Dialogflow vs rasa NLU: What are the differences?

Introduction

Dialogflow and Rasa NLU are two popular natural language understanding (NLU) platforms used for building conversational agents or chatbots. Both platforms offer similar functionalities but have some key differences that set them apart.

1. Training and Deployment: Dialogflow provides a user-friendly web interface for training and deploying chatbots, which makes it easy for non-technical users to build and manage conversational agents. On the other hand, Rasa NLU requires technical knowledge as it is a command-line tool and needs to be integrated with a developer's own application for training and deployment.

2. Customization and Control: Rasa NLU offers more customization and control compared to Dialogflow. With Rasa NLU, developers have full control over the machine learning models and can customize them according to specific requirements. Dialogflow, on the other hand, offers limited customization options and relies on pre-trained models provided by Google.

3. Open Source vs Proprietary: Rasa NLU is an open-source platform, which means developers can access the source code, customize it, and contribute to its development. This provides more flexibility and allows for community-driven improvements. On the contrary, Dialogflow is a proprietary platform owned by Google, and its development is controlled by Google.

4. Speech Recognition: Dialogflow has built-in support for automatic speech recognition (ASR) and can directly process spoken input, making it suitable for voice-based applications. Rasa NLU, on the other hand, primarily focuses on text-based input and does not provide native support for speech recognition.

5. Integration: Rasa NLU provides more flexibility for integration with other frameworks and technologies. Developers can integrate Rasa NLU with their own applications, backend systems, or even other NLU platforms. Dialogflow also allows integration with certain platforms, but it may have limitations depending on the specific use case.

6. Cost: Dialogflow offers a free tier with limited usage quotas, but there are pricing plans available for higher usage and enterprise features. Rasa NLU, being open-source, has no direct cost associated with it, but developers may need to invest in their own infrastructure and development resources.

In Summary, Dialogflow provides a user-friendly interface and easy deployment for chatbot development, while Rasa NLU offers more customization, control, and flexibility for integration and speech recognition.