5 Ways to Match AliExpress Product Variants — LLM, Embedding, Vision, Rules, and Why I Chose None of Them Alone

TL;DR — I compared 5 technical approaches for matching product variants across AliExpress suppliers: string rules, vector embeddings, LLM prompting, vision models (CNN/CLIP), and hybrid algorithmic. Each has clear trade-offs in accuracy, speed, cost, and tolerance for real-world naming chaos. I ended up building a hybrid algorithm — no model, no GPU, no API call — specifically designed to run inside MCP tool calls where latency and determinism matter. This article breaks down each approach with real AliExpress examples so you can choose what fits your stack. The Problem: Supplier Replacement Is a Matching Problem When a dropshipping supplier goes down — dead link, out of stock, price spike — you need to find a replacement and remap every SKU variant to the new supplier. That sounds simple until you see what AliExpress variant data actually looks like: Supplier A (current) Supplier B (replacement) ─────────────────────── ───────────────────────── Color: Navy Blue 颜色: Dark Blue Size: XL