Inference cost is the real bottleneck
Model architectures are commoditized. The teams that win are the ones with sustainable unit economics. On-device inference makes every AI feature profitable instead of a cost center.
About
We built ML infrastructure at billion-request scale. Now we're giving it to you.
Octomil was founded by engineers who spent years building the ML platforms behind the world's largest consumer and cloud products. We operated services handling billions of predictions per day and watched inference costs grow faster than revenue. We started Octomil to fix that.
The problem
Inference costs scale linearly. Your revenue doesn't.
Every AI feature you ship adds another line to your cloud bill. Recommendation models, classification endpoints, NLP pipelines — they all cost per inference, and the bill grows linearly with usage. The more successful your product, the worse your unit economics.
Meanwhile, your users carry powerful ML-capable hardware in their pockets. Modern smartphones have neural engines, 8GB of RAM, and multi-core CPUs sitting idle. Octomil routes inference to those devices — cutting your cloud costs by up to 90%. Smart routing keeps hard queries in the cloud while common flows run on-device. Quality monitoring and automatic rollback ensure your users never notice the difference.
On-device inference also eliminates an entire category of privacy risk. No user data leaves the device. No PII hits your servers. Compliance becomes an architectural property, not a policy you have to enforce.
Team
Engineers who've done this before
Our founding team built and operated ML serving infrastructure handling billions of daily requests at very high reliability for some of the world's largest technology and advertising platforms. We've shipped model orchestration, ad personalization, and ranking systems at the scale where downtime and bad predictions cost real money.
That background shapes how we build Octomil: production-grade reliability, not research prototypes. Real observability, not dashboards bolted on after the fact. And an architecture designed for regulated environments from day one — because we've seen what happens when compliance is an afterthought.
Why us
What we learned building at scale
Quality is the blocker
Founders fear edge models degrade UX. That's why we built routing, evals, and automatic rollback. You get on-device cost savings with cloud-quality guarantees.
Privacy is a product advantage
On-device means zero data collection. HIPAA, GDPR, CCPA compliance becomes architectural, not procedural. Your users get personalization without the trade-off.
We're hiring
We're looking for engineers who've built ML infrastructure, mobile SDKs, or distributed systems at scale. If you've seen inference costs eat margins and want to solve the problem at the infrastructure layer, we'd like to talk.
Contact
Get in touch
Office
Octomil
447 Broadway 2nd Floor #1202
New York, NY 10013
Every AI feature you ship makes your cloud bill worse
Stop paying per inference.
Move ML to user devices. Cut inference costs up to 88%, personalize without collecting data, and ship AI features that scale with users — not with your cloud bill.