At a Glance:
Keep is an open-source AIOps and alert management platform that provides a single pane of glass for alert deduplication, enrichment, filtering, correlation, bi-directional integrations, and declarative YAML-based workflows.
Overview:
Keep is an open-source alert management and AIOps platform designed to centralize and automate incident response. It aggregates alerts from multiple observability, monitoring, and communication tools into a single customizable UI, applying deduplication, correlation, filtering, and enrichment to reduce noise. It supports bi-directional integrations and allows users to define declarative, YAML-based workflows that automate actions like ticket creation and notifications. The platform is built for engineering teams that need to manage alerts from a diverse tooling ecosystem, offering deployment flexibility with options for on-premises or air-gapped environments. Its AI-powered correlation and summarization are supported by multiple configurable AI backends.
Key Decision Points:
Deployment: Supports on-premises and air-gapped deployments, with a cloud-agnostic architecture suitable for organizations with strict hosting requirements.
Workflow model: Alert automation is managed through declarative YAML workflows, with distinct triggers, steps, and actions, similar to a CI/CD pipeline for monitoring.
Integration scope: Offers a deep ecosystem of bi-directional providers spanning observability tools, communication platforms, incident management, ticketing systems, databases, and container orchestrators.
Alert processing: Includes deduplication, correlation, filtering, and enrichment to reduce alert fatigue before alerts become incidents.
AI flexibility: AI-powered features like correlation and summarization can be powered by various backends, including OpenAI, Anthropic, Ollama, and others.
Access control: Provides enterprise authentication support (SSO, SAML, OIDC, LDAP) with granular RBAC and ABAC permissions for team management.
Core Features:
Unified alert dashboard: A customizable single-pane-of-glass UI for visualizing all alerts and incidents from integrated tools.
Alert deduplication and correlation: Reduces redundant alerts by deduplicating and correlating them into consolidated, actionable incidents.
Declarative workflows: YAML-defined automations that trigger on alerts, perform enrichment steps, and execute actions such as creating Jira tickets or sending Slack messages.
Bi-directional provider integrations: Maintains state between Keep and connected tools like PagerDuty, Datadog, Jira, and Kubernetes, enabling automatic ticket syncing and status updates.
AI-powered summarization: Uses configurable AI backends to generate incident summaries and correlate events for faster investigation.
Customizable alert enrichment: Allows fetching additional context from databases, webhooks, or custom scripts before determining the next step in a workflow.
Use Cases:
Centralized alert triage: Platform engineering teams can consolidate alerts from disparate monitoring tools like Prometheus, Datadog, and CloudWatch into a single interface.
Noise reduction for on-call teams: DevOps and SRE teams can configure deduplication and correlation rules to ensure only meaningful, enriched incidents are routed to on-call responders.
Automated incident response: Developers can write YAML workflows to automatically create tickets in Jira or Linear, and notify stakeholders in Slack or Teams based on specific alert triggers.
Air-gapped incident management: Organizations with strict data residency or security requirements can deploy Keep on-premises or in disconnected environments to manage their entire alert lifecycle.
Open-Source Alternative Value:
Keep provides an open-source path to a centralized alert management and AIOps workflow, which is typically delivered through a patchwork of commercial SaaS products. Its value lies in its deep, bi-directional integrations with existing observability and incident management tools and its declarative workflow engine that treats alert automation as code. The platform can be run on-premises or in air-gapped environments, offering a deployment model that matches organizations moving away from cloud-only commercial products like PagerDuty or Datadog's alerting suite. Its AI capabilities are decoupled from a single vendor, supporting multiple AI backends for summarization and correlation.
