First published: Oct 2018 | Last updated: Sep 2020
I get these types of questions often:
“How does Watson compare to Dialogflow?”
“What do you think of LUIS?”
So I am going to discuss my opinions in this article.
The first disclaimer is that I am clearly biased. I am primarily a Dialogflow consultant. At the same time, I am hoping this post starts a discussion amongst people and helps people do some homework before selecting the appropriate framework.
The second disclaimer is that I haven’t had time to actually work on bots using the other frameworks, and used my knowledge of Dialogflow to consider what I feel are the important features. Then I watched a bunch of online tutorials to check how to implement those same features in the other frameworks. It is definitely possible I missed a thing or two during this process.
If you still want to read my opinions after those disclaimers, read the lessons in this chapter in order (use the Table Of Contents on the left pane).
IBM Watson Assistant
September 2020 Update
Dialogflow has now released a completely revamped version called Dialogflow CX. The existing version has been renamed to Dialogflow ES (for Essentials). Note: Dialogflow CX is still in beta.
Not only does it have a much steeper learning curve for non-programmers, it is also more expensive. Plus it only makes sense for fairly complex chatbots. So why bother? 🙂
Here is why: it is also a complete departure from the way other chatbot frameworks operate (more on that in a later article), and fixes many annoying issues with Dialogflow ES. It also uses the latest Machine Learning algorithms from Google for its intent mapping. So how does that benefit you? If it is designed well, it will make your bot capable of having much more intelligent multi-turn conversations, which have been pretty hard to achieve till date.
July 2020 Update
Here is how I would summarize my views in July 2020.
Consider the following:
- Dialogflow is the easiest option if you are not a programmer, but want to participate fully in the bot building process. It is very easy to compose all kinds of conversational bots (see some examples) using Dialogflow’s visual dialog builder and existing flowchart tools
- The other bot frameworks don’t have a comparable visual dialog builder. The lack of explicit contexts (described below) makes a big difference, in my opinion
- RASA NLU, being open source, is a lot more customizable than Dialogflow. But it is also much harder for technical non-programmers to use. Still, it automatically becomes the best alternative if you need an open source bot framework for privacy and flexibility reasons
So the choice will likely boil down to this:
Are you looking for a low code option? Choose Dialogflow
Are you looking for maximum flexibility (and have a large budget to pay the associated costs of hiring developers who know NLU quite well)? Choose RASA NLU
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