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Nash
Game-theoretic AI token pricing & margin simulator
Most AI startups pick a flat $20/mo tier and get crushed by whale token burn, or switch to pure usage meters and lose users to bill-shock. Nash is a 100% browser-native AI FinOps workbench that simulates 1,000 log-normal agents using Kahneman-Tversky loss aversion and 2-tier self-selection math. Test 18 real LLM routing stacks, parse private CSV logs locally, run a 240-combination Nash grid-search optimizer, and export production TypeScript router middleware.
Hey Product Hunt! I built Nash (nashps.netlify.app) because watching AI products bleed negative gross margins on flat $20/mo plans is painful and pure usage meters just trigger bill-shock churn. Instead of guessing pricing tiers on a napkin, Nash runs a live 1,000-agent Monte Carlo cohort right in your browser with zero cloud uploads. Test real Frontier-to-Lite model routing (like GPT-4.5 or Claude 3.7 Sonnet paired with GPT-4o-mini), drop in a CSV of your own user logs locally, run the grid-search Nash Optimizer, and grab production TypeScript router code and a VC unit-economics memo. Try selecting GPT-4.5 Preview and running the optimizer. Would love to hear what gross margins you unlock!
About Nash on Product Hunt
“Game-theoretic AI token pricing & margin simulator”
Nash was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #126 on the daily leaderboard. Most AI startups pick a flat $20/mo tier and get crushed by whale token burn, or switch to pure usage meters and lose users to bill-shock. Nash is a 100% browser-native AI FinOps workbench that simulates 1,000 log-normal agents using Kahneman-Tversky loss aversion and 2-tier self-selection math. Test 18 real LLM routing stacks, parse private CSV logs locally, run a 240-combination Nash grid-search optimizer, and export production TypeScript router middleware.
On the analytics side, Nash competes within SaaS, Developer Tools and Artificial Intelligence — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Nash performed against the three products that launched closest to it on the same day.
Who hunted Nash?
Nash was hunted by SHREYAS KODURI. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Nash including community comment highlights and product details, visit the product overview.
SHREYAS KODURI
Hey Product Hunt! I built Nash (nashps.netlify.app) because watching AI products bleed negative gross margins on flat $20/mo plans is painful and pure usage meters just trigger bill-shock churn. Instead of guessing pricing tiers on a napkin, Nash runs a live 1,000-agent Monte Carlo cohort right in your browser with zero cloud uploads. Test real Frontier-to-Lite model routing (like GPT-4.5 or Claude 3.7 Sonnet paired with GPT-4o-mini), drop in a CSV of your own user logs locally, run the grid-search Nash Optimizer, and grab production TypeScript router code and a VC unit-economics memo. Try selecting GPT-4.5 Preview and running the optimizer. Would love to hear what gross margins you unlock!