groundcover: LLM Observability Solution

groundcover has launched an observability solution for large language models (LLMs) and AI workflows, leveraging eBPF technology for comprehensive insights. The platform aims to provide transparency in AI application monitoring and troubleshooting. By utilising eBPF, the solution captures system activity with minimal overhead, offering a detailed view of AI interactions.

The solution delivers access to token usage, latency, throughput, and error rates across LLM interactions. It helps pinpoint token-heavy use cases, track inefficient flows, and uncover opportunities to reduce costs and improve response times. The platform captures the complete execution context behind every request, including prompts, response payloads, tool usage, and session history. This allows users to trace quality issues back to their source, debug inconsistencies, and maintain output quality in production environments.

groundcover's eBPF-based platform offers visibility into API requests and responses, enabling monitoring of LLM calls and identification of anomalies and sensitive data sent to third-party LLM providers. It delivers LLM and agent observability without instrumentation, request limits, or sensitive data leaving the cloud.