Guides on AI cost management, LLM observability, Claude API optimization, and building sustainable AI infrastructure.
AI adoption outpaced policy at most enterprises. Engineering ships features; finance sees invoices weeks later. AI cost governance closes that gap with rules that scale across providers, teams, a…
Read article →Every platform shift creates a new infrastructure layer. Mobile had analytics and push networks. Cloud had observability and FinOps. AI has gateways, evals, agents — and now, AI infrastructure star…
Read article →Saving cost for enterprises running LLM workloads is not about saying no to AI — it is about making every token accountable. This playbook covers the five highest-leverage moves we see across pro…
Read article →As LLM adoption moves from experiments to production, enterprises face a familiar problem with a new shape: AI spend is growing faster than visibility . An AI FinOps platform is the control la…
Read article →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…
Read article →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.…
Read article →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…
Read article →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…
Read article →LLM cost observability is the practice of measuring, attributing, and alerting on every dollar your AI infrastructure spends — before the monthly bill arrives.
Read article →Join the Tokenistt waitlist for early access to AI cost management and LLM spend observability.
