1. Natural Language Processing (NLP): IBM Watson offers more advanced and customizable NLP capabilities compared to Wit.ai. Watson provides a wide range of NLP services, including sentiment analysis, entity recognition, and language translation, while Wit.ai focuses more on simpler natural language understanding tasks.

2. Training Data Requirement: Wit.ai usually requires less training data compared to IBM Watson to achieve comparable performance in language processing tasks. Watson typically needs a larger and more diverse dataset for training its models effectively, while Wit.ai can perform well with smaller amounts of data, making it more suitable for smaller projects or applications with limited training data.

3. Pricing model: IBM Watson often follows a complex and tiered pricing model, where users may need to pay for specific services or features separately. In contrast, Wit.ai is known for its simpler and more flexible pricing structure, often offering a freemium model that allows developers to start using the platform for free with certain usage limits before upgrading to paid plans.

4. Integration Capabilities: IBM Watson provides a wide range of integration options with various platforms, tools, and programming languages, making it more versatile for developers working in different environments. Wit.ai, on the other hand, may have more streamlined integrations with specific platforms such as Facebook Messenger, making it easier to implement chatbots and conversational interfaces within those ecosystems.

5. Development and Support Community: Wit.ai has a more user-friendly and developer-focused community compared to IBM Watson. Wit.ai's community forum and resources are often more accessible and catered towards developers of all levels, making it easier for newcomers to get started with the platform. On the other hand, IBM Watson's community and support channels may be more geared towards enterprise users and larger-scale projects, potentially requiring a higher level of expertise.

6. Customization and Control: IBM Watson offers greater customization and control over the underlying machine learning models and algorithms, allowing developers to fine-tune their applications for specific use cases. Wit.ai, while easier to use out-of-the-box, may have limitations in terms of customization options and may not provide as much control over the underlying AI models, which could affect the precision and accuracy of language processing tasks.

In Summary, IBM Watson and Wit.ai differ in their NLP capabilities, training data requirements, pricing models, integration capabilities, community support, and customization options.