Kerno Core

Product Introduction

1. Kerno Core is a lightweight runtime intelligence engine designed to establish a continuous feedback loop between live production systems, developers, and AI code agents. It integrates directly into Kubernetes environments to provide real-time insights without requiring code modifications or disrupting workflows. The product enables developers and AI agents to access runtime context, performance metrics, and dependency maps during coding and deployment cycles.

2. The core value of Kerno Core lies in its ability to reduce operational overhead and accelerate development cycles by bridging the gap between production environments and development tools. It empowers developers to validate code against live system behavior, optimize performance proactively, and minimize customer-facing incidents. By embedding runtime intelligence into IDEs and AI workflows, Kerno ensures code changes are context-aware and aligned with actual system conditions.

Main Features

1. Runtime Context Mapping: Kerno Core uses eBPF technology to create a graph-based representation of runtime environments, linking services, APIs, databases, and code dependencies across production and pre-production clusters. This enables developers to visualize hotspots, slow queries, and API drift directly in their IDEs. The system automatically correlates code changes with runtime impacts, reducing debugging time.

2. IDE and AI Agent Integration: Kerno provides IDE extensions that surface live performance metrics, exception traces, and dependency graphs within popular development environments. AI code agents receive continuous context updates via Kerno’s API, allowing them to generate code snippets optimized for specific runtime conditions. Alerts for issues like latency spikes or configuration drift are delivered in real time during coding sessions.

3. Secure, Zero-Overhead Deployment: Kerno Core deploys via Helm charts in under two minutes, running as a sidecar in Kubernetes clusters without affecting application latency or resource usage. Sensitive data, including PII and system metrics, remains within the user’s cloud environment using object storage. The platform employs smart sampling to minimize storage costs while retaining full observability coverage.

Problems Solved

1. Reduced Incident Resolution Time: Kerno Core addresses the challenge of delayed issue detection by providing developers with immediate access to production context during coding. This reduces the average time to diagnose and fix errors, preventing minor issues from escalating into critical outages. Teams report a 64% reduction in customer-facing incidents after adoption.

2. Streamlined Collaboration Between Developers and AI: The product eliminates friction between AI-generated code and real-world system constraints by feeding AI agents with live runtime data. Developers avoid manual validation of AI suggestions, as Kerno automatically tests code against production behavior. This reduces deployment failures and accelerates feature delivery.

3. Operational Burden on Engineering Teams: By automating context sharing across pre-production and production environments, Kerno reduces the need for manual environment replication. Developers gain self-service access to system insights, freeing Ops teams from repetitive troubleshooting tasks. Engineering hours spent on non-feature work decrease by 20% quarterly.

Unique Advantages

1. eBPF-Based Observability: Unlike traditional APM tools that require language-specific instrumentation, Kerno Core operates at the Linux kernel level via eBPF, making it compatible with all programming languages running on Kubernetes. This eliminates the need for code modifications or SDK integrations.

2. AI-Ready Runtime Feedback: Kerno uniquely supports AI code agents by streaming granular system data (e.g., API response times, database query patterns) through its MCP (Model Context Protocol). This allows AI tools to generate code that adheres to actual production constraints, increasing deployment success rates by 3x.

3. Cost-Efficient Architecture: The platform’s object-storage-native design reduces total ownership costs by 40% compared to time-series-based solutions. Kerno’s smart sampling algorithm retains critical system patterns while discarding redundant data, ensuring comprehensive coverage without bloated storage requirements.

Frequently Asked Questions (FAQ)

1. Does Kerno support my programming language? Yes. Kerno uses eBPF to monitor systems at the kernel level, making it language-agnostic. It works with Go, Java, Node.js, Python, and any other language running on Linux-based Kubernetes nodes without requiring code changes or instrumentation.

2. Where does Kerno store sensitive data? All runtime data, including metrics and traces, is stored in your own cloud account’s object storage (e.g., AWS S3, GCP Cloud Storage). Kerno never retains sensitive information externally, ensuring compliance with data residency and security policies.

3. Can Kerno replace my existing APM tools? No. Kerno complements APM systems by focusing on developer workflows rather than operational dashboards. It integrates with OpenTelemetry and Prometheus to enrich existing data while delivering context directly to IDEs and AI agents, reducing alert fatigue and tool-switching overhead.

4. How does billing work? Kerno charges based on the monthly average of active Kubernetes nodes in your environment. This model smooths out temporary scaling fluctuations and avoids per-pod or per-metric fees, ensuring predictable costs even for dynamic clusters.

5. What protocols are supported for traffic analysis? Kerno natively supports HTTP/HTTPS, gRPC, PostgreSQL, and Kafka protocols in Kubernetes environments. New protocols are added automatically through kernel-level packet analysis without requiring user configuration updates.