Kerno

What is Kerno?

Kerno is a runtime intelligence engine designed for AI-native teams and developers. It provides instant, context-rich feedback and insights from production environments directly to developers and their AI code agents. Unlike traditional time-series solutions, Kerno maps runtime environments, linking systems, code, and teams to offer a comprehensive understanding of how everything fits together. Its core value proposition is to reduce the burden on Ops teams, accelerate development cycles, minimize production incidents, and ensure AI-generated code is fine-tuned for real-world environments.

How to use Kerno

Kerno can be deployed quickly on any Kubernetes environment (EKS, AKS, GKE, generic K8s) using a simple Helm command, requiring zero code changes and taking approximately two minutes. Sensitive data remains within the user's cloud environment. Users can get started for free without needing a credit card by signing up. Developers primarily interact with Kerno through its IDE extension (Kerno IDE), which integrates with popular IDEs like VS Code, Cursor, Windsurf, and IntelliJ, allowing them to pull live performance metrics, understand change impact, and validate code against production. Additionally, Kerno Studio provides a visual interface for exploring system behavior, collaborating on issues, configuring alerts, and managing workspaces. Kerno also integrates with AI copilots (e.g., GitHub Copilot, Claude, OpenAI) to feed them continuous production context and with tools like Jira, Linear, and Slack for issue tracking and communication.

Kerno's core features

Kerno IDE Extension: Provides real-time runtime context, performance metrics, hotspots, and dependencies directly within popular IDEs for developers and AI code agents.

Kerno Studio: Offers a visual platform to explore and understand code behavior across runtime environments, featuring unified views, service maps, and collaborative tools.

Graph-Based Runtime Context: Maps runtime environments by linking systems, code, and teams to deliver context-rich insights, unlike time-series solutions.

AI Code Underwriting (Kerno MCP): Feeds continuous production context to AI code agents and copilots, ensuring AI-generated code is optimized for real environments.

Early Issue Detection & Resolution: Identifies and helps fix issues like exceptions, slow queries, API drift, and performance bottlenecks within the IDE.

Change Impact Analysis (@Kerno/impact): Helps developers understand the potential impact of every code change to prevent breaking production.

Production Validation (@Kerno/validate): Allows developers to validate changes against what's running in production before merging.

Zero Config Dashboards & Context-Rich Alerts: Provides immediate insights and reduces noise with granular, targeted alerts.

Secure & Low Operational Footprint: Keeps sensitive data within the user's cloud, runs with minimal impact on application latency, and uses smart sampling for cost efficiency.

Open Standard & Tool Integrations: Built on OpenTelemetry and Prometheus, and integrates with CI/CD, observability, IDE tooling, Jira, Linear, and Slack.

Use cases of Kerno

Reducing customer-facing production incidents by providing developers with timely insights.

Freeing up engineering hours by enabling developers to catch and fix issues faster.

Increasing the success rate of first-time code deployments.

Optimizing, refactoring, and shipping new code with real-world performance context.

Ensuring AI-generated code is fine-tuned and production-ready by feeding it live environment data.

Visually exploring and debugging complex distributed systems.

Collaborating on issues and coordinating efforts across development teams.

Receiving context-rich alerts to quickly address emerging performance bottlenecks or API drift.

Integrating runtime intelligence seamlessly into existing developer workflows and toolchains (IDEs, CI/CD, observability).

Maintaining data security and compliance by keeping sensitive system data within the user's cloud environment.