IT Operations Automation with n8n
Nordic SaaS provider with 850 employees automates incident management using n8n workflows, reducing mean time to resolution from 4 hours to 45 minutes, achieving 89% auto-triage rate, 97% platform uptime, and €1.4M annual cost savings through intelligent alert routing and automated remediation.
Executive Summary
Client Profile
Industry: SaaS & Cloud Technology
Company: Nordic SaaS Provider
Employees: 850 (120 IT operations)
Revenue: €85 million ARR
Customers: 4,200 B2B customers, 2.8M end users
Project Timeline
Duration: 4 months (Jan-Apr 2024)
Pilot: Incident routing, 1 product team
Rollout: 32 workflows, full IT ops
Go-Live: May 2024
Project Scope
Platform: n8n (self-hosted on Azure)
Workflows: 32 IT automation workflows
Integration: PagerDuty, Datadog, Slack, Jira
Monitoring: 450+ services, 2.8M users
Business Challenge
The Problem
Manual incident management caused 4-hour mean time to resolution, 200+ monthly incidents with alert fatigue, and 93.2% uptime (below SLA), resulting in customer churn, revenue loss, and burned-out on-call engineers working nights and weekends to keep systems running.
Slow Incident Response & Alert Fatigue
4-hour mean time to resolution (MTTR)
Incidents detected by Datadog manually triaged by on-call engineers - 30 min to acknowledge, 3.5 hours to resolve (SLA target: 1 hour MTTR)
200+ incidents per month
Mix of critical (15%), high (30%), medium (40%), low (15%) - all trigger PagerDuty alerts to on-call rotation (24 engineers)
2,400+ alerts per month
92% false positives or low-severity noise - engineers ignoring alerts, missing critical issues buried in noise (alert fatigue)
Downtime & Revenue Impact
93.2% platform uptime
SLA commits to 99.5% uptime - missing SLA 6 months in a row, customer SLA credits €340K/year, churn risk on 18% of customer base
€2.8M revenue at risk
18% of ARR (€85M × 18% = €15.3M) threatened by churn due to reliability issues - lost 12 enterprise customers in past year citing "too many outages"
42 hours downtime/year
Across 450 microservices - database crashes (28%), API timeouts (35%), deployment failures (22%), infrastructure issues (15%)
Manual Processes & Tribal Knowledge
80% manual remediation
Common fixes (restart service, scale replicas, clear cache, rollback deployment) done manually via kubectl, Azure CLI - no runbooks or automation
Knowledge in people's heads
"Ask Johan about payment service" culture - 5 senior engineers hold critical tribal knowledge, no documentation, new hires take 6 months to ramp up
No automatic escalation
Critical incidents manually escalated via Slack "war room" - 45 min average to assemble right team (database expert, network admin, product owner)
Engineer Burnout & Hiring Costs
38% on-call engineer turnover
On-call rotation (24 engineers) experiencing burnout - paged 8× per night on average, working weekends to fix incidents (industry avg: 18% turnover)
€480K annual hiring cost
Replacing 9 engineers/year at €110K avg salary + recruiting/training costs - burnout causing talent exodus, hard to hire replacements (6-month avg time-to-hire)
60 hours/week on-call load
Engineers spending 20 hours/week on incident response (50% of time) instead of product development - feature velocity down 40% vs 2 years ago
Solution Architecture
Technspire deployed n8n on Azure to create 32 intelligent IT operations workflows that automate incident triage, intelligent alert routing with Azure OpenAI-powered severity classification, automatic remediation for common issues, and escalation management - reducing MTTR from 4 hours to 45 minutes while improving uptime to 97%.
n8n Platform on Azure Kubernetes Service (AKS)
Deployed n8n on Azure Kubernetes Service (AKS) for high availability and auto-scaling (3 replicas, horizontal pod autoscaling). PostgreSQL on Azure Database stores workflow execution history and incident tracking data. Azure Key Vault manages credentials for 18 integrated systems (PagerDuty, Datadog, Slack, Jira, Azure DevOps, GitHub, etc.). Azure Monitor tracks workflow performance and sends alerts if workflows fail.
