Rootly
Unclaimed verified 20 oct 2026AI-powered incident management and on-call operations for modern engineering teams.
TL;DR
Rootly is a Slack- and Microsoft Teams-centered platform for incident response, on-call management, workflow automation, and post-incident analysis. It is aimed primarily at engineering, SRE, DevOps, and security teams, with its combination of broad incident-lifecycle automation and AI-assisted operations serving as its key differentiator.
What Users Actually Pay
No user-reported pricing yet.
Our Take
Rootly occupies the modern incident-management layer between dedicated paging tools such as PagerDuty and broader IT service-management suites such as Jira Service Management and ServiceNow. Its primary value proposition is coordinating the complete incident lifecycle—from responder mobilization and stakeholder communication through timeline creation, retrospectives, and follow-up work—while keeping much of the experience inside Slack or Microsoft Teams. Its strongest apparent advantage is workflow centralization. Rootly can automate incident-channel creation, role assignment, escalation, communications, reminders, status updates, timeline generation, and postmortem processes. User feedback is generally positive about ease of use, Slack integration, automation, setup speed, visibility, and vendor support. The platform’s combination of incident response and on-call functionality can also reduce the need for multiple operational tools. The primary trade-off is breadth and complexity. Reviewers report that the number of configuration options can create a learning curve, and some teams may encounter notification noise if workflows are not carefully designed. Feedback also points to possible gaps in native integrations, complex scheduling, automatic shift management, and certain advanced use cases compared with more established competitors. These concerns are not necessarily universal, but they warrant hands-on validation. Rootly is best suited to growing SaaS and technology companies with recurring production incidents, a strong Slack or Teams culture, and enough operational maturity to define escalation policies, incident roles, runbooks, and postmortem practices. Enterprise buyers should evaluate advanced scheduling, integrations, permissions, retention, security controls, notification behavior, and total bundled pricing during a trial.
Alternatives
Ranked by Revuo score — paid tiers never affect order.Pros
- + Strong Slack and Microsoft Teams integration that reduces context switching during incidents.
- + Broad incident-lifecycle automation covering responder coordination, communications, timelines, retrospectives, and follow-up actions.
- + Generally easy to adopt and configure compared with heavier enterprise ITSM platforms.
- + Positive user feedback about customer support and vendor responsiveness.
- + Growing AI capabilities for incident summaries, scribing, investigation support, impact analysis, and remediation suggestions.
Cons
- - The number of workflows and configuration options can feel overwhelming for new users.
- - Some native integrations or integration depth may lag larger competitors, requiring webhooks or custom workarounds.
- - Advanced on-call scheduling and automatic shift-management scenarios may require additional validation.
- - Notification volume can become excessive in larger organizations or poorly tuned workflows.
- - Independent review coverage outside G2 is sparse, limiting the reliability of aggregate ratings.
MCP Integrations
6 servers19 tools91 total usesDetect AI-generated images, videos, and audio with identifAI's deepfake detection tools.
