Log management for AI workloads: How to bring your logs and telemetry plan into the AI-first century

To support the scale and complexity of AI workloads, teams must transition from traditional, fragmented log management to a unified observability platform that integrates logs, metrics, and traces into a single, continuously queryable context layer. This "AI-ready" approach optimizes costs by eliminating expensive indexing and rehydration processes while standardizing telemetry ingestion for better data quality. Ultimately, this shift enables real-time, preventive operations, allowing organizations to move from reactive troubleshooting to managing trustworthy, autonomous AI systems.