Dialogflow vs rasa NLU

Overview

rasa NLU

Stacks120

Followers282

Votes25

Dialogflow

Stacks267

Followers667

Votes42

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.

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.
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.
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.
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.
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.
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.

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Advice on rasa NLU, Dialogflow

Stefan

Jul 17, 2020

Needs advice

Hi, does anyone have recommendations for a chatbot framework? I am currently using Botpress, and I am not happy with it. The upside is: They pretty much have everything you can ask for in a bot solution, but the issue is: They did nothing right, the documentation is terrible, and you have this feeling of it falling apart at any time, which is what actually happened once.

My ideal solution would have:

Support for Messenger and web (should either have a website chat plugin or straightforward integration with a different one)
A visual builder (for none tech team members) | This is not a hard requirement though
A slick DX for building simple things like API calls or more advanced stuff.
We currently only have a "click bot," so no crazy NLP features required, but in the future a requirement

What I do not want:

I do not want a solution where "someone else" builds the bot for me

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Detailed Comparison

rasa NLU	Dialogflow
rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.	Give users new ways to interact with your product by building engaging voice and text-based conversational apps.
Open source; NLP; Machine learning	-
Statistics
Stacks 120	Stacks 267
Followers 282	Followers 667
Votes 25	Votes 42
Pros & Cons
Pros 9 Open Source 6 Self Hosted 6 Docker Image 3 Comes with rasa_core 1 Enterprise Ready Cons 4 Wdfsdf 4 No interface provided	Pros 18 Built-in conversational agents 7 Custom Webhooks 5 Multi Lingual 5 Great interface 4 OOTB integrations Cons 9 Multi lingual 2 Can’t be self-hosted
Integrations
Slack RocketChat Google Hangouts Chat Telegram Microsoft Bot Framework Twilio Mattermost	No integrations available

What are some alternatives to rasa NLU, Dialogflow?

Engati

It is a free chatbot platform to build bots quickly without any coding required. It allows you to build, manage, integrate, train, analyse and publish your personalized bot in a matter of minutes.

Telegram Bot API

Bots are third-party applications that run inside Telegram. Users can interact with bots by sending them messages, commands and inline requests. You control your bots using HTTPS requests to our bot API.

Botpress

Botpress is an open-source bot creation tool written in TypeScript. It is powered by a rich set of open-source modules built by the community. We like to say that Botpress is like the WordPress of bots; anyone can create and reuse other peo

Microsoft Bot Framework

The Microsoft Bot Framework provides just what you need to build and connect intelligent bots that interact naturally wherever your users are talking, from text/sms to Skype, Slack, Office 365 mail and other popular services.

Amazon Lex

Lex provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text, to enable you to build applications with highly engaging user experiences and lifelike conversational interactions.

SpaCy

It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.

Chatfuel

Send news, collect feedback, receive and answer questions and share content libraries — from GIFs to full business docs.

Flow XO

Everything you need to create and manage bots. Build powerful bots without code, bots work seamlessly across platforms, and we host, manage & scale your bots.

IBM Watson

It combines artificial intelligence (AI) and sophisticated analytical software for optimal performance as a "question answering" machine.

Wit.ai

Iti is an API that makes it very easy for developers to create applications or devices that you can talk to. Any app, or any device, like a smart watch, Google Glass, Nest, even a car, can stream audio to the Wit API, and get actionable data in return. We turn speech into actions. Think Twilio for Natural Language, with Stripe-level developer friendliness.

Related Comparisons

Dialogflow vs rasa NLU: What are the differences?

Introduction

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.
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.
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.
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.
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.
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.

Dialogflow vs rasa NLU

Overview

Dialogflow vs rasa NLU: What are the differences?