Tag: llmops

A July 2026 field guide to choosing self-hosted open models by exact checkpoint, license, resident memory, usable context, workload quality, and production economics.

Why a $1.05 Cloud Run L4 hour can be cheaper or more expensive than hosted inference depending on utilization, throughput, fallback rate, and accepted business outcomes.

Why adding a private open model beside hosted APIs fails unless routing, fallback, evaluation, and authority are designed as one production contract.

Why LLM fine-tuning projects fail when teams jump to NeMo or Hugging Face training commands before deciding the model, data, evaluation, serving, and governance loop.

A practical AI strategy framework with a worked example that connects business levers, data readiness, pilots, evaluation, governance, deployment, and operating metrics.