Optimizing embedding model selection is crucial for large language models (LLMs), and Topological Data Analysis (TDA) clustering can help reveal hidden weaknesses in these models. Traditional methods often rely on public leaderboards, average metrics, or scalability issues, which may not accurately represent real-world performance. Navigable TDA clustering offers flexibility to adjust hyperparameters, map data topology, identify critical clusters, and provide automated interpretability. By applying TDA clustering techniques, such as the Mapper algorithm, teams can create visual representations that reveal underlying structures, clusters, and outliers in high-dimensional embeddings. This approach helps overcome train-test mismatch, avoids overfitting on public benchmarks, and provides granular performance insights to deploy models with precision. Pairing TDA with vector databases like Zilliz Cloud or Milvus simplifies storing and querying embeddings, improving search efficiency, interactivity, and optimized resource allocation. By adopting TDA early, monitoring post-deployment, and following best practices for embedding model development, teams can unlock the full potential of their LLMs and enhance user experiences.