The hidden tax on every AI product: working around limits

When we launched oneinfer.ai five months ago, we set out to solve one problem, making AI infrastructure usable at scale through a unified inference layer, smart routing, and cost optimization.

What we didn't expect was how often a different problem would come up in every conversation with teams in production.

It wasn't the models. It wasn't even the infrastructure cost. It was the constant engineering tax of working around access, retry logic for rate limits, juggling multiple provider accounts, splitting prompts to fit token windows, building fallback chains for throughput ceilings, monitoring usage caps that change without warning.

Teams told us the same thing in different words: "We're spending more time managing access than building features."

That stuck with us. So we started asking a new question: what would AI access look like if it were built for sustained, production-scale usage from the start, not retrofitted onto a per-request, per-token billing model designed for experimentation?

That question became our next project: openbandwidth.live

A rethink of the AI access layer, focused on: Predictable throughput that doesn't break under real load A pricing model that matches how teams actually consume Less time spent tuning around limits, more time building

If oneinfer.ai is about making AI infrastructure smarter, openbandwidth.live is about making it sustainable at the scale teams actually run at. We're not launching today, this is an early signal for the people who've been following along since oneinfer.ai and the wider build-in-public community. Before we go live, we'd love to hear from you.

What's the most painful access constraint you've worked around in production?

The team behind oneinfer.ai & openbandwidth.live