RunWhen provides an AI‑SRE platform that deploys autonomous agents running LLM‑optimized scripts to detect, prioritize, and remediate issues across cloud infrastructure, applications, and data. Engineers can query the system for root‑cause analysis, cost impact, and remediation steps, while the platform integrates with existing observability stacks and offers hybrid SaaS or self‑hosted deployments for secure enterprise environments.
Funding
$2.7M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Troubleshooting and resolving issues in complex cloud and Kubernetes environments often requires specialized expertise and time-consuming manual tasks. Developers and operations teams face challenges in quickly identifying root causes, leading to increased mean time to resolution (MTTR) and reduced productivity. Existing automation solutions are often fragile, difficult to maintain, and fail to adapt to dynamic environments.
Solution
RunWhen provides an AI-powered platform that automates troubleshooting and remediation tasks for cloud-native applications. The platform leverages a library of AI-native automation tools, enabling Engineering Assistants to respond to alerts, tickets, and chat prompts by building workflows in real time. These assistants analyze the environment, identify the most logical next steps, and execute tasks to provide root cause analysis and suggested remediation steps. By centralizing and scaling automation, RunWhen reduces manual toil, minimizes context switching, and empowers developers and operations teams to resolve issues faster and more efficiently.
Target Audience
RunWhen targets DevOps engineers, platform engineers, SREs, and development teams working with cloud-native applications and Kubernetes environments who need to automate troubleshooting, reduce MTTR, and improve operational efficiency.
Features
- AI-powered Engineering Assistants that automate troubleshooting and remediation workflows
- A library of pre-built, AI-native automation tasks covering cloud infrastructure, platform services, and observability tools
- Automated alert triage and ticket drafting, reducing alert fatigue and improving incident response
- Self-service troubleshooting capabilities for developers and on-call teams via Slack integration
- Real-time workflow creation and execution based on environmental context
- Integration with CI/CD pipelines for automated troubleshooting of failed jobs and tests
- Ability to create custom automation tasks using a variety of programming languages
- A VS Code plugin for self-serve troubleshooting