groundcover
groundcover

In a move that could significantly impact how organizations monitor and troubleshoot AI applications, groundcover—a next-gen observability platform powered by eBPF—has announced a new solution focused on large language models (LLMs) and complex AI workflows. The platform aims to bridge a critical gap in AI operations: providing deep, real-time insight without the need for intrusive instrumentation or shared data.

As AI models grow more sophisticated—expanding beyond simple prompts to multi-turn conversations, tool integrations, and decision-making agents—traditional monitoring tools increasingly fall short. They often require additional code, middleware, or data egress, creating barriers to deployment and compliance challenges, especially in sensitive environments.

groundcover's new LLM Observability solution appears designed to address these challenges head-on. By leveraging eBPF—a low-level Linux technology capable of capturing system activity with minimal overhead—the platform offers a comprehensive view of AI interactions directly within the customer's environment. This includes tracking prompts, completions, latency, token usage, errors, and reasoning paths—all without sending any data outside the organization's cloud.

A Game-Changer for AI Reliability and Trust

For engineers and data scientists, understanding where AI systems go wrong—particularly with issues like hallucinations or unexpected responses—has been a persistent headache. groundcover's ability to trace the "reasoning path" and analyze prompt drift promises to improve model debugging and reduce erroneous outputs.

"AI applications today are too complex for traditional observability methods," said Orr Benjamin, VP of Product at groundcover. "Our approach delivers complete visibility into AI pipelines with zero instrumentation and no data leaving the environment, which is essential for trust, compliance, and operational efficiency."

It's a significant step forward in making AI deployment more reliable and transparent, especially as organizations face mounting pressure to ensure their AI systems are both responsible and performant.

Market Recognition and Strategic Advantages

groundcover's technology isn't just novel—it's gaining industry recognition. The platform was recently included in Gartner's Magic Quadrant for Observability Platforms, a testament to its innovative architecture and rapid growth. Its "Bring Your Own Cloud" (BYOC) model offers organizations flexibility and security advantages, allowing them to retain control over their data while enjoying extensive observability coverage.

With nearly 70% of organizations now leveraging LLMs and AI-powered workflows, the demand for advanced observability solutions is mounting. As AI continues to integrate deeper into business-critical systems, the ability to monitor, debug, and optimize these models in production will be crucial—and increasingly complex.

groundcover's zero-instrumentation approach could set a new standard, enabling organizations to maintain visibility, ensure compliance, and improve AI models faster and more securely than ever before.

As AI adoption accelerates, solutions like groundcover's may become essential tools in managing the operational transparency and robustness necessary for trustworthy AI. How well these tools perform in real-world, high-stakes environments remains to be seen, but their arrival marks a notable step forward in AI observability.