← Wednesday, October 21, 2026Models & trainingCase study
Shopify’s post-trained GraphQL model is 7× faster and about 90% cheaper
For a context-heavy GraphQL subagent task, Shopify says its post-trained Nemotron model scores slightly above a frontier model, responds 7× faster, and costs about 90% less to serve. The results illustrate how specialized open models can cut latency and inference cost in an agent workflow.
- What
- Shopify routes token-heavy GraphQL work from Sidekick’s frontier model to a post-trained open-model subagent.
- How
- The GraphQL subagent reads the API schema and docs, writes and runs a query, and returns results; Shopify reports 7× speed and about 90% lower serving cost.
- Why it matters
- Post-training focused open models and serving their weights in-house lets engineers tune cost and latency for bounded subagent tasks.
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