PRESS
Press information for Starfield Inc.
Use this page when you write about Starfield Inc.
Company description
Starfield Inc. is a distributed AI infrastructure company. It deploys standardized compute close to power, connectivity and demand, then connects it through one programmable network for AI inference. Fabric, its control plane, is in development and is designed to schedule, meter and record every request across many sites.
Leadership
- Paweł Czech, Founder & CEO
- Ian Arden, CTO
- Cole Crawford, President / Chairman
- Andrea Marazzi, CCO
Name spelling. Paweł is written with the Polish letter ł. "Pawel" is the plain-text form.
Contact
- Press: press@starfield.net
- General: hello@starfield.net
In the news
- Compute is moving closer to power and demand: notes from eight weeks on the road
- Why we say "distributed AI infrastructure"
- Why AI chips need so much cooling
- Why AI chips are hard to get
- Where should AI inference run? Three things decide
- Paweł Czech on Money Moves: why AI compute will go decentralized
- Stop buying the sun: right-sized AI capacity
- Paweł Czech and Cole Crawford on theCUBE at the NYSE
- What is AI compute worth? Nobody knows
- What a token is and why it sets your AI bill
- What is a modular data center?
- When a smaller AI model is enough
- How to size your AI workload before you buy
- Introducing Starfield AI infrastructure
- What it takes to run an open model yourself
- Data residency or data localisation?
- Rent, reserve or own: ways to get AI hardware
- Renewable power and AI: what a buyer can ask
- How to read an AI service's reliability promises
- How to read an AI benchmark without being misled
- What an AI infrastructure operator does all day
- Why the opening word of an AI answer is the slow one
- Open-weight or paid AI models: what is the difference?
- On-site power for data centers, explained
- Modular vs traditional data center: how to compare
- How AI services answer many people at once
- Latency and throughput in AI, explained
- Input tokens and output tokens: why prices differ
- Why grid connections hold up AI data centers
- GPUs and CPUs: what AI needs from each
- What "in development" means for Fabric
- What can be done with a data center's waste heat
- What a compliance team asks about AI
- How to choose an AI inference provider
- Air cooling or liquid cooling for AI?
- AI and data rules: where to look by region
- Adding AI capacity one unit at a time
- LLM API pricing: why we publish dated prices
- Modular data center: why standard units matter
- Why does AI use so much electricity? In plain words
- AI inference cost: why one task gets different prices
- Data center outage: what happens when one site goes down
- What we will and won't claim here
- Edge AI inference: what close to the user really means
- AI data center power: what a megawatt buys you
- AI compliance audit trail: trust is a record, not a promise
- AI training vs inference: two different jobs
- Data processing location: stored here, processed there
- Data center site selection: spare power is not usable power
- Cost per million tokens: how to read an AI price list
- AI data privacy checklist: questions before you send data
- Edge data center partners: where each one fits
- AI infrastructure operators, not developers
- Edge data center vs hyperscale: many small or a few big?
- Distributed AI: after the internet, intelligence is next
- What is AI inference? Explained without the jargon
- AI inference latency: how far your answer travels
- Host a data center? What hosting AI capacity involves
- Hidden costs of AI capacity that aren't on the quote
- Edge inference vs cloud inference: does location matter?
- Data residency for AI inference, in one page
- AI workload scheduling: a control tower for AI work
- Edge data center: can a small town host AI capacity?
- How to buy AI compute: eight questions to ask
- AI infrastructure glossary: cloud, edge and in between
- Data center grid connection: why AI capacity follows power
- AI audit trail: a receipt for AI work
