The document compares and evaluates three chatbot platforms: Rasa, Google Dialogflow, and AWS Lex. It summarizes that Rasa is more customizable than Dialogflow or Lex as it allows for custom models, flexible conversation flows, model tweaking, and integration of external APIs. While Dialogflow and Lex are easier to set up and use, Rasa provides more control and customization options for building chatbots. The document concludes that Rasa is a better option if more customization is required, while Dialogflow and Lex are better for chatbots that do not require extensive customization.

1. Battle of
the Bots:
Rasa vs Google
Dialogflow vs
AWS Lex

2. 02
About Rasa
Rasa is an open-source Machine Learning
(ML) framework for building chatbots. It
supports both text and voice-based
conversations. It can connect to
messaging channels like Facebook, Slack,
etc., and APIs.
Rasa also has an additional component
called Rasa X, which helps in improving
the chatbot using real conversations and
also helps in building a chatbot.
There are a lot of frameworks and
platforms to build a chatbot. The basic
features are common in all the
frameworks. Considering features and
customization, in our opinion, Google
Dialogflow and Rasa works better.
At Ideas2IT we frequently develop
chatbots for our customers. Before
starting each project we evaluate which
chatbot platform to build on as there are
many compelling choices and it is a
moving target as the platforms keep
evolving.
We recently evaluated Rasa vs Google
DialogFlow vs AWS Lex. Here is our
conclusion and the reasons behind it.
Spoiler alert, we went with Rasa!

3. 03
Why Rasa?
Intent/Entity Model
Rasa has a concept of a pipeline for this. They have added a recent state-of-the-art
model - Bidirectional Encoder Representation from Transformer (BERT) into the
pipeline which helps in developing better models. Rasa also provides an additional
advantage of adding our own custom model into the pipeline for any task.
While Dialogflow from Google is a solid platform with good models and pre-trained
entities, but it does not support custom models. The same goes for Lex.
Conversation Flow
For the conversation flows, Rasa uses a transformer model (recent state-of-the-art
model) and it’s based on the data or conversation flow given. In this way, we can create
our own flow in a more generic manner.
In Dialogflow, conversations are driven by the context provided for intents (their own
dialogue management), which is somewhat rule-based and not as flexible.
Model Tweaking
Since Rasa is open-source, we can evaluate the model and configure it based on our
needs. In Dialogflow or Lex, models are a black box, where we don’t have any option of
improving or evaluating the models.
API Integration
In Rasa, any external API can be integrated, like SQL, graph db or our own API to keep
up the conversation.
Dialogflow or Lex also provides API integration but Rasa additionally supports the
Knowledge graph integration which is more useful in having a better conversation.
Language Support
Rasa provides language support using the pipeline concept since it is more
customizable, we can add our own component to support multiple languages.
Dialogflow supports multiple languages as an inbuilt feature, but Lex currently
supports only US English.

4. Deployment
It can be deployed in the cloud and also on-premise.
Lex and Dialogflow are cloud-based platforms.
Customization
Rasa provides a lot of customization, in terms of business logic to be implemented,
model, deployment and integration. Rasa provides its SDK where you can implement
your own custom logic.
Dialogflow and Lex don’t provide any kind of customization based on business needs,
only the training data and the input rule-based dialog flow can be customized.
Pricing
There is an enterprise version in Rasa which provides additional features like Analytics,
Expert support, Role-based access control, etc.
Dialogflow has a Freemium version and also an Enterprise version. In the enterprise
version, it supports Text to Speech/Speech to Text features, Sentiment analysis feature,
etc.
With Lex, it’s pay for what you use. The pricing is based on the number of requests.
Stability
Rasa has more than 8.5k GitHub stars with 300+ contributors, provides consistent and
stable releases every month.
Documentation
Rasa also has good documentation, video tutorials, and examples for anyone to get
started. They have a separate forum to resolve any issues and also a blog page to
explain their features.
Project Adoption
Rasa supports a diverse range of industries like Healthcare, Insurance, Travel, HR,
Banking, Telecom, and a lot more.
Companies like HCA healthcare, Helvetia Insurance, Adobe, Orange SA telecom,
Raiffeissen Swiss bank, Engie Utilities use Rasa to create their AI assistants.
04

5. 05
Latest Improvement
Recently Rasa has added support for Tensorboard 2. Tensorboard is used to visualize
training metrics on top of Tensorflow. It helps to understand if the model has been
trained properly and you can make changes to your hyperparameters based on the
metrics to improve your model.
Funding
Rasa has announced that it has raised $13 million in a Series A round of funding led by
Accel, with participation from Basis Set Ventures, Greg Brockmon (co-founder & CTO
OpenAI), Daniel Dines (founder & CEO UiPath) and Mitchell Hashimoto (co-founder &
CTO Hashicorp).
Cons of Rasa
The initