Inference infrastructure presents significant, often underestimated complexity as AI applications scale, moving beyond simple commodity status. While early-stage startups may initially attempt to build internal infrastructure, offloading this to specialized providers like Baseten allows teams to focus on core product differentiation and agility. As the gap between open-source and closed-source models narrows, enterprises increasingly require granular control and robust workflows for model deployment. Founders scaling from seed to Series B must prioritize clear product-market fit, specifically identifying their target buyer, the technical requirements for proof-of-value, and the value-based budget. Future growth in the sector will be driven by incumbents integrating AI into complex workflows like IT service management and site reliability engineering, where code generation improvements enable new levels of automation and efficiency. Corinne Riley, a partner at Greylock, provides these insights alongside Baseten CEO Tuhin Srivastava.
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