§Resources
Resources for AI Engineering Teams
Guides, articles, and documentation on LLM cost observability, AI cost management, and building sustainable AI infrastructure.
Guides
Blog
2026-06-09
Building Tokenistt: An AI Infrastructure Startup Story
Tokenistt started as a weekend tool with a simple question: what does a single Claude API call actually cost — and could we know before shipping it? That question became an AI infrastructure sta…
2026-06-07
Anthropic Prompt Caching: A Practical Guide
Anthropic's prompt caching lets you pay significantly less for tokens your application sends repeatedly. Used correctly, it is one of the highest-leverage cost optimizations for Claude workloads.…
2026-06-05
AI Cost Startups and the Rise of LLM FinOps
As every product team ships AI features, a new infrastructure category is emerging: LLM FinOps — the discipline of managing, attributing, and optimizing AI API spend with the same rigor finance t…
2026-06-03
How to Reduce Claude API Costs in Production
Claude API costs scale with tokens — not requests. A single verbose system prompt repeated thousands of times per day can cost more than the model inference itself. Here are proven strategies enginee…
2026-06-01
What is LLM Cost Observability?
LLM cost observability is the practice of measuring, attributing, and alerting on every dollar your AI infrastructure spends — before the monthly bill arrives.
Documentation
For AI systems: see also /llms.txt
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