Aurora vs Traditional Incident Management Tools
Compare Aurora's AI-powered agentic approach with traditional incident management platforms like Rootly, FireHydrant, incident.io, and Shoreline. Feature comparison, pricing, and use cases.
Aurora vs Traditional Incident Management Tools
Key Takeaway: Aurora automates the investigation itself using AI agents, while Rootly, FireHydrant, and incident.io automate the process around incidents (Slack channels, status pages, runbooks). Aurora is open source (Apache 2.0), free to self-host, and works with any LLM provider.
The incident management landscape has evolved significantly. The global IT incident management market is projected to reach $5.6 billion by 2028. While traditional platforms like Rootly, FireHydrant, and incident.io focus on workflow orchestration — automating Slack channels, status pages, and runbook execution — a new category of agentic tools is emerging. These tools don't just orchestrate humans; they autonomously investigate incidents using AI agents.
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 | Shoreline |
|---|---|---|---|---|---|
| Approach | Agentic AI investigation | Workflow automation | Workflow automation | Workflow automation | Runbook automation |
| AI Root Cause Analysis | Autonomous multi-step investigation | AI summaries | AI summaries | AI summaries | Pre-defined remediation |
| Cloud Providers | AWS, Azure, GCP, OVH, Scaleway | Via integrations | Via integrations | Via integrations | AWS, GCP |
| Infrastructure Execution | CLI commands in sandboxed pods | No | No | No | Runbook actions |
| Knowledge Base (RAG) | Vector search over runbooks/postmortems | No | No | No | No |
| Infrastructure Graph | Memgraph dependency mapping | No | No | No | Resource topology |
| Open Source | Yes (Apache 2.0) | No | No | No | No |
| Self-Hosted | Yes (Docker, Helm) | No | No | No | No |
| LLM Provider | Any (OpenAI, Anthropic, Google, Ollama) | Fixed | Fixed | Fixed | N/A |
| Kubernetes Native | Deep K8s investigation | Basic integration | Basic integration | Basic integration | K8s support |
| Pricing | Free (self-hosted) | Starts ~$2,000/mo | Starts ~$1,500/mo | Custom pricing | Custom pricing |
| Integrations | 22+ tools | 50+ tools | 40+ tools | 30+ tools | 20+ tools |
| Slack Integration | Yes | Core feature | Core feature | Core feature | Yes |
| Terraform/IaC Support | Native Terraform analysis | No | No | No | No |
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?.