Parlon builds observability for infrastructure layer controls such as dry runs, approval, rollback, audit, and production context underneath them.

Parlon is the infrastructure layer every AI workload runs on. GPU fleets. The power feeding them. The AI agents now operating inside them. The network path between all three. Most observability platforms were never built to see any of it, they were built for static servers and predictable traffic, and that world is gone. Parlon unifies telemetry from infrastructure, applications, and AI/LLM workflows into a single normalized fabric: GPU utilization, thermal throttle, and ECC/XID errors down to a single device. Breaker-level power load and feed redundancy before a rack trips. Every AI agent read, attributed, and audit-logged. East-west network health across the AI cluster, hop by hop. One data model, correlated and governed in one platform. Three capabilities make that possible. Normalization as a Primitive: every metric normalized at ingestion, not after the fact, so correlation, alerting, and AI-ready analytics are dramatically more reliable. LLM-Aware Synthetic Testing: Parlon actively tests AI workflows, monitoring inference latency, response quality, and behavioral drift, while observing the infrastructure underneath them, no other platform connects those two layers in one view. Alert Auto-Tune™: learning-based alerting that cuts noise by roughly 70%, so when Parlon alerts, it earns attention. 47.7% of enterprises are already running AI workloads in production. Only 35% believe their tools are ready to observe them. Parlon is built for the other 65%. Founded by operators with decades inside enterprise observability. Built from first principles for what infrastructure looks like today, and where AI is taking it next. Parlon. Observability for Intelligent Infrastructure. Clean. Fast. Deeply Integrable.

Founded 2026