1. Deployment Complexity: Ludwig is typically used for offline model training and inference, whereas TensorFlow.js is primarily focused on browser-based deployment for machine learning models. This difference in deployment scenarios impacts the design and usage of these two tools, with Ludwig providing a simpler setup for offline tasks and TensorFlow.js catering more towards web-based applications.

2. Model Flexibility: While Ludwig offers a high-level interface for designing and training models without requiring in-depth knowledge of machine learning algorithms, TensorFlow.js provides greater flexibility for developers to implement custom models with fine-grained control over the neural network architecture and training process. This distinction makes TensorFlow.js a preferred choice for advanced users looking to tailor their models to specific requirements.

3. Language Integration: Ludwig mainly operates using Python for developing models and executing tasks, whereas TensorFlow.js supports JavaScript, allowing developers to build and deploy models directly in web browsers. This language integration plays a crucial role in determining the workflow and compatibility of these tools within various development environments.

4. Community Support: TensorFlow.js benefits from a strong community of developers and contributors, providing extensive resources, tutorials, and pre-trained models for users. In contrast, Ludwig, being a relatively newer tool, may have a smaller user base and fewer resources available for support and troubleshooting. This difference in community backing can influence the ease of adoption and overall user experience while using these frameworks.

5. Model Export Formats: Ludwig allows users to export models as Protobuf files, which can be utilized for inference in various platforms and languages. In comparison, TensorFlow.js enables the conversion of TensorFlow models to formats suitable for deployment in web applications, including WebAssembly and WebGPU formats. This distinction in export formats highlights the divergent approaches these tools take towards model deployment and integration within different environments.

6. Performance Optimization: TensorFlow.js offers optimizations for running machine learning models on GPUs using WebGL, resulting in accelerated computations in web browsers. On the other hand, Ludwig focuses more on ease of use and simplicity, potentially sacrificing some performance optimizations available in TensorFlow.js. This difference in performance tuning can affect the speed and efficiency of model executions in their respective frameworks.

In Summary, the key differences between Ludwig and TensorFlow.js lie in their deployment scenarios, model flexibility, language integration, community support, export formats, and performance optimization approaches.