A Comparison Of Eight Chatbot Environments

Original article was published on Artificial Intelligence on Medium

A Comparison Of Eight Chatbot Environments

Discover which Chatbot Framework Might Work Best For Your Organisation


This research based on a nine point comparison matrix, here I look at the strengths, weaknesses and possible growth of each solution…

I have built prototypes with most of the commercial cloud and opensource Conversational UI & AI products currently available.

Overview Of Development Environment

What I have found is that the environments are generally very similar in their approach to tools available for crafting a conversational interface.

Considering what’s available, I am beginning to realize chatbot development environments can be segmented into 4 distinct groups.

These groups being:

  • Leading Commercial Cloud Offerings
  • NLU / NLP Tools (most opensource)
  • The Avant-Garde
  • The Use-the-Cloud-You’re-In

Leading Commercial Cloud Offerings

The leading commercial cloud environments attract customers and users to them purely for their natural language processing prowess and presence.

Among these I would count IBM Watson Assistant, Microsoft Bot Framework / Composer / LUIS / Virtual Agents, Google Dialog Flow etc.

Watson Assistant Logo

Established companies gravitate to these environments, at significant cost of course. These are seen as a safe bet, to meet their Conversational AI requirements.

They are seen as chatbot tools providers in and of their-self.

Scaling of any enterprise solution will not be an issue and continuous development and augmentation of the tools are a given. Resources abound with technical material, tutorials and more.

NLU /NLP Tools

There are also opensource tools like spaCy, Apache OpenNLP, RASA NLU and others which can be used to to process natural language in your environment.

Some organisations are creating their own chatbot framework making use of these tools.

Industrial-Strength Natural Language Processing

This is the harder route and is more time consuming, but if you have an existing environment, augmenting it with natural language processing capability, making use of these tools is a viable option.

It is truly astonishing the power of most of these opensource tools. And with the documentation available, it can serve as a “no software cost” point of departure for a first foray into natural language processing.

The Avant-Garde

Here RASA really finds itself alone. They follow a very unique path in terms of wanting to deprecate the state machine and hard-coded dialog flows/trees.


Their entities are contextually aware and they follow an approach where entities and intents really merging.

It is said their market-share is currently at 6% (I could not confirm this). However based on their expansion, funding, developer advocacy and events this is a company to watch. And hopefully the bigger players will emulate them. One of their strong points is developer advocacy.

RASA has succeeded in creating a loyal developer following.


I cannot help but feel Amazon Lex with Oracle Digital Assistant (ODA) find themselves in this group. My sense is that someone will not easily opt for ODA or Lex if they do not have an existing attachment with Oracle or AWS.

Especially if the existing attachment is Oracle Cloud or Oracle Mobile Cloud Enterprise. Or with AWS via Echo & Alexa.

Oracle Digital Assistant

Another impediment with ODA is cost. Free access plays a huge role in developer adoption and the platform gaining that critical mass. We have seen this with IBM being very accessible in terms of their free tier with an abundance of functionality.

Microsoft has gone a long way in more accessible tools, especially with developer environments. RASA, even though a relatively late starter, has invested much time and effort in developer advocacy. Google Dialogflow is also popular and often a point of departure for companies exploring NLU and NLP.

ODA is not accessible enough and the existing impediments to experimenting and prototyping are not helping.