Viewing profile — Barathkanna

Sounds like a plan, But what if you can just pay a fixed cost every month and not worry about anything?

That’s true, but AI is interesting because consumption-based pricing introduces a lot more variance than typical SaaS infrastructure. One user action can trigger dozens of model ca…

Local models help remove token cost uncertainty, but they shift the problem to infrastructure and ops. GPUs, scaling, maintenance, and latency can add up quickly depending on the w…

That’s great. Real-time tracking is a big step already. The tricky part we kept running into was the variance itself, especially with retries and agent loops. That’s partly why we …

One underlooked source of variance is retries from formatting failures. In many agent systems the loops dominate the cost, not the raw token length. We ran into the same issue buil…

Agreed. The real cost unit becomes the whole agent workflow, not a single LLM call. One user action can trigger dozens of calls. We ran into the same issue and ended up building ht…

Exactly. That’s actually why we started building Oxlo.ai. Early stage builders usually just want to experiment without worrying too much about token cost spikes.

True, but for early stage builders it’s harder to design those guardrails upfront. A lot of the time you only discover the retry patterns and cost spikes once real users start hitt…

Local models solve the marginal cost problem, but they move the complexity into infrastructure and throughput planning instead.

I’ve been experimenting with agent-based features and one thing that surprised me is how hard it is to estimate API costs. A single user action can trigger anywhere from a few to d…

Hi HN, I’m one of the founders of Oxlo.ai. We’re building a developer first AI API platform focused on simplifying how small teams integrate AI into production. Most AI APIs charge…

Agreed. Self-hosting gives the cleanest fixed cost, but you pay for it in ops and capacity planning. I’m mainly curious whether there’s a middle ground that gives early teams more …

The default norm today for using AI models via APIs is token based pricing, where you pay based on how much you use. While this isn’t hard to understand, in practice it makes costs…

I asked GPT for a rough estimate to benchmark prompt prefill on an 8,192 token input. • 16× H100: 8,192 / (20k to 80k tokens/sec) ≈ 0.10 to 0.41s • 2× Mac Studio (M3 Max): 8,192 / …

That won’t realistically work for this model. Even with only ~32B active params, a 1T-scale MoE still needs the full expert set available for fast routing, which means hundreds of …

A realistic setup for this would be a 16× H100 80GB with NVLink. That comfortably handles the active 32B experts plus KV cache without extreme quantization. Cost-wise we are lookin…

TLDR: AI didn’t diagnose anything, it turned years of messy health data into clear trends. That helped the author ask better questions and have a more useful conversation with thei…

TLDR: IPv4 is fully exhausted and no longer growing. Internet growth now depends on IPv6 adoption and address sharing, but IPv6 rollout is still uneven across regions.

I get why this exists and appreciate the transparency, but it still feels like a slippery middle ground. Age prediction avoids hard ID checks, which is good for privacy, yet it als…

What’s interesting here isn’t the humanoid form factor, it’s the systems integration. Plugging robots into Siemens’ industrial stack means they’re being treated like first-class no…

TLDR: Soft deletes look easy, but they spread complexity everywhere. Actually deleting data and archiving it separately often keeps databases simpler, faster, and easier to maintai…

Thank you!! We are definitely fully focused on Developer experience. Would love some feedback if it looks interesting

Totally fair question, and you’re not being negative. We’re not claiming better token economics in the sense of magically cheaper tokens, and we’re not just burning money to subsid…