Barath Kanna's profile on Product Hunt

About

Builder focused on AI infrastructure and developer platforms. Currently working on Oxlo.ai, a predictable, developer-first AI inference platform built to remove token-based complexity and surprise costs. Previously spent years working on distributed systems and edge infrastructure, which led to building products based on real-world pain points rather than theory. Interested in AI infra, developer experience, and building products that scale from early users to production

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Maker History

Oxlo.aiScale across AI models without scaling your bill
Jun 2026

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Joined Product HuntJanuary 4th, 2026

Forums

p/oxlo-ai

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2mo ago

Oxlo.ai is Live & Trending 🔥

It s been 4 hours and Oxlo.ai is ranking #1 on Product Hunt!

A huge thank you to everyone who has supported us so far, we truly appreciate it

If you re seeing this now, don t forget to check us out and drop an upvote.
Every bit of support helps us keep the momentum going!

https://www.producthunt.com/prod...

p/oxlo-ai

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2mo ago

Oxlo.ai Launching on Product Hunt Today

Hey Product Hunt!

Barath here, founder of Oxlo.ai.

We built Oxlo.ai because we saw a growing problem as AI agents moved from demos into production.

When agents run continuously, usage becomes difficult to forecast. A successful agent does more than generate text. It reasons, calls tools, executes workflows, and serves real users. As adoption grows, infrastructure spend grows with it.

p/oxlo-ai

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2mo ago

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.