19 tools
classify_imageUpload an image file to detect whether it is human-made or AI-generated. Provide the image content as a base64-encoded string. Returns a classification identifier for async result retrieval. WARNING: base64 encoding adds ~33% overhead to the original file size. For images larger than 4 MB, use classify_image_url instead and provide a publicly accessible URL to avoid payload size issues. Authentication: provide your Identifai API key via the apiKey parameter or configure the X-Api-Key HTTP header in your MCP client (recommended).classify_image_urlSubmit a publicly accessible image URL to detect whether it is human-made or AI-generated. Returns a classification identifier for result retrieval. Supports the same analysis options as file-based classification. Authentication: provide your Identifai API key via the apiKey parameter or configure the X-Api-Key HTTP header in your MCP client (recommended).get_image_classificationRetrieve the classification result for a previously submitted image. Use the identifier returned by classify_image or classify_image_url. Poll this endpoint until the result is available.get_all_image_classificationsRetrieve classification results for multiple images in a single request. Accepts up to 100 identifiers. Useful for batch result polling.get_classification_heatmapRetrieve a visual heatmap highlighting which regions of the image were detected as AI-generated. Requires the classification to have been submitted with withHeatmap enabled.override_image_classificationManually override the classification verdict for a previously classified image. Sets the result to either "human" or "artificial". Used for corrections and feedback.classify_videoUpload a video file to detect whether it is human-made or AI-generated. Provide the video content as a base64-encoded string. The video is split into frames which are individually classified. Returns a classification identifier for async result retrieval. WARNING: base64 encoding adds ~33% overhead to the original file size. For videos larger than 10 MB, use classify_video_url instead and provide a publicly accessible URL to avoid payload size issues. Authentication: provide your Identifai API key via the apiKey parameter or configure the X-Api-Key HTTP header in your MCP client (recommended).classify_video_urlSubmit a publicly accessible video URL for AI-generated content detection. Supports the same frame extraction and analysis options as file-based classification. Authentication: provide your Identifai API key via the apiKey parameter or configure the X-Api-Key HTTP header in your MCP client (recommended).get_video_classificationRetrieve the classification result for a previously submitted video. Poll until the result is ready — video classification is always asynchronous.get_all_video_classificationsRetrieve classification results for multiple videos in a single request. Accepts up to 100 identifiers.override_video_classificationManually override the classification verdict for a previously classified video. Sets the result to either "human" or "artificial".classify_audioUpload an audio or speech file to detect whether it is human-recorded or AI-synthesized. Provide the audio content as a base64-encoded string. Returns a classification identifier for async result retrieval. WARNING: base64 encoding adds ~33% overhead to the original file size. For audio files larger than 10 MB, use classify_audio_url instead and provide a publicly accessible URL to avoid payload size issues. Authentication: provide your Identifai API key via the apiKey parameter or configure the X-Api-Key HTTP header in your MCP client (recommended).classify_audio_urlSubmit a publicly accessible audio URL for AI-generated speech detection. Returns a classification identifier for async result retrieval. Authentication: provide your Identifai API key via the apiKey parameter or configure the X-Api-Key HTTP header in your MCP client (recommended).get_audio_classificationRetrieve the classification result for a previously submitted audio file. Poll until the result is ready.get_all_audio_classificationsRetrieve classification results for multiple audio files in a single request. Accepts up to 100 identifiers.override_audio_classificationManually override the classification verdict for a previously classified audio file. Sets the result to either "human" or "artificial".get_user_creditsRetrieve the current available and used classification credits for the authenticated Identifai account. Use this to check quota status before submitting large batches.submit_tampering_ticketsSubmit one or more ticket images (as base64-encoded strings) to the Identifai v2 API for batch tampering detection. Each ticket is analysed independently; results are retrieved asynchronously via get_tampering_batch_results using the returned batch_id. Supports PDF files (each page becomes a separate analysis entry). Maximum 10 tickets per batch. Authentication: provide your Identifai API key via the apiKey parameter or configure the X-Api-Key HTTP header in your MCP client (recommended).get_tampering_batch_resultsRetrieve the tampering detection results for a previously submitted batch of tickets. Poll until the "done" field is true. Each result contains a verdict ("authentic" or "tampered") and per-heuristic verdicts.
Manage incident alerts, events, and workflows with custom automations
Needs a self-provisionable API key
Incident management, on-call scheduling, and intelligent analysis powered by Rootly.
Needs a self-provisionable API key
Every Rootly incident, alert, and on-call object as a typed command - plus a local SQLite mirror
Needs a self-provisionable API key
MCP server for Rootly — incidents, alerts, escalations, and post-incident reports.
Needs a self-provisionable API key
MCP server for Rootly — incidents, alerts, escalations, and post-incident reports.
Last checked Oct 8, 2026
Compare With
Reviews
No reviews yet. Be the first to review Rootly!