Welcome to the GPU Clouds Engineering Blog

Most "AI for business" content on the internet falls into two buckets:

1. Vendor pitches — generic, high-level, written by marketing.
2. Academic research — rigorous but disconnected from the operational reality of running AI in production.

This blog is neither. It's where we'll write about the messy middle: what actually happens when you put AI systems in front of real users, real data, and real budgets.

What you'll find here

We're going to write about things like:

• Honest cost breakdowns of running inference at different scales
• Why your computer vision PoC works on the demo dataset and dies on real photos
• The OCR tools we tested on real invoices, and which ones broke
• How to wire LLM agents into existing enterprise software without rewriting everything
• What "production-grade" actually means when you're not Google

If a post doesn't include numbers, screenshots, or code, we probably didn't write it.

Who we are

GPU Clouds is an AI engineering team based in Pennsylvania, USA. We build production AI systems and run the AI Marketplace — a B2B platform where companies discover, compare, and deploy AI agents tailored to real business problems.

We've built things like:

• Computer vision pipelines for quality inspection
• LLM-powered agents that handle customer onboarding workflows
• Robotics perception stacks that ship in actual products
• Full-stack platforms combining vector search, retrieval, and modern web frontends

Most of our customers are small and mid-sized US companies who need AI to actually work, not be a press release.

Why we're writing in public

Three reasons:

1. Accountability. Writing forces us to defend our choices. If we can't explain why we picked one approach over another in plain English, we probably didn't think hard enough.
2. Recruiting. The kind of engineers we want to work with read technical blogs. Hello if you're one of them.
3. Trust. The best way for a prospective customer to know if we can solve their problem is to see how we've solved similar ones. A case study is better than a sales deck. A blog post showing how we debugged a real production issue is better than both.

What's next

The first batch of posts will cover:

• A teardown of 4 popular OCR APIs on real-world invoice data
• The honest cost of running a Llama 70B inference endpoint in 2026
• Why most "AI agents" demos fall apart on the second turn

If any of that sounds useful, subscribe below. One post a week, sent on Thursdays. No spam, no growth-hack tactics, just the work.

— The GPU Clouds team