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Siddharth Singh
Siddharth Singh

Posted on • Originally published at arvoai.ca

Aurora vs Traditional Incident Management Tools: An Honest Comparison

#devops #ai #sre #opensource

The incident management market is projected to reach $5.6 billion by 2028. But not all incident management tools solve the same problem.

Traditional platforms like Rootly, FireHydrant, and incident.io focus on workflow automation — automating Slack channels, status pages, and runbook execution. A new category of agentic tools is emerging that automates the investigation itself.

This guide provides an honest comparison to help you choose the right approach for your team.

The Core Difference

Workflow automation:

  • An incident fires → tool creates a Slack channel → pages the on-call → runs a predefined runbook → generates a status page update
  • Humans still investigate the root cause

Agentic investigation (Aurora):

  • An incident fires → AI agent autonomously queries your infrastructure → runs CLI commands in sandboxed pods → searches your knowledge base → delivers a root cause analysis
  • The AI investigates. Humans review and remediate.

"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

Feature Comparison

Approach:

  • Aurora: Agentic AI investigation
  • Rootly: Workflow automation
  • FireHydrant: Workflow automation
  • incident.io: Workflow automation
  • Shoreline: Runbook automation (acquired by NVIDIA)

AI Root Cause Analysis:

  • Aurora: Autonomous multi-step investigation
  • Rootly: AI summaries
  • FireHydrant: AI summaries
  • incident.io: AI summaries

Cloud Providers:

  • Aurora: AWS, Azure, GCP, OVH, Scaleway natively
  • Others: Via integrations only

Infrastructure Execution:

  • Aurora: CLI commands in sandboxed pods
  • Others: No direct infrastructure execution

Knowledge Base (RAG):

  • Aurora: Vector search over runbooks and postmortems
  • Others: None

Infrastructure Graph:

  • Aurora: Memgraph dependency mapping
  • Others: None (Shoreline had resource topology)

Open Source:

  • Aurora: Yes (Apache 2.0)
  • All others: No

Self-Hosted:

  • Aurora: Yes (Docker, Helm)
  • All others: No

LLM Provider:

  • Aurora: Any (OpenAI, Anthropic, Google, Ollama)
  • Others: Fixed/locked

Pricing:

  • Aurora: Free (self-hosted)
  • Rootly: ~$2,000/mo
  • FireHydrant: ~$1,500/mo
  • incident.io: Custom
  • Shoreline: N/A (acquired)

Integrations:

  • Aurora: 22+ tools
  • Rootly: 50+ tools
  • FireHydrant: 40+ tools
  • incident.io: 30+ tools

When to Choose Aurora

Aurora is the best fit when:

  • You want AI that investigates, not just summarizes. Aurora's agents autonomously query infrastructure, run commands, and correlate data across systems.
  • You run multi-cloud. Native support for AWS, Azure, GCP, OVH, Scaleway, and Kubernetes — not just API integrations.
  • You need open source. When an AI agent runs kubectl on your production cluster, you should be able to read every line of code.
  • You want LLM flexibility. Choose any provider, or run local models via Ollama for air-gapped environments.
  • Cost matters. No per-seat or per-incident pricing. Self-hosted is free.

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 channels, status pages, and stakeholder communication.
  • You need a larger ecosystem. 50+ integrations out of the box.
  • You prefer managed SaaS. No infrastructure to maintain.
  • You have established workflows. Your team has mature 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 pricing, self-hosted is free
  • Customization — modify investigation workflows, add custom tools

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
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Aurora can receive webhooks from PagerDuty, Datadog, and Grafana, running AI-powered investigations in the background while your existing incident process continues.

Originally published at https://www.arvoai.ca/blog/aurora-vs-traditional-incident-management-tools by https://www.arvoai.ca/

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