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LLM EconomicsBest AI Cost Optimization Tools in 2026: A Buyer's Framework
The best AI cost optimization tool depends on four things: how deeply it attributes spend, whether it can enforce limits, how much of your stack it covers, and whether it connects cost to pricing.
LLM EconomicsAI Cost Optimization in 2026: A Practical Guide for Founders
Cut your AI bill in 2026 by working five levers in order, model routing, prompt size, caching, output limits, and inference efficiency, then re-check that your pricing still covers the new cost basis.
LLM EconomicsSelf-Hosting vs API: The Real Cost Math Behind '1/6 the Price'
Self-hosting an open LLM can cost a fraction of a frontier API, but only when your GPUs stay busy. The honest comparison is GPU dollars per hour divided by your actual throughput, versus the API price per token.
LLM EconomicsInference Economics: Why a $13B Valuation Is a Bet on the Token Spread
When an inference provider raises at a $13B valuation, investors are buying the spread between what a token costs to serve and what you are charged. That spread is why your API price is not a cost floor.
Pricing StrategyFlat vs Usage-Based AI Pricing: Stop Billing Your Users for Tokens
Per-token billing feels fair, but it hands your customer a cost-modeling problem even you find hard. In most cases, model the token cost yourself and charge a flat price.
Founder GuidesAI Spend Controls vs Cost Forecasting: How to Set a Cap That Actually Fits
A spend cap limits the damage of a bad month, but it can't tell you what your AI budget should be. Forecast your token cost per user first, then set the cap above your power users.
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