Open source ML models offer developers a level of control and customization over proprietary model APIs, allowing them to choose the best-suited architecture for their needs. By building on top of open source models, developers can access a wide range of capabilities that would otherwise be lacking from black box endpoint providers. Using open source models directly provides full control over inputs, outputs, and environment, protecting against "model shift" where endpoint providers change underlying models without notice. This independence also enables customization optimizations, such as reducing latency or optimizing GPU usage, to meet specific use case requirements. Additionally, using dedicated hardware for inference gives developers more control over their spend, predictable costs, and reduced attack surface, while also enabling better regulatory compliance. With open source models, developers can estimate expected request volume and provision hardware accordingly, automatically scaling up and down within configured limits in response to traffic.