What if AI had no token limits?

Five months ago, we launched oneinfer.ai on Product Hunt with a clear goal, make AI infrastructure usable at scale through a unified inference layer, intelligent routing, and cost optimization.

Since then, we've spent time working alongside teams shipping real AI products in production. One pattern kept surfacing in almost every conversation:

Even with better infrastructure, teams still spend a disproportionate amount of engineering time working around access constraints, token windows, rate limits, throughput ceilings, and unpredictable usage caps. Not because the underlying models can't handle the load, but because the access layer was never designed for the way modern AI products actually scale.

So we started exploring a different question: What would AI access look like if it were designed for sustained, high-throughput usage from day one?

That exploration became our next project, which we're getting ready to share soon: openbandwidth.live

A rethink of the AI access layer, focused on:

Predictable throughput for production workloads

Reduced overhead from constant limit-tuning

A pricing and usage model built around real-world consumption patterns

If oneinfer.ai was about making AI infrastructure smarter, openbandwidth.live is about making it more accessible at the scale teams actually operate.

We're not launching today, this is an early heads-up for the people who've supported us through oneinfer.ai and the broader build-in-public community. We'd genuinely love your thoughts, questions, and honest feedback before we go live.

What would you want to see from a project like this?

The team behind oneinfer.ai & openbandwidth.live