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Intelligence for engineers building and operating AI infrastructure at scale. LLMOps, FinOps, Kubernetes, and the tools that keep production AI running.
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Benchmarks run on real infrastructure. Config files you can copy-paste. No vendor fluff.
Cost Optimization Playbooks
Datadog to Grafana migrations. GPU budget triage. Reserved instance strategy. Real savings.
Production Incident Frameworks
Postmortem templates for AI failures. Runbooks your on-call team will actually use.
Latest Articles
View all →How to Monitor Ollama in Production: The Observability Stack
Stop flying blind on self-hosted LLMs. This guide covers the metrics to track (GPU utilization, VRAM, TTFT, model cache hit rate), the Prometheus setup, and the Grafana dashboard that catches Ollama failures before they become incidents.
SGLang Production Monitoring: Complete Guide for AI Engineers
Monitor SGLang in production: RadixAttention architecture, KV cache metrics, prefill/decode throughput, TTFT, Prometheus + Grafana instrumentation, and a frank comparison with vLLM and Ollama.
LLM Hallucinations: Five Production Detection Methods
A practical guide to monitoring LLM hallucinations in production. Covers deterministic checks, LLM-as-a-judge evaluation, embedding-based drift detection, and the full hallucination monitoring pipeline with alerting thresholds.
Open Source LLM Monitoring Stack in 2026 - A Practical Guide
Build a production-ready LLM observability stack with OpenTelemetry, Prometheus, Grafana, and Loki — no vendor lock-in, no per-token fees.
LLM Monitoring Dashboard Templates: Grafana + Prometheus
Production-ready Grafana dashboard JSON and Prometheus queries for LLM monitoring. Token throughput, TTFT/TPOT latency, cost attribution, error rates, and context window utilization — all in one template.
Build Your First LLM Monitoring Stack: OTel + Prometheus
A practical guide to instrumenting LLM applications with OpenTelemetry, scraping metrics with Prometheus, and visualizing token costs, latency, and quality signals in Grafana dashboards.
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