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A Five-Row Scorecard to Evaluate OSS AI Infrastructure Startups

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A framework for assessing open-source AI infrastructure startups proposes evaluating companies across five dimensions: attention, adoption, retained usage, paid boundary, and unit economics. Rather than relying on a single vanity metric like GitHub stars or package downloads, the scorecard layers signals to distinguish curiosity from genuine dependency. The paid boundary row examines what a company offers free to drive adoption versus what it charges for, such as hosted cloud, security, or managed deployment. The economics row scrutinizes gross margin, compute costs, and whether value capture keeps pace with usage growth. Startups like HelixDB, Caplets, and InsForge are cited as examples where the scorecard reveals fundability questions that headline traction numbers alone cannot answer.

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