The Shift to Open Source Incident Management: Sovereignty and AI Transparency

Open Source Incident Management: Why It Matters

Siddharth Singh highlights the transition of incident management from proprietary silos to open source frameworks like Aurora. While 96% of commercial codebases already utilize open source components, SRE teams are now adopting these tools to avoid monthly vendor costs exceeding $5,000.

Why This Matters

In technical reality, incident data contains highly sensitive infrastructure details and failure modes that organizations often unwittingly outsource to third-party SaaS providers. By utilizing self-hosted open source solutions, engineering teams regain data sovereignty and eliminate the ‘black box’ nature of AI investigations, ensuring that every command executed on production infrastructure can be audited for security and compliance. This shift is driven by the need for customization and the avoidance of deep vendor dependencies. Proprietary platforms often lock runbooks and postmortem history into closed ecosystems, making the cost of migration prohibitive for growing SRE teams.

Key Insights

• 96% of commercial codebases contain open source components according to the 2024 Open Source Security and Risk Analysis Report.
• Enterprise incident management platforms typically charge between $1,500 and $5,000+ per month, creating significant overhead for scaling teams.
• Aurora by Arvo AI uses LangGraph-orchestrated LLM agents to autonomously investigate production incidents across major cloud providers like AWS, Azure, and GCP.
• The concept of ‘agentic investigation’ allows tools like Aurora to execute sandboxed kubectl or cloud CLI commands to perform root cause analysis.
• Grafana OnCall serves teams already within the Grafana ecosystem by providing open-source alert routing and on-call scheduling.

Working Examples

Deploying Aurora via Docker Compose

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

Kubernetes deployment via Helm

helm install aurora ./helm/aurora

Practical Applications

• Use case: SRE teams using Aurora to run local LLMs via Ollama for air-gapped incident analysis and data privacy. Pitfall: Using proprietary SaaS for sensitive incident data which leads to vendor lock-in and high switching costs.
• Use case: DevOps teams utilizing Grafana OnCall for integrated alert routing within existing monitoring stacks. Pitfall: Relying on opaque AI agents without the ability to audit the underlying code, risking unvetted production changes.

References:

• https://dev.to/siddharth_singh_409bd5267/open-source-incident-management-why-it-matters-cei