Rootly Alternative: Open Source AI Incident Management

Key Takeaway: Rootly is an AI-native incident management platform with on-call, workflows, and AI SRE agents — starting at $20/user/month with AI SRE priced separately. Aurora is an open source (Apache 2.0) AI agent focused purely on autonomous incident investigation and root cause analysis. Rootly orchestrates your entire incident lifecycle. Aurora automates the hardest part — figuring out why something broke.

What is Rootly?

Rootly describes itself as an "AI-native incident management platform" — an all-in-one tool for detecting, managing, learning from, and resolving incidents. Founded in 2021, it's used by teams at Replit, NVIDIA, LinkedIn, Figma, and hundreds more, with a 4.8/5 rating on G2.

Rootly offers three products:

• Incident Response — Slack/Teams-native workflows, playbooks, roles, status pages, retrospectives
• On-Call — Schedules, escalation policies, alert routing, live call routing, mobile app
• AI SRE — Autonomous AI agents for root cause analysis, remediation, and alert triage

What is Aurora?

Aurora is an open source AI agent that automates incident investigation. When a monitoring tool fires an alert, Aurora's LangGraph-orchestrated agents autonomously query your infrastructure across AWS, Azure, GCP, OVH, Scaleway, and Kubernetes — correlating data from 25+ tools and delivering a structured root cause analysis with remediation recommendations.

Aurora doesn't manage your incident lifecycle. It investigates the root cause.

How They Compare

Incident Response & Coordination

Rootly is a full incident lifecycle platform:

• Slack and Microsoft Teams native incident channels
• Automated workflows (create channels, page responders, update status)
• Incident roles (commander, communications lead, etc.)
• Playbooks and runbooks
• Status pages (internal and external)
• Action item tracking with Jira sync
• DORA metrics and advanced analytics
• Mobile app (iOS and Android)

Aurora is not a full incident coordination platform — no roles or status pages. However, Aurora does create and manage Slack incident channels, tracks action items with Jira sync, sends investigation notifications, and supports @Aurora mentions in any channel for conversational investigation.

On-Call Management

Rootly has a full on-call product:

• Schedules with shadow rotations, holiday calendars, PTO overrides
• Escalation policies with gap detection
• SMS, voice, push notifications (bypass Do Not Disturb)
• Live call routing
• On-call pay calculator
• 99.99% uptime claim

Aurora has no on-call capabilities. No schedules, no paging, no escalation. For on-call, use Rootly, PagerDuty, Grafana OnCall, or Opsgenie alongside Aurora.

AI Investigation

This is where the tools diverge most.

Rootly AI SRE (rootly.com/ai-sre):

• Correlates alerts with code changes, deploys, and config changes
• Generates root cause analysis with confidence scores
• Surfaces similar past incidents and proven solutions
• Drafts remediation steps and PRs with suggested fixes
• AI Meeting Bot that transcribes incident bridges in real time
• @Rootly AI chat in Slack/Teams for summaries and task assignment
• MCP server for IDEs (Cursor, Windsurf, Claude Code)
• Chain-of-thought visibility ("see why a root cause is flagged")
• Whether it directly queries cloud infrastructure APIs is unverified

Aurora AI Investigation:

• Autonomous multi-step investigation using LangGraph-orchestrated agents
• Dynamically selects from 30+ tools per investigation
• Executes kubectl, aws, az, gcloud commands in sandboxed Kubernetes pods (non-root, read-only filesystem, capabilities dropped, seccomp enforced)
• Queries cloud APIs directly — AWS (STS AssumeRole), Azure (Service Principal), GCP (OAuth), OVH, Scaleway
• Traverses Memgraph infrastructure dependency graph for blast radius
• Searches Weaviate knowledge base (vector search over runbooks, past postmortems)
• Generates structured RCA with timeline, evidence citations, and remediation
• Suggests code fixes with diff preview — human approves and creates PR via GitHub and Bitbucket
• Exports postmortems to Confluence and Jira

Knowledge Base

Rootly: Surfaces similar past incidents during investigations. Integrates with Glean for broader knowledge search. No native vector search product.

Aurora: Built-in Weaviate-powered vector store. Upload runbooks, past postmortems, and documentation — the AI agent searches them using semantic similarity during every investigation.

Postmortems

Rootly: AI-generated retrospectives with context, timelines, and custom templates. Collaborative editing. Jira sync for action items.

Aurora: AI-generated postmortems with timeline, root cause, impact assessment, and remediation steps. One-click export to Confluence and Jira.

