Story

Hi HN,

I’ve noticed a recurring theme in many threads here: AI is powerful, but once you move past demos, token based pricing becomes expensive and hard to reason about.

We ran into this problem ourselves while building AI powered systems. Predicting costs, budgeting usage, and experimenting safely all got harder as workloads grew. So we built a small AI API platform for inference, aimed at early developers and small teams who want to integrate AI without constantly calculating token usage. The focus is on lower and more predictable costs rather than chasing the newest model.

This is still early, and I’m mainly posting to learn from others here. For people running AI in production, what’s been the hardest part to manage so far? Cost, predictability, performance, or something else?

I’d really appreciate any insights or experiences.