10,000 GPUs, One TSDB: Cardinality at GPU Scale
1,000 nodes × 8 GPUs × 60 metrics = 1.4M time series - before you add pod names or Slurm job IDs. GPU monitoring is a cardinality problem disguised as a metrics problem. How to design for it before production OOMs your Prometheus.
Shekhar
From GPU Silicon to Business Metrics: The 8 Layers of GPU Observability
GPU observability isn't one thing - it's eight connected layers from silicon to cost. See why correlation across layers is what cuts debugging from 2 hours to 2 minutes, and why most teams instrument only one or two
Shekhar
Every Token Has a Price: Per-Request GPU Cost Attribution
Flat per-token pricing is wrong by 10–50× per request. Prefill vs decode, batch sharing, and cache effects break the math. How to attribute real GPU cost - compute, energy, and dollars - to each inference request.
Shekhar
Last9 integration with TrueFoundry AI Gateway
TrueFoundry AI Gateway now integrates with Last9. Get unified observability for LLM traffic alongside your existing traces, metrics, and logs.
Sahil Khan
9 Monitoring Tools That Deliver AI-Native Anomaly Detection
A technical guide comparing nine observability platforms built to detect anomalies and support modern AI-driven workflows.
Anjali Udasi
What Are AI Guardrails
Learn the core concepts of AI guardrails and how they create safer, more reliable, and well-structured AI systems in production.
Anjali Udasi