Your Infrastructure
Heals Itself

InfraSage detects anomalies, pinpoints root causes with AI, and auto-remediates — 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 spike +340% (p99: 2.4s)
14:32:02 WATCHDOG weirdness_score: 0.87 — triggering RCA pipeline
14:32:03 RCA building incident dossier... 23 related spans, 8 log entries
14:32:05 RCA vector search: 3 similar past incidents matched (cosine > 0.82)
14:32:08 SAGE root cause: connection pool exhaustion on db.insert
14:32:08 SAGE confidence: high | blast_radius: 3 services
14:32:09 AUTO executing runbook: restart-payment-pods
14:32:12 AUTO pod payment-service-7f8d4 restarted
14:32:15 RESOLVED latency back to normal (p99: 180ms)
14:32:15 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 next 45 minutes 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 MTTR

45+ minutes average to diagnose. Jumping between Grafana, Kibana, Jaeger, and Slack threads.

Tribal Knowledge

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

Reactive Firefighting

Your team spends 60% of time fighting fires instead of building. Incidents repeat without root cause capture.

$1K–$5.6K
per minute of downtime
Gartner / IDC
39 hrs
lost per SRE per month on diagnosis
Industry average
40%
of incidents repeat within 30 days
Without structured post-incident learning
2–4 wks
to ramp a new on-call engineer
Typical onboarding window

Before InfraSage

  • Alert fires at 3 AM
  • SSH into servers, grep logs
  • Check 5 dashboards manually
  • Ping on-call in Slack
  • Find root cause after 45 min
  • Manually restart pods
  • Write postmortem nobody reads
MTTR: 45+ min

With InfraSage

  • Anomaly auto-detected in 60s
  • LLM correlates traces + logs + metrics
  • Root cause identified with confidence
  • Past incidents matched via vectors
  • Runbook auto-executed
  • Slack notified with full RCA report
  • Knowledge base updated automatically
MTTR: < 2 min

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 gRPC. Ingest from AWS CloudWatch, Kubernetes events, and webhooks. Store in ClickHouse with materialized views.

OTLP gRPC • ClickHouse • Redpanda
2

Detect

Every 60 seconds, the Vectorizer computes 18-dimensional embeddings per service. The Watchdog compares against seasonal baselines using Z-score and Isolation Forest ML models.

18-dim embeddings • Z-score • Isolation Forest
3

Diagnose

RCA Orchestrator builds a full incident dossier: recent spans, logs, metrics, topology, and vector-matched past incidents. Sage AI returns structured root cause + confidence score.

Sage AI • Vector Search • Knowledge Base
4

Remediate

Automation Engine executes YAML runbooks: restart pods, scale deployments, drain nodes, run custom scripts. Supports human-in-the-loop approval. Notifies Slack, PagerDuty, Jira.

YAML Runbooks • K8s Actions • Approval Gates

Anomaly Detection

18-dimensional temporal embeddings capture latency, error rate, throughput, status code distribution, and more. Seasonal baselines adapt to your traffic patterns. Isolation Forest + Z-score ensemble catches subtle degradations that static thresholds miss.

Embeddings18 dimensions per service
Window60-second vectorization cycles
ML ModelsIsolation Forest + Z-score ensemble
BaselinesSeasonal with auto-retraining

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 everything into structured root cause with confidence score.

AI EngineSage AI — Claude / Gemini (configurable)
ContextTraces + logs + metrics + topology
HistoryVector-matched past incidents
OutputStructured JSON with confidence

Automated Remediation

Define runbooks in YAML with Kubernetes-native actions: restart pods, scale deployments, drain nodes, run custom scripts. Built-in approval gates for critical operations. Cooldown enforcement prevents runbook storms. Full audit trail for compliance.

ActionsRestart, scale, drain, custom script
SafetyHuman-in-the-loop approval gates
CooldownPer-trigger configurable cooldown
RollbackAutomatic on failure detection

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 ×3
OTLP gRPC :4317
REST API :8080
1M+ events/sec*
*Scales to infinity; add replicas for unlimited throughput.
Storage
ClickHouse
raw_firehose (traces/logs/metrics)
aggregated_metrics (materialized views)
service_embeddings (18-dim vectors)
Analysis (Leader-Elected)
Vectorizer (60s)
Watchdog
RCA Orchestrator
Sage AI (Claude / Gemini)
Actions
Prometheus + Alertmanager
Automation Engine
Slack / PagerDuty / Jira
1M+
Events/sec ingested
Tested load. Scales to infinity; add replicas.
<1ms
Query latency
ClickHouse materialized views for instant aggregation
60s
Detection window
From anomaly to alert in one vectorization cycle
18-dim
Service embeddings
Rich temporal vectors capturing multi-signal behavior
50+
API endpoints
Full REST API for every InfraSage capability
20+
ClickHouse tables
Raw telemetry, aggregated metrics, 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: "Auto-restart payment pods on connection pool exhaustion"

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"]
      timeout: "5m"
      auto_approve_if: "confidence > 0.85"

  - 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",
  "weirdness_score": 0.87,

  "root_cause": "Connection pool exhaustion on PostgreSQL.
    The db.insert span shows p99 latency of 2.4s (baseline: 45ms).
    Connection pool max_open_conns=25 is saturated under current
    load of 340 req/s. Upstream api-gateway is timing out and
    retrying, amplifying the problem.",

  "confidence": 0.91,
  "evidence": [
    "23 spans with db.insert > 2s in last 5min",
    "8 error logs: 'connection pool exhausted'",
    "Error rate jumped 12% → 67% at 14:31:45"
  ],

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

  "recommended_actions": [
    "Immediate: Restart payment-service pods",
    "Short-term: Increase max_open_conns to 50",
    "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",
      "weirdness_score": 0.87,
      "rca_status": "completed",
      "incident_id": "inc-1847"
    },
    "api-gateway": {
      "status": "healthy",
      "weirdness_score": 0.12
    }
  },
  "total_services": 12,
  "anomalies": 1
}
LanguageGo 1.23
DatabaseClickHouse
StreamingRedpanda (Kafka API)
TelemetryOpenTelemetry SDK
MLIsolation Forest + Z-score
LLMClaude / Gemini
MonitoringPrometheus + Grafana
OrchestrationKubernetes
HALeader election via K8s Lease
AuthAPI Key + HMAC tokens
LicenseCommercial — Self-Hosted
BinarySingle Go binary (~90MB)

Before vs. After InfraSage

Transformation metrics alongside your personalized savings estimate.

