Shopify Global Catalogue: multimodal LLMs standardize product data across billions of listings at 40 million daily inferences
Shopify's billions of product listings are created by millions of merchants in unstructured, heterogeneous formats—free-form text, custom attribute schemas, missing fields, multilingual content, and information spread across text and images—making machine understanding, semantic search, and consistent product discovery impossible at scale.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Streaming product data ingestion
Over 10 million product updates arrive daily from merchant uploads, APIs, apps, and integrations in a streaming fashion.
The Global Catalogue runs 40 million LLM inferences daily (16 billion tokens per day), reduced median inference latency from 2 seconds to 500 milliseconds, and cut GPU usage by 40%, enabling improved search, recommendations, and conversational commerce across Shopify.
What failed first
Commercial LLM APIs (OpenAI and Gemini) proved prohibitively expensive at Shopify's inference volume, and training a fine-tuned model to predict all fields simultaneously caused a loss of generalizability at inference time.