The blog post discusses the use of Fireworks Eval Protocol and Ollama to facilitate the selection and deployment of AI models by allowing teams to replace OpenAI models with local open-source alternatives without altering existing application logic. By maintaining a consistent evaluation harness, teams can seamlessly swap model backends using an OpenAI-compatible API provided by Ollama, enabling robust, evidence-based model comparisons. Two examples are provided: one involves evaluating an agent on the Chinook dataset using PydanticAI, and the other assesses Langfuse traces, demonstrating how local models like qwen3:8b can outperform some remote models in specific tasks. The approach supports minimal code changes, preserving evaluation and logging processes while enabling rapid and secure validation of alternative models.