BST‑Frequently Bought Together vs OnVoard Product Recommenderfor Shopify (2026)

BST‑Frequently Bought Together and OnVoard Product Recommender both aim to increase revenue for Shopify merchants through upsells and cross-sells, but they approach the problem with distinct features and targeting. BST‑Frequently Bought Together focuses on AI-powered recommendations based on consumer behavior, mobile-first design, and extensive customization options, drawing inspiration from Amazon's product bundling strategy. It positions itself as a comprehensive solution for merchants seeking to implement visually appealing and highly adaptable 'frequently bought together' sections with discounting features. OnVoard Product Recommender, on the other hand, emphasizes social proof and data privacy, highlighting the use of meta labels like "25% of customers bought this together" to persuade customers and hosting data in the EU to comply with GDPR. The key difference lies in their core value proposition. BST leverages AI and visual appeal for widespread theme compatibility while OnVoard centers around leveraging social cues to push products. BST seems ideal for merchants prioritising customisation and powerful recommendation engines, while OnVoard may be better for merchants focusing on compliance and social proof. Furthermore, the significant difference in review count suggests BST has more established market presence and has been battle tested more. OnVoard's key strength is appealing to EU customers and their sensitivity to data privacy.

BST‑Frequently Bought Together and OnVoard Product Recommender both aim to increase revenue for Shopify merchants through upsells and cross-sells, but they approach the problem with distinct features and targeting. BST‑Frequently Bought Together focuses on AI-powered recommendations based on consumer behavior, mobile-first design, and extensive customization options, drawing inspiration from Amazon's product bundling strategy. It positions itself as a comprehensive solution for merchants seeking to implement visually appealing and highly adaptable 'frequently bought together' sections with discounting features. OnVoard Product Recommender, on the other hand, emphasizes social proof and data privacy, highlighting the use of meta labels like "25% of customers bought this together" to persuade customers and hosting data in the EU to comply with GDPR. The key difference lies in their core value proposition. BST leverages AI and visual appeal for widespread theme compatibility while OnVoard centers around leveraging social cues to push products. BST seems ideal for merchants prioritising customisation and powerful recommendation engines, while OnVoard may be better for merchants focusing on compliance and social proof. Furthermore, the significant difference in review count suggests BST has more established market presence and has been battle tested more. OnVoard's key strength is appealing to EU customers and their sensitivity to data privacy.