Dialogflow vs Wit.ai: What are the differences?

Introduction

Dialogflow and Wit.ai are both popular natural language processing (NLP) tools used for building conversational AI applications. While they have similarities in terms of functionality, they also have key differences that set them apart. In this comparison, we will explore six important differences between Dialogflow and Wit.ai.

1. Integration with Platforms: Dialogflow provides seamless integration with various platforms, including Facebook Messenger, Slack, and Google Assistant. It offers pre-built integrations and easy setup for these platforms. On the other hand, Wit.ai is more flexible and allows integration with any platform using its API. It provides developers with more control and customization options for integrating their applications.

2. Training Process: Dialogflow offers a user-friendly web interface for training and managing conversational agents. It uses an intent-driven approach, where developers define specific intents and train the AI model accordingly. Wit.ai, on the other hand, takes a more data-driven approach. It relies on a chatbot builder platform to collect user interactions and uses machine learning algorithms to train the AI model based on the collected data.

3. Built-in Small Talk: Dialogflow comes with a built-in small talk feature, which makes it easier to handle generic queries and provide appropriate responses. This feature allows developers to quickly handle common conversational scenarios without the need for extensive training or customization. Wit.ai, on the other hand, does not have a built-in small talk feature. Developers using Wit.ai need to handle generic queries by defining specific intents and training the model accordingly.

4. Entity Extraction: Dialogflow provides robust entity extraction capabilities. It allows developers to define custom entities and extract important information from user inputs. Developers can build complex entity hierarchies and use them to improve the accuracy of their conversational agents. Wit.ai also supports entity extraction but has limitations compared to Dialogflow. It does not support hierarchical entities and has fewer options for customizing entity recognition.

5. Language Support: Dialogflow supports a wide range of languages, including English, French, German, Spanish, and many more. It has built-in language support for over 20 languages. On the other hand, Wit.ai supports fewer languages compared to Dialogflow. Although it covers popular languages like English, Spanish, and French, the language support for Wit.ai is more limited.

6. Pricing Structure: Dialogflow offers a pricing structure based on usage and provides a free tier with certain limitations. It also offers various pricing plans based on the scale and needs of the application. Wit.ai, on the other hand, is completely free to use, regardless of the usage or scale of the application. This makes Wit.ai a cost-effective option for developers on a tight budget.

In summary, Dialogflow offers extensive platform integration, a user-friendly interface, built-in small talk, robust entity extraction, wide language support, and a flexible pricing structure. Wit.ai, on the other hand, provides more customization options for platform integration, takes a data-driven training approach, requires explicit definition of intents for small talk, has limited entity extraction capabilities, supports fewer languages, and is completely free to use.