Key Features: High availability (99.9% uptime), horizontal scaling (handle 10K alerts/hour), secure credential management, workflow execution monitoring
Architecture: 3 n8n pods (AKS), PostgreSQL (Azure Database), Azure Key Vault, Azure Monitor, Azure Application Insights
Intelligent Alert Triage & Routing (12 Workflows)
Created 12 alert triage workflows triggered by Datadog webhooks (APM, infrastructure, logs, synthetics). Workflows use Azure OpenAI GPT-4 to analyze alert context (error message, stack trace, service dependencies, recent deployments) and classify severity (P0 critical, P1 high, P2 medium, P3 low). Intelligent routing: P0 → page entire on-call team + create Slack war room, P1 → page primary on-call, P2 → create Jira ticket, P3 → log to database (no alert). Deduplication: Similar alerts within 15 min grouped into single incident (reduced 2,400 → 220 alerts/month, 91% reduction).
Workflows: Datadog APM → GPT-4 classify → route, Infrastructure alerts → triage → escalate, Log errors → analyze → assign, Synthetic monitors → validate → page
Result: 89% auto-triage accuracy, 91% alert noise reduction (2,400 → 220/month), 30 min → 2 min acknowledgement time
Automated Remediation & Self-Healing (8 Workflows)
Built 8 auto-remediation workflows for common incidents (80% of issues): (1) Restart Service - if health check fails 3×, restart AKS pod automatically, (2) Scale Replicas - if CPU >85%, scale from 3→6 replicas, (3) Clear Cache - if Redis memory >90%, flush LRU cache, (4) Rollback Deployment - if error rate spikes post-deploy, auto-rollback to previous version, (5) Database Connection Pool - if pool exhausted, increase max connections, (6) Certificate Renewal - renew SSL certs 7 days before expiry, (7) Disk Cleanup - delete old logs if disk >85% full, (8) DNS Propagation - refresh DNS cache if lookup failures spike.
Key Tech: n8n HTTP Request (kubectl API, Azure CLI), Datadog API (metrics), Slack API (notifications), runbook scripts (Bash, PowerShell)
Result: 68% of incidents auto-resolved (136/200 per month), 4h → 45min MTTR, 93.2% → 97% uptime
Escalation Management & War Room Automation (6 Workflows)
Created 6 escalation workflows for P0/P1 incidents that cannot be auto-resolved: (1) Assemble War Room - create Slack channel, invite on-call team + service owners + execs, (2) Stakeholder Notifications - notify customer success, post status page update, send email to affected customers, (3) Knowledge Retrieval - search Confluence runbooks, pull relevant past incidents from Jira, send to Slack, (4) Expert Finder - query GitHub commits to identify service owners, ping via Slack, (5) Incident Timeline - auto-generate timeline from Datadog events, deployments, alerts, Slack messages, (6) Post-Mortem - create Jira template with timeline, assign to service owner, schedule blameless post-mortem meeting.
Integrations: Slack, PagerDuty, Jira, Confluence, GitHub, Statuspage, Datadog, Calendly
Result: 45 min → 5 min to assemble war room, 100% post-mortems completed (vs 30% before), knowledge capture improved
Monitoring & Observability Workflows (4 Workflows)
Built 4 monitoring workflows for proactive issue detection: (1) Anomaly Detection - Azure ML model detects traffic/latency anomalies, triggers investigation workflow, (2) SLA Monitoring - track uptime per customer, send alerts if approaching SLA breach (99.5% threshold), (3) Cost Optimization - detect idle resources (AKS pods scaled to 0 traffic, unused storage), create tickets to decommission, (4) Security Scanning - daily vulnerability scans (Trivy for containers, Dependabot for code), create Jira tickets for CVEs, auto-patch low-risk vulnerabilities.
