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Rootly

Rootly

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What you will be able to buy

Plans

Incident Response Essentials

  • Incident response inside Slack or Microsoft Teams
  • Chat with @Rootly, similar past incident matching and an AI scribe on calls
  • Retrospectives, status page and mobile app
  • Advanced incident metrics and dashboards
  • SAML single sign-on, purchasable self-serve on the site

On-Call Essentials

  • Alert grouping and noise reduction with alert routes
  • Schedules, escalation policies and shadow rotations
  • Live call routing with a call routing tree
  • Holiday calendars, PTO, overrides and cover requests
  • On-call pay calculator, schedule gap detection and fallback
  • Sync to Slack user groups

AI SRE

  • Probable root cause with supporting evidence
  • Correlation of alerts with changes: deployments, configs, feature flags
  • Impact analysis and links to related incidents
  • Drafted remediation steps and a suggested fix as a pull request
  • Suppression of noisy alerts and paging the responder who is actually needed
  • Works with Rootly or standalone; reachable through the API and MCP
  • Available under a contract only

Enterprise

  • Everything in Essentials at organization scale
  • Custom forms, custom incident types and private incidents
  • Native secrets management, custom data retention and audit logs
  • Advanced workflows with SAML and SCIM single sign-on
  • Dynamic escalation paths and unlimited schedules in on-call
  • Bring your own OpenAI, Anthropic and Gemini keys, priority round-the-clock support
  • Scope and terms agreed under a contract

Plan contents as published by the vendor; seller prices arrive at launch.

About the service

Three products you can buy together or separately: on-call with schedules and escalations, incident response inside Slack and Teams, and an AI SRE agent that hunts for the cause of a failure. The agent starts the moment an alert fires and tests several hypotheses in parallel — telemetry, recent commits, feature-flag and configuration changes, the history of similar incidents — then returns ranked theories with confidence scores. It also correlates a deployment with a metric dip, assesses blast radius, suppresses noisy pages, and drafts remediation steps together with a candidate fix, but never applies anything without a human. Alongside it sit an AI scribe on incident calls, matching against similar past outages and auto-generated retrospectives. You can bring your own model keys, and the agent plugs into the rest of your stack — Datadog, GitHub, Jira — and can be driven through the API and MCP.

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