Why did we build Oxlo.ai?

Over the last year, we’ve watched AI move from simple chatbots to agents that can reason, call tools, execute workflows, and serve real users.

As founders, we noticed something interesting.

Most teams spent weeks comparing models, optimizing prompts, and building product features. Then they deployed to production and discovered a completely different challenge: cost predictability.

A successful AI application often becomes a victim of its own success. More users means more requests. More requests means more model usage. Before long, teams find themselves spending more time watching token consumption than building their product.

The problem becomes even more apparent with AI agents. Agents don’t just generate a response and stop. They think, call tools, retrieve information, execute actions, and often make multiple model calls to complete a single task.

We felt that AI infrastructure should become more predictable as products scale, not less.

That belief led us to build Oxlo.ai.

Instead of asking developers to constantly think about token pricing, provider changes, and usage spikes, we wanted to offer access to frontier AI models through a single API with predictable monthly pricing.

The goal isn’t to replace great models. The goal is to make them easier to adopt in production.

I’m curious how others here are thinking about this.

If you’re building AI products or agents today, what worries you more:

1. Model quality?

2. Privacy and data handling?

3. Infrastructure complexity?

4. Cost predictability?

Would love to hear how other builders are approaching it.