The Transformation

Metric Before With InfraSage Impact
Mean Time to DetectHours (alert noise)60 seconds~100× faster
Mean Time to Diagnose45+ minutes< 2 minutes90% reduction
Incident Recurrence~40% within 30 days< 5%8× fewer repeats
On-Call EscalationsEvery alert pages someoneOnly unresolved anomalies~70% fewer pages
SRE Firefighting Time~60% of working hours~15% of working hours45% reclaimed
Tribal Knowledge RiskIn Slack & memoryVersioned KB articlesZero bus-factor
New Eng Ramp-up2–4 weeksDay 1 with KB + RCAInstant readiness
Runbook ExecutionManual, error-proneAutomated, audited100% consistent
Postmortem CoverageOften skippedAuto-generated every incident100% coverage
SLA Breach ExposureHigh (slow respond)Minimal (<2-min response)~90% lower risk

Your ROI Estimate

5
230
20
5100
45 min
15 min3 hrs
$150/hr
$50$300
$1,000/min
$100$10K
$0
Annual cost savings
Engineer time + downtime cost combined
0 hrs
Eng hours saved / mo
0%
MTTR reduction
0
Fewer pages / mo
Estimates assume 2-min median MTTR, avg 2 engineers/incident, 70% escalation reduction, 40% recurrence eliminated.
Get a Detailed ROI Analysis

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
Auto-remediation Not included YAML runbooks with approval gates
Pricing model Per-host × per-log × per-trace Self-hosted — no per-event cost
Cost at 50+ hosts $300K – $600K / year typical 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 — live in 15 min
Incident knowledge base Not included AI-generated per incident, searchable
Multi-tenant RBAC Available, complex pricing tiers 5 roles, 11 permissions — included

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

Value for Every Role

InfraSage delivers measurable outcomes to 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.
  • SLA breach incidents drop ~90% — fewer customer impact events, 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 decreases by 45%
  • Board-ready reliability metrics generated automatically from incident data
~90% reduction in SLA breach exposure
SRE Lead
Sleep through the night. Build, don't fight.
  • MTTR drops from 45 min to under 2 min — the headline number you can take to leadership
  • Alert fatigue eliminated — correlated events collapsed to one actionable incident
  • On-call burden reduced by ~70% — 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
45 min → <2 min mean time to resolution
Engineering Manager
Improve velocity. Retain your best engineers.
  • Feature velocity improves as engineers spend 45% less time in firefighting mode
  • Incident repeat rate drops from ~40% to <5% — same root causes stop costing 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
45% of 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 Self-Healing Infrastructure

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 — either automatically via runbook or with a suggested resolution. 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 has 15–20 resolved incidents. RCA suggestions now reference past incidents with high similarity scores. The first repeated incident is resolved in under 30 seconds. The team notices the drop in on-call noise.

20+ KB articles accumulated First repeat incident auto-resolved On-call pages down ~40%
Month 3 Measurable Business Impact

MTTR is consistently under 5 minutes. On-call escalations are down 60%. SREs are spending more time on capacity planning and reliability improvements than firefighting. Engineering managers can present incident metrics to leadership with confidence.

MTTR consistently < 5 min On-call escalations down 60% SRE toil reduced by half
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 measurable 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 — generated automatically by the RCA pipeline with zero human intervention.

5Services Monitored
4Incidents Analyzed
<60sRCA Generation Time
91%Avg Confidence
HIGH
91% confidence
payment-service

Chaos Latency Injection Detected on Payment Pipeline

HTTP response times inflated to avg 13.8s (max 25.9s), DB query durations spiked to 7.2s, cache hit ratio collapsed to 7.5%.

CRITICAL
78% confidence
order-service

CPU Saturation Causing Cascading Latency Degradation

CPU at 93.4%, HTTP latency avg 4.9s, DB queries avg 1.95s, cache hit ratio collapsed to 44%.

HIGH
81% confidence
user-service

Cache Collapse Causing DB Overload and Latency Spike

HTTP latency avg ~9s (peak ~15s), DB query durations avg ~3.4s, 45% request failure rate.

MEDIUM
61% confidence
auth-service

Silent Degradation: Slow DB Queries and Low Cache Hit Ratio

HTTP latency avg ~3.4s, memory at ~3GB, DB queries avg 1.25s, cache hit ratio below 60%.

Every article is generated in under 60 seconds — from anomaly detection to root cause analysis to actionable remediation steps. No runbooks. No war rooms. Just answers.

Deploy in 5 Minutes

From zero to anomaly detection in under 5 minutes. 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-..."
Scale
1M+
Events / sec
60s
Detection window
<1ms
Query latency
Works With Your Stack
OpenTelemetry
CloudWatch
Kubernetes
Prometheus
Grafana
Slack
PagerDuty
Jira
ClickHouse
Redpanda
Claude AI
Gemini AI

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
  • Automated 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 AI-powered healing 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