Aurora vs Traditional Incident Management Tools
Compare Aurora's open source agentic AI approach with incident management platforms like Rootly, FireHydrant, and incident.io. Verified feature comparison, pricing, and use cases.
Key Takeaway: Aurora is an open source (Apache 2.0) AI agent that autonomously investigates cloud incidents across AWS, Azure, GCP, and Kubernetes. While Rootly and incident.io have added AI agents to their workflow platforms, Aurora is the only option that is fully open source, self-hosted, and works with any LLM provider including local models.
The incident management landscape has evolved significantly. The global IT incident management market is projected to reach $5.6 billion by 2028. Platforms like Rootly and incident.io have evolved from pure workflow automation to include AI agents, while FireHydrant focuses on workflow automation with AI-enhanced summaries. Aurora takes a different approach — it's built from the ground up as an agentic investigation tool, open source, and self-hosted.
This guide provides an honest comparison of Aurora against the leading incident management platforms to help you choose the right tool for your team.
How Aurora Differs
Aurora takes a fundamentally different approach to incident management. Instead of automating the process around incident response (creating channels, paging people, running predefined workflows), Aurora automates the investigation itself.
When an incident is triggered, Aurora's AI agents:
- Autonomously query your infrastructure across multiple cloud providers
- Execute CLI commands in sandboxed pods to gather real data
- Search your knowledge base for relevant runbooks and past incidents
- Build a dependency graph to assess blast radius
- Synthesize findings into a structured root cause analysis
This is the difference between workflow automation and agentic investigation.
Feature Comparison
| Feature | Aurora | Rootly | FireHydrant | incident.io |
|---|---|---|---|---|
| Approach | Agentic AI investigation | Workflow + AI agents | Workflow automation | Workflow + AI agents |
| AI Root Cause Analysis | Autonomous multi-step | Autonomous multi-step | Post-incident summaries | Autonomous multi-agent |
| Cloud Providers | AWS, Azure, GCP, OVH, Scaleway | Not specified | Not specified | Not specified |
| Infrastructure Execution | CLI commands in sandboxed pods | No | No | No |
| Knowledge Base (RAG) | Weaviate vector search | Past incidents | No | Past incidents |
| Infrastructure Graph | Memgraph dependency mapping | No | Service Catalog | No |
| Open Source | Yes (Apache 2.0) | No | No | No |
| Self-Hosted | Yes (Docker, Helm) | No | No | No |
| LLM Provider | Any (OpenAI, Anthropic, Google, Ollama) | Multi-provider (BYOK) | Undisclosed | Undisclosed |
| Code Fix PRs | Yes | Not verified | No | Yes |
| Pricing | Free (self-hosted) | From $20/user/mo | From $9,600/yr | From $15/user/mo (free Basic tier) |
| Integrations | 22+ tools | 50+ tools | 40+ tools | 30+ tools |
| Terraform/IaC Support | Native Terraform analysis | No | No | No |
Note: Shoreline was acquired by NVIDIA and is no longer an independent product. Data verified from official sources as of March 2026.
When to Choose Aurora
"We evaluated Rootly and FireHydrant but chose Aurora because we needed AI that actually investigates, not just routes alerts to Slack. The open-source model meant we could audit exactly what the AI was doing on our infrastructure." — Early Aurora adopter
Aurora is the best fit when your team needs:
- Autonomous investigation: You want AI that actually investigates incidents, not just summarizes them.
- Multi-cloud environments: You run infrastructure across AWS, Azure, GCP, OVH, or Scaleway and need unified incident investigation.
- Open source and self-hosted: You need to keep incident data in your own environment for compliance or security reasons.
- LLM flexibility: You want to choose your own LLM provider, or run models locally with Ollama.
- Deep Kubernetes support: Your infrastructure is heavily Kubernetes-based and you need deep pod-level investigation.
- Infrastructure as Code: You use Terraform and want the AI to understand your IaC state.
When to Choose Traditional Tools
Rootly, FireHydrant, or incident.io may be better when:
- Process orchestration is the priority: Your main need is automating Slack channel creation, status pages, and stakeholder communication.
- Larger ecosystem: You need 50+ integrations out of the box.
- Managed service: You prefer SaaS over self-hosted.
- Established workflows: Your team has mature incident processes and just needs tooling to automate them.
The Open Source Advantage
Aurora's Apache 2.0 license means:
- No vendor lock-in: Deploy on your infrastructure, use your LLM provider, keep your data.
- Full transparency: Audit exactly how the AI investigates your incidents.
- Community-driven: Contribute integrations, tools, and improvements.
- Cost efficiency: No per-seat or per-incident pricing. Self-hosted is completely free.
- Customization: Modify investigation workflows, add custom tools, integrate with internal systems.
Getting Started
Try Aurora alongside your existing tooling — it complements rather than replaces workflow platforms:
git clone https://github.com/Arvo-AI/aurora.git
cd aurora
make init
make prod-prebuilt
Aurora can receive webhooks from PagerDuty, Datadog, and Grafana, running AI-powered investigations in the background while your existing incident process continues.
Learn more at arvoai.ca or read the full documentation. For a deeper look at how agentic investigation works, see our guide on What is Agentic Incident Management?.