Maintaining backward compatibility is a critical challenge for AI APIs in production. As AI models evolve rapidly, ensuring existing clients aren’t broken by updates requires robust versioning strategies. This article dives deep into practical approaches like URL, header, and query parameter versioning, along with advanced techniques such as data transformation layers and side-by-side deployments. We’ll explore best practices to manage model drift, facilitate seamless transitions, and keep your AI services stable and reliable for all consumers.