Kerno

Kerno is a next-generation runtime intelligence platform designed to empower AI-native development teams with real-time, context-rich insights directly within their development environments. Kerno positions itself as the world’s first Agentic Native Observability platform, combining advanced technologies like eBPF and application graphRAG to deliver precision observability without the need for code changes or intrusive instrumentation. Kerno addresses a fundamental challenge in modern software development: the disconnect between code creation and runtime behavior. Traditional observability tools often rely on time-series data and external dashboards, which can be noisy, fragmented, and difficult for developers to interpret. Kerno flips this model by embedding runtime context directly into the developer’s IDE, allowing both human developers and AI code agents to understand how their code behaves in real-world environments, whether in pre-production or live deployments. Kerno’s architecture is built for Kubernetes (K8s) environments and supports a wide range of protocols, including HTTP/HTTPS, gRPC, PostgreSQL, and Kafka. It uses eBPF to observe system behavior at the kernel level, making it language-agnostic and capable of monitoring services written in Go, Java, Node.js, Python, and more. This zero-code instrumentation ensures that developers can deploy Kerno in minutes without modifying their applications or compromising performance. The platform includes several powerful components. Kerno IDE integrates directly with popular development environments, providing live performance metrics, dependency maps, and alerts for issues like slow queries, exceptions, and API drift. Developers can validate changes against production behavior, optimize code with real-world data, and collaborate more effectively with their teams. Kerno Studio offers a visual interface for exploring system behavior, configuring alerts, and managing observability workflows. It supports service maps, custom dashboards, and integrations with tools like Jira, Linear, and Slack to streamline issue tracking and communication. Another standout feature is Kerno MCP (Model Context Provider), which feeds continuous runtime context to AI code agents. This ensures that AI-generated code suggestions are grounded in the actual production environment, improving accuracy and reducing the risk of introducing bugs. By aligning AI outputs with real-world system behavior, Kerno enhances the reliability and efficiency of AI-assisted development. Kerno’s impact on engineering teams is significant. Companies using the platform report a 64% reduction in customer-facing production incidents, a 20% increase in engineering hours redirected to feature development, and a threefold improvement in successful first-time deployments. These metrics highlight Kerno’s ability to reduce operational overhead, accelerate development cycles, and improve code quality. From a security and cost perspective, Kerno is designed to be lightweight and scalable. It stores sensitive data securely within the user’s cloud environment, avoiding vendor lock-in and ensuring compliance with data privacy standards. Its object storage-optimized architecture and smart sampling algorithms minimize resource usage and total cost of ownership. Kerno is redefining observability for the AI-native era. By embedding runtime intelligence directly into the development workflow, it enables faster, smarter, and safer software delivery. Whether you're a startup building AI-driven applications or an enterprise managing complex cloud-native systems, Kerno offers a transformative approach to understanding and improving your code in real time.

Kerno stands out in the observability and runtime intelligence space by offering a developer-first, AI-native platform that redefines how engineering teams monitor, debug, and optimize cloud-native applications. Its competitive edge lies in its unique combination of technologies, seamless developer experience, and deep integration with AI code agents features that most traditional observability platforms lack. Unlike legacy tools that rely heavily on time-series data and external dashboards, Kerno embeds runtime context directly into the developer’s IDE. This tight feedback loop allows developers to see live performance metrics, system dependencies, and anomalies without leaving their coding environment. This real-time visibility accelerates debugging, reduces cognitive load, and empowers developers to make informed decisions faster. It also integrates with AI code agents, feeding them continuous production context so that generated code is optimized for the actual runtime environment—an innovation that sets Kerno apart in the era of AI-assisted development. Kerno’s use of eBPF technology is another major differentiator. It enables zero-code instrumentation, meaning developers don’t need to modify their applications or add SDKs to start collecting runtime data. This makes deployment fast, secure, and scalable across Kubernetes environments. The platform supports multiple protocols and languages, including HTTP, gRPC, PostgreSQL, Kafka, Go, Java, Node.js, and Python, ensuring broad compatibility across modern tech stacks. Security and cost-efficiency are also core strengths. Kerno stores all sensitive data within the user’s cloud infrastructure, ensuring compliance with privacy regulations and eliminating vendor lock-in. Its lightweight architecture and smart sampling reduce resource consumption, lowering operational costs by up to 70% compared to traditional observability platforms. Kerno’s graph-based runtime mapping, powered by its proprietary application graphRAG, offers a holistic view of how systems, code, and teams interact. This contextual intelligence helps developers understand the impact of every code change, validate deployments, and catch issues like API drift or performance bottlenecks before they escalate. Kerno’s competitive edge lies in its developer-centric design, AI-native capabilities, zero-code deployment, and secure, scalable architecture. It transforms observability from a reactive, ops-heavy process into a proactive, developer-driven workflow, making it a compelling choice for modern engineering teams building and maintaining complex cloud-native applications.

• Small Businesses (1-99)
• Mid Market (100-500)
• Enterprise (500+)