Feature Comparison

Rootly has, Aurora doesn't:

• On-call scheduling, escalation policies, paging (SMS/voice/push)
• Microsoft Teams support (Aurora is Slack-only)
• Automated incident workflows (create channels, page responders, update status)
• Status pages (internal and external)
• Incident roles
• DORA metrics and analytics
• Mobile app (iOS, Android)
• MCP server for IDEs
• AI Meeting Bot for incident bridges
• SOC 2 Type II, HIPAA, GDPR, CCPA compliance
• 70+ integrations

Aurora has, Rootly doesn't (or is unverified):

• Direct cloud infrastructure querying (AWS, Azure, GCP, OVH, Scaleway APIs)
• CLI command execution in sandboxed Kubernetes pods
• Native vector search knowledge base (Weaviate RAG)
• Infrastructure dependency graph (Memgraph)
• Terraform/IaC state analysis
• Open source (Apache 2.0) — full codebase auditable
• Self-hosted deployment (Docker Compose, Helm)
• LLM provider flexibility including local models (Ollama for air-gapped)
• Free — no per-user or per-incident pricing

Both have:

• AI-powered root cause analysis
• AI-suggested code fixes (human-approved PRs)
• Automated postmortem generation
• PagerDuty, Datadog, Grafana integrations
• GitHub integration
• Confluence integration
• HashiCorp Vault integration
• BYOK for LLM providers (Rootly: OpenAI, Anthropic, Gemini; Aurora: OpenAI, Anthropic, Google, Ollama, OpenRouter, Vertex AI)
• Slack incident channels
• Action item tracking with Jira sync

Pricing

Rootly (rootly.com/pricing):

• Incident Response Essentials: $20/user/month
• On-Call Essentials: $20/user/month
• AI SRE: Contact sales (no published price)
• Enterprise tiers: Contact sales
• Bundle discounts available for IR + On-Call + AI SRE
• Startup discount: up to 50% off (<100 employees, <$50M raised)
• Free 14-day trial

Aurora:

• Free — Apache 2.0, self-hosted
• Costs: infrastructure (VM or K8s cluster) + LLM API usage
• $0 LLM cost possible with Ollama local models

Example: 20-person SRE team

For Rootly IR + On-Call: $20 + $20 = $40/user/month x 20 = $800/month (before AI SRE add-on, which is priced separately via sales).

For Aurora: $0 + infrastructure + LLM API.

Note: Rootly pricing from rootly.com/pricing. AI SRE pricing is not publicly listed.

Open Source vs SaaS

Rootly is SaaS-only. The core platform is proprietary. They have open source tooling on GitHub (Terraform provider with 400,000+ downloads, Backstage plugin, CLI, SDKs) but not the platform itself.

Aurora is fully open source under Apache 2.0. The entire codebase — backend, frontend, agent orchestration — is on GitHub. You can:

• Audit exactly what the AI does on your infrastructure
• Modify investigation workflows and add custom tools
• Fork and customize for your organization
• Run fully air-gapped with local LLMs via Ollama
• Keep all incident data in your own environment

When to Choose Rootly

Rootly is the better choice when:

• You need a full incident lifecycle platform — on-call, workflows, status pages, roles, retrospectives, DORA metrics in one tool
• Slack/Teams-native workflows matter — Rootly's incident channels and AI chat are deeply embedded in collaboration tools
• Compliance requirements — SOC 2 Type II, HIPAA, GDPR out of the box
• You want managed SaaS — no infrastructure to maintain
• You need a mobile app — iOS and Android for on-call
• Enterprise support — dedicated support, SLAs, BAA for HIPAA

When to Choose Aurora

Aurora is the better choice when:

• Investigation is your bottleneck — your team spends hours diagnosing incidents manually
• You need deep cloud investigation — AI agents that directly query AWS, Azure, GCP, and Kubernetes
• You want open source — full transparency into how AI investigates your infrastructure
• Self-hosted is required — compliance, data sovereignty, or air-gapped environments
• Budget is limited — free forever, no per-user pricing
• LLM flexibility matters — bring any provider, including local models
• You already have on-call — PagerDuty, Grafana OnCall, or Opsgenie handles paging; you need the investigation layer
• You want a custom integration — Aurora is open source and the Arvo AI team actively builds custom integrations for companies that need them — at no cost. If there's a feature gap, reach out and they'll build it with you.

Using Rootly + Aurora Together

They're not mutually exclusive. Rootly manages your incident lifecycle; Aurora investigates the root cause:

1. Alert fires → Rootly creates incident channel, pages on-call
2. Same alert → Aurora receives webhook, starts AI investigation
3. Rootly coordinates the response (roles, comms, status page)
4. Aurora investigates in the background (queries cloud, checks K8s, searches knowledge base)
5. On-call SRE finds Aurora's completed RCA with root cause and remediation
6. Aurora generates postmortem → exports to Confluence
7. Rootly tracks action items → syncs to Jira

Getting Started with Aurora

git clone https://github.com/Arvo-AI/aurora.git
cd aurora
make init
make prod-prebuilt

Configure your monitoring webhooks (PagerDuty, Datadog, Grafana), add cloud provider credentials, and investigations start automatically. See the full documentation for deployment guides.

Learn more at arvoai.ca or read the full documentation. For comparisons with other tools, see Aurora vs Traditional Incident Management Tools and PagerDuty Alternative for Root Cause Analysis.

Every claim in this post is sourced from official websites and public repositories. Rootly data from rootly.com. Aurora data from github.com/Arvo-AI/aurora. Last verified: April 2026.