Keep: Building an Open-Source AIOps Platform That Actually Uses AI
Hook
Most ‘AIOps’ platforms are just rule engines with marketing departments. Keep is different: it ships with support for seven AI backends, from OpenAI to locally-hosted Ollama, and uses them for actual alert correlation—not just chatbot interfaces.
Context
If you’ve worked in SRE or platform engineering, you know the alert fatigue problem: Prometheus fires alerts, Datadog sends notifications, PagerDuty escalates incidents, and somewhere in Slack, your team is trying to figure out which of the 47 alerts from the last hour actually matter. Traditional monitoring tools operate in silos, each with their own alert semantics and routing logic. You end up with alerts scattered across systems, duplicate notifications for the same underlying issue, and no programmatic way to correlate a database slowdown alert with the deployment that caused it.
The enterprise solution has been tools like PagerDuty or Opsgenie, which aggregate alerts but lock you into expensive SaaS contracts with limited customization. The open-source answer has been… fragmented. AlertManager handles Prometheus alerts well but nothing else. Grafana OnCall focuses on scheduling. StackStorm can automate responses but isn’t monitoring-native. Keep positions itself as the missing piece: an open-source, AI-powered alert aggregation platform that treats alert management as a first-class problem, with bidirectional integrations, programmable workflows, and LLM-based correlation built in from day one.
Technical Insight
Keep’s architecture centers on a provider-first design pattern that abstracts the chaos of different monitoring tools into a unified alert model. The platform ships with numerous providers covering everything from observability tools (Datadog, New Relic, Prometheus, Dynatrace, Coralogix, CloudWatch) to AI backends (OpenAI, Anthropic, Gemini, Ollama, DeepSeek, Grok, LlamaCPP) to communication platforms.
What makes this interesting is the workflow engine, which Keep describes as ‘GitHub Actions for monitoring.’ Workflows are defined in YAML and can trigger on alert conditions, enrich alerts with additional context, correlate related alerts, and execute automated responses. The workflow engine supports conditional logic and automated actions, making it genuinely programmable for complex alert handling scenarios.
The AI integration is where Keep diverges from traditional alert managers. Instead of requiring you to manually write correlation rules, Keep can use LLMs to analyze alert content and determine relationships. The platform supports multiple AI backends through a pluggable provider pattern. This abstraction means you can start with OpenAI for quick setup, switch to Anthropic for different capabilities, or move to self-hosted Ollama for data sovereignty—without changing your correlation logic. The AI features extend beyond correlation to incident summarization and enrichment suggestions, turning LLMs into operational tools rather than just chat interfaces.
The bidirectional sync capability is architecturally significant. Most alert aggregation tools are one-way: they ingest alerts but can’t push state back. Keep maintains sync state for each provider, allowing it to create incidents in external systems, update tickets as alerts resolve, or post resolution notes back to communication platforms—all from a single platform. This turns Keep into a true integration layer rather than just a dashboard.
The platform appears to run as a multi-tier stack with a Python-based backend for alert ingestion and workflow execution. You can self-host the entire stack via Docker Compose or deploy to Kubernetes using Helm charts. For teams not wanting to manage infrastructure, Keep also offers a hosted SaaS version at platform.keephq.dev.
Gotcha
The AI-powered correlation sounds impressive until you realize it’s only as good as the LLM you configure and the context you provide. In testing, generic alert messages like ‘Service Unhealthy’ or ‘High CPU’ likely don’t give LLMs enough signal to correlate meaningfully—you need alerts with rich metadata (service names, error messages, stack traces) for AI correlation to add value over simple rule-based grouping. This means Keep’s AI features work best when you’ve already invested in good alert hygiene, which is exactly what most teams struggling with alert fatigue haven’t done.
The workflow engine, while powerful, requires you to learn yet another YAML DSL. If you’re already managing GitHub Actions, Argo Workflows, and Terraform configurations, adding Keep workflows to your YAML pile might feel like more cognitive overhead than value—especially for simple use cases where a webhook to a script would suffice. The expressiveness is there for complex scenarios, but the learning curve isn’t trivial.
As an open-source project with 11.5K stars, Keep has momentum, but it’s young compared to established enterprise competitors. Production stability, especially around the workflow engine’s error handling and the AI correlation’s rate limiting and fallback behavior, will need real-world hardening. Self-hosting also means you’re responsible for securing API keys for potentially dozens of provider integrations—Keep stores these encrypted, but key rotation and access control become your operational burden.
Verdict
Use Keep if you’re managing alerts from 5+ different monitoring tools and need a unified layer with programmable workflows—especially if you want to experiment with AI-powered correlation without committing to a single LLM vendor. It’s ideal for platform engineering teams comfortable self-hosting Python applications who value customization and open-source flexibility over vendor support contracts. The workflow engine genuinely enables alert automation patterns that are painful to build with webhook glue code. Skip Keep if you’re operating a simple monitoring stack (just Prometheus or just Datadog) where native features suffice, if you need enterprise SLAs and 24/7 vendor support, or if you’re not ready to operationalize another piece of infrastructure. For small teams without complex multi-tool environments, the operational overhead of running Keep outweighs the benefits—stick with your monitoring tool’s built-in alert manager and upgrade when the pain justifies the complexity.
