redpanda
Claude AI
Gemini AI
Teams & Discord
Email
Opsgenie
RCA MCP Server
REST API & CLI
AWS Bedrock

Simple Licensing. No Per-Event Billing.

InfraSage is a self-hosted commercial platform — you pay a flat annual license. No per-host, per-log, or per-trace charges. Your infra cost is your infra cost.

Monthly Annual Save 20%
Starter
$ 479 /mo
Billed annually ($5,748/yr)

For small engineering teams deploying AIOps for the first time.

Get Started
  • 1 Kubernetes cluster
  • Up to 15 services monitored
  • Anomaly detection & RCA
  • Approval-gated runbook execution
  • 7-day telemetry retention
  • Slack & email alerts
  • Community support
  • Multi-tenant RBAC
  • SSO / audit logs
Enterprise
Custom
Annual license — volume discounts available

For regulated enterprises with unlimited scale and dedicated support requirements.

Talk to Sales
  • Unlimited clusters & services
  • Unlimited telemetry retention
  • SSO (SAML / OIDC) + audit logs
  • Dedicated Slack support channel
  • Custom SLA — 4h critical response
  • Security review & pen test docs
  • Custom onboarding & training
  • Custom integrations on request
  • Named account manager

Get in Touch

Have questions about InfraSage? Want to discuss how evidence-cited AIOps can transform your operations? We'd love to hear from you.

Book a 20-min Demo

See InfraSage in action on a live Kubernetes environment. We'll tailor it to your stack, compliance requirements, and team size.

Book a Demo

No credit card. No agents. No data leaves your cluster.

Send us an email

Questions about compliance, architecture, or pricing? Drop us a line and we'll get back to you within 24 hours.

contact@infrasage.dev