AI Agent Observability: What to Trace and What It Catches
AI agent observability means tracing tool calls, reasoning steps, and handoffs, not just tokens and latency. Here's what to instrument and why.
Sejal Pandey
Best AI Observability Tools in 2026: 8 Tools Compared
Eight LLM and AI agent observability platforms compared: what each tracks, pricing and free tiers, self-hosting options, and who each is built for.
Sejal Pandey
The GPU Metrics That Actually Matter
Most teams monitor three GPU metrics - utilization, temperature, memory. There are 50+ that matter, and the ones you skip cause your worst outages. A vendor-neutral guide across NVIDIA, AMD, and Intel Gaudi
Shekhar
Your LLM Is Slower Than You Think
60% GPU utilization and 3-second response times? GPU utilization is the wrong signal for LLM inference. Here's why TTFT, KV-cache pressure, and queue depth - not utilization - predict user-facing latency.
Shekhar
Predicting GPU Failures Before They Cost You
Predict GPU hardware failures 48–72 hours in advance. A guide to the five rate-based signals — ECC error trends, XID events, thermal ramp, row remap exhaustion, PCIe downtraining — and how to combine them into a composite health score.
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