Pinterest, with its 620 million monthly active users, faced significant expenses calling a frontier AI model for every image recommendation. CTO Matt Madrigal addressed this by removing the vision layer from the Qwen3-VL model and reconstructing it using proprietary embeddings. This approach cut costs by 90% and improved accuracy by 30%. The team has invested heavily in tailoring open-source models in-house, focusing on leveraging unique proprietary data over sheer model size. By customizing Qwen3-VL and incorporating multimodal embeddings, Pinterest delivers personalized visual discovery experiences efficiently at scale. Additionally, a “taste graph” models users’ evolving preferences, aiding the transition from discovery to intent, such as purchasing. This innovation empowers Pinterest to offer relevant content while maintaining low latency and high engagement. For further insights, the full discussion is available on the Beyond the Pilot podcast.
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