1. Integration with TensorFlow and Keras: NSFWJS is specifically tailored to work with TensorFlow and Keras, making it easy to integrate into existing machine learning workflows that use these frameworks. On the other hand, Polyaxon is a more comprehensive platform that supports a wider range of machine learning frameworks, tools, and libraries, providing more flexibility in choosing the ones that best suit the project requirements.

2. Model Pre-trained on Multiple Datasets: NSFWJS comes with a pre-trained model that is trained on various adult content datasets, helping to detect and filter NSFW content accurately. Polyaxon, on the other hand, does not come with a pre-trained NSFW model, requiring users to train their own model from scratch using their dataset, which can be more time-consuming and resource-intensive.

3. Real-time Inference: NSFWJS is optimized for real-time inference, making it suitable for live content moderation applications such as social media platforms. In comparison, Polyaxon focuses more on training and managing machine learning models, lacking the real-time capabilities needed for instant NSFW content detection.

4. Deployment Options: NSFWJS is primarily designed for front-end deployment, making it easy to integrate into web applications for client-side content filtering. Conversely, Polyaxon offers more deployment options, including cloud-based, on-premises, and hybrid setups, catering to a wider range of deployment requirements.

5. Community Support and Documentation: NSFWJS has a smaller community and less comprehensive documentation compared to Polyaxon, which has a larger user base and extensive resources available for troubleshooting, tutorials, and best practices in machine learning model development and deployment.

In Summary, NSFWJS and Polyaxon differ in their TensorFlow integration, pre-trained models, real-time inference capabilities, deployment options, and community support, influencing their suitability for specific NSFW content detection applications.