Keep: Building an Open-Source AIOps Platform That Actually Reduces Alert Fatigue

Hook

The average DevOps team receives over 3,500 alerts per week, and 99% of them are noise. Keep tackles this with an architecture that treats alert correlation as a first-class citizen, not an afterthought.

Context

Modern infrastructure monitoring has become a fragmented nightmare. You're running Prometheus for metrics, Sentry for errors, DataDog for APM, CloudWatch for AWS resources, and PagerDuty for incidents. Each tool sends its own alerts, formatted differently, with different severity levels, and zero awareness of what the others are doing. A single production issue can trigger dozens of redundant alerts across systems, creating what the industry politely calls "alert fatigue" and what on-call engineers call "hell."

The traditional solution has been commercial incident management platforms that centralize notifications but don't fundamentally solve the correlation problem. They'll route alerts to the right person, but they won't tell you that fifteen alerts are actually describing the same database outage. Keep approaches this differently: it's an open-source AIOps platform built from the ground up to ingest alerts from anywhere, apply intelligent correlation using AI, deduplicate aggressively, and let you build custom automation workflows. Instead of just managing alert noise, it actively reduces it.

Technical Insight

Keep's architecture centers on three core components: a provider system for bidirectional integrations, a correlation engine powered by pluggable LLMs, and a workflow automation system that feels like GitHub Actions for your monitoring infrastructure.

The provider system is where Keep gets interesting. Rather than building point-to-point integrations, Keep uses a provider abstraction that currently supports over 100 monitoring tools. Each provider implements a simple interface for both pulling alerts and pushing updates back. Here's what a basic workflow looks like using Keep's declarative syntax:

workflow:
  id: enrich-and-dedupe-database-alerts
  triggers:
    - type: alert
      filters:
        - key: source
          value: prometheus
        - key: labels.service
          value: postgres
  actions:
    - name: enrich-with-context
      provider:
        type: postgres
        config: "{{ providers.prod_db }}"
        with:
          query: "SELECT connection_count, replication_lag FROM pg_stats"
    - name: correlate-similar
      provider:
        type: ai
        config:
          model: gpt-4
          prompt: "Group these alerts if they describe the same incident"
    - name: create-incident
      provider:
        type: pagerduty
        with:
          title: "{{ alert.name }}"
          enrichment: "{{ steps.enrich-with-context.results }}"
          correlation_id: "{{ steps.correlate-similar.group_id }}"

This workflow triggers on Prometheus alerts for PostgreSQL, enriches them with live database metrics, uses AI to correlate similar alerts, and creates a single PagerDuty incident instead of one per alert. The bidirectional nature means Keep can also update the incident in PagerDuty when the alert resolves.

The correlation engine is where Keep's AI-native design shines. Under the hood, it uses a pluggable LLM backend system that supports OpenAI, Anthropic, Azure OpenAI, and local models via Ollama. The correlation process works in two modes: rule-based for deterministic cases, and AI-powered for fuzzy matching. The AI correlation extracts semantic meaning from alert descriptions, groups them by root cause, and assigns confidence scores. This happens asynchronously to avoid blocking alert ingestion, with results cached in Redis for performance.

The database layer supports MySQL, PostgreSQL, and SQLite (for development) through SQLAlchemy. Alert data is stored in a normalized schema with separate tables for alerts, incidents, and correlation groups. Keep implements a clever deduplication strategy using fingerprinting: each alert gets hashed based on configurable fields (source, severity, labels), and duplicates within a time window get collapsed. The fingerprinting is exposed through the API:

from keep.api.core.db import get_alerts
from keep.api.models.alert import AlertDto

# Custom fingerprinting for your alert sources
fingerprint_fields = ['source', 'labels.host', 'labels.alertname']
alert = AlertDto(
    source='prometheus',
    name='High CPU Usage',
    severity='critical',
    labels={'host': 'web-1', 'alertname': 'HighCPU'}
)

# Keep automatically deduplicates based on fingerprint
fingerprint = alert.generate_fingerprint(fields=fingerprint_fields)

Deployment happens primarily via Docker Compose for single-node setups or Kubernetes for production. The architecture separates the API backend (FastAPI), frontend (Next.js), database, and Redis. What's clever is how Keep handles alert ingestion at scale: it uses a queue-based system where alerts are immediately acknowledged and processed asynchronously, preventing backpressure from slow correlation or enrichment operations. The API exposes webhooks that monitoring tools can push to, eliminating polling overhead.

One architectural choice worth highlighting: Keep stores alert history indefinitely by default (configurable via retention policies), which enables temporal correlation. This means it can recognize patterns like "this alert always precedes that alert by 5 minutes" and proactively group them, something most incident management tools can't do because they don't maintain sufficient history.

Gotcha

Keep's AI-powered correlation is its headline feature, but it introduces latency and unpredictability that might frustrate teams expecting deterministic behavior. LLM calls can take seconds, and the quality of correlation varies significantly based on how well-structured your alert descriptions are. If your monitoring tools emit terse, cryptic alerts like "Error code 5023," the AI has nothing semantic to work with. You'll need to invest time enriching alerts with context, either at the source or through Keep workflows, before the AI correlation delivers value. The LLM costs can also add up quickly at high alert volumes—running correlation on thousands of alerts daily through OpenAI's API isn't cheap.

The self-hosting operational burden is non-trivial. You're managing a multi-service application with a database, cache layer, frontend, and backend, each with their own scaling characteristics. The documentation covers the happy path well, but production edge cases (database migrations during upgrades, Redis persistence configuration, handling LLM API rate limits) require diving into the codebase. The project is moving fast—monthly releases introduce breaking changes occasionally, and the upgrade path isn't always smooth. If you're expecting a turnkey solution like commercial SaaS offerings, Keep will demand more hands-on maintenance than you might anticipate. The cloud-hosted option exists but defeats the purpose for teams choosing Keep specifically for self-hosting and data sovereignty.

Verdict

Use if: You're managing alerts from 5+ monitoring tools and spending significant engineering time correlating incidents manually, you want to build custom alert automation workflows that go beyond simple routing, you need a self-hosted solution for compliance or data sovereignty, or you're already comfortable operating multi-service Python applications in production. Keep's workflow engine and extensive provider ecosystem make it particularly valuable for platform teams building internal developer platforms. Skip if: You have a simple monitoring stack with one or two tools (Alertmanager alone is simpler), you're resource-constrained and can't dedicate engineering time to maintaining another service, you need battle-tested reliability and can afford commercial solutions like PagerDuty, or your alerts are poorly structured and you're hoping AI will magically fix that without upstream investment. Keep is a power tool that rewards teams willing to invest in configuration and maintenance.