Build and buy: why the smartest AI teams do both

This post introduces the 'build and buy' strategy for AI model deployment, advocating for a 90/10 split between open-weight models (for 90% of workloads) and frontier models (for the remaining 10%). It details the economic rationale, contrasting the unpredictable costs of API-based frontier models with the predictable costs of reserved compute for self-hosted open-weight models. The post emphasizes the advantages of owning the model, such as cost control, roadmap independence, and data privacy through fine-tuning. It also provides a framework for deciding model allocation based on task complexity and integration maturity.