Key Tech: Azure ML (anomaly detection), Datadog API, Azure Cost Management, Trivy, Dependabot, Snyk, Jira API
Result: 12 production issues prevented (caught in monitoring before customer impact), €120K cloud cost savings from idle resource cleanup
ChatOps & Incident Commands (Slack Bot)
Created Slack bot powered by n8n for ChatOps (engineers trigger workflows via Slack commands): /incident status - show all active incidents with timelines, /restart [service] - restart AKS pod for service, /scale [service] [replicas] - scale service to N replicas, /rollback [service] - rollback to previous deployment, /runbook [service] - search Confluence for service runbook, /oncall - show who's on-call this week (PagerDuty integration). Engineers can also ask GPT-4 via Slack ("how do I restart payment service?") and bot executes workflow.
Key Tech: Slack slash commands, n8n webhook triggers, Azure OpenAI GPT-4 (natural language commands), kubectl API, Azure CLI
Result: 85% of manual tasks now done via Slack (vs logging into Azure Portal), 10 min → 30 sec for common operations
Implementation Timeline
Discovery & n8n Infrastructure Setup
Incident analysis (200 incidents over 3 months), MTTR baseline measurement, runbook documentation, n8n deployment on AKS (3 replicas, PostgreSQL, Key Vault), integration setup (Datadog, PagerDuty, Slack, Jira), pilot workflow selection
Pilot - Alert Triage & Auto-Remediation
Built 6 pilot workflows (alert triage, restart service, scale replicas, rollback, Slack notifications), Azure OpenAI integration for severity classification, tested with 1 product team (payment service), monitoring accuracy and false positive rate, refined classification logic
Full Rollout & Escalation Workflows
Built remaining 26 workflows (escalation, war room, monitoring, ChatOps bot), rollout to all 450 services, on-call team training (24 engineers, 8 hours training), runbook migration to automated workflows, established incident review process
Optimization & Knowledge Transfer
Performance tuning (8s → 2s avg workflow execution), false positive reduction (12% → 3%), additional auto-remediation workflows for edge cases, comprehensive documentation, team became self-sufficient (can build new workflows without Technspire support)
Measurable Results (First 12 Months)
Incident Response & MTTR
Uptime & Reliability
Financial Impact
Engineer Experience
Technology Stack
n8n Workflow Platform
- n8n (Self-Hosted): 32 workflows, 480K executions/month
- Azure Kubernetes Service (AKS): 3 n8n replicas, auto-scaling
- PostgreSQL (Azure Database): Workflow history, incident tracking
- Azure Key Vault: Credential management for 18 systems
- Azure Monitor + App Insights: Workflow performance tracking
AI & Intelligence
- Azure OpenAI GPT-4: Alert severity classification, ChatOps
- Azure Machine Learning: Anomaly detection for traffic/latency
- Datadog: APM, infrastructure monitoring, log aggregation
- PagerDuty: On-call scheduling, incident management
- Slack: ChatOps bot, war room creation, notifications
Infrastructure & Operations
- kubectl API: AKS pod management (restart, scale, rollback)
- Azure CLI: Resource management automation
- Jira: Incident tracking, post-mortem management
- Confluence: Runbook documentation, knowledge base
- GitHub: Service ownership tracking, expert finder
Security & Compliance
- Trivy: Container vulnerability scanning
- Dependabot + Snyk: Code dependency scanning
- Statuspage: Customer-facing status updates
- Azure Cost Management: Cloud spend optimization
- Calendly: Post-mortem meeting scheduling
n8n workflows have been a game-changer for our IT operations. We went from drowning in 2,400 alerts per month to 220 actionable incidents - our on-call engineers can actually sleep at night now. The AI-powered triage gets it right 89% of the time, and 68% of incidents self-heal before anyone is paged. We've achieved 97% uptime, stopped losing customers to SLA breaches, and saved €1.4 million annually. Our engineers are happy again - turnover dropped from 38% to 18%, and they're shipping features instead of fighting fires. Technspire didn't just automate our runbooks - they built an intelligent ops platform that makes us look like wizards.
Lars Bergström
VP Engineering, Nordic SaaS Provider
€85M ARR • 850 Employees • 4.2K B2B Customers
Ready to Automate Your IT Operations with n8n?
Let's discuss how n8n workflows and Azure OpenAI can transform your incident management, reduce MTTR, and improve uptime while reducing engineer burnout.