groundcover, the eBPF-powered observability platform for cloud-native environments, announced the launch of its LLM Observability solution. The platform provides real-time, code-free visibility into AI applications that use large language models (LLMs), including multi-turn agents, retrieval-augmented generation (RAG) pipelines, and tool-augmented workflows all without sending data outside the customer’s environment.
With no SDKs, middleware, or instrumentation required, groundcover’s eBPF-based approach captures every interaction with providers such as OpenAI and Anthropic. This includes prompts, completions, latency, token usage, errors, and reasoning paths, enabling teams to debug failures, track performance, and optimize cost directly in production.
“The rise of LLMs and GenAI have outpaced even the most aggressive predictions,” said Oren Zeev, Partner at Zeev Ventures. “I can’t think of an observability solution better positioned to help companies ensure optimal performance, increase trust, and reduce hallucinations within LLMs than groundcover.”
Built for the Next Generation of AI Applications
AI workloads are evolving beyond single-turn prompts to multi-step agents and tool integrations that are harder to monitor and debug. groundcover is designed for this complexity, providing:
- End-to-End Visibility: Monitor every LLM request and response, tool call, and session flow without modifying application code.
- Reasoning Path and Prompt Drift Analysis: Identify why outputs fail, where context shifts across turns, and how agents make tool decisions.
- Full Data Residency: All captured data stays inside the customer’s cloud no third-party storage or outbound traffic meeting privacy and compliance requirements.
- Cost and Performance Insights: Analyze token-heavy workloads, latency bottlenecks, and error patterns to optimize performance and spend.
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Recognized for Innovation in Observability
groundcover was recently named in the Gartner Magic Quadrant for Observability Platforms, a reflection of its rapid growth and unique Bring Your Own Cloud (BYOC) architecture. This model maximizes security and privacy by keeping data in customers’ environments, while also delivering unlimited data coverage and a simplified pricing model.
“LLM-driven applications fail in ways that don’t fit traditional observability models,” said Orr Benjamin, VP of Product at groundcover. “By using eBPF, we deliver complete insight into AI pipelines with zero instrumentation and zero data egress. Teams can understand exactly how their AI apps behave in production without changing their code or exposing sensitive information.”
Solving a Real Operational Gap
Nearly 70% of organizations now use LLM-powered applications, but most teams lack the ability to trace AI performance or debug issues in production. With groundcover, engineers can:
- Debug hallucinations and inconsistent responses by tracing the reasoning path and session context.
- Analyze tool and agent workflows to find misfires or unnecessary complexity.
- Maintain compliance when handling regulated or sensitive data.
Source: PRNewswire