Shopify built a continual learning loop using PyTorch and vLLM that compresses production failures into fine-tuned model weights daily, reducing estimated annual serving costs from $27 million on a frontier model to approximately $1 million1. The company's GraphQL agent handles up to 2,000 requests per minute in production. A gisting technique compressed the agent's system prompt from roughly 6,000 tokens to about 1,500 learned gist tokens, cutting end-to-end latency by approximately 38% and time-to-first-token by about 19% in load testing at 350 requests per minute.
Shopify Cuts AI Serving Costs 96% With Continual Learning Loop
Shopify's continual learning loop using PyTorch and vLLM reduces estimated AI serving costs from $27 million to $1 million annually while cutting latency…