Barathkanna

A single user action can trigger anywhere from a few to dozens of LLM calls (tool use, retries, reasoning steps), and with token-based pricing the cost can vary a lot.

How are builders here planning for this when pricing their SaaS?

Are you just padding margins, limiting usage, or building internal cost tracking? Also curious, would a service that offers predictable pricing for AI APIs (like a fixed subscription cost) actually be useful for people building agentic workflows?

While this isn’t hard to understand, in practice it makes costs harder to predict, especially for small teams moving from experiments to early production. This feels less like a technical problem and more like a budgeting and planning problem.

I’m curious about alternative pricing abstractions, for example a subscription with unlimited tokens but a capped number of requests, aimed at making monthly spend easier to reason about while building.

For people running AI in production today, does token based billing give you enough predictability, or would a model like this actually reduce friction? What tradeoffs would matter most to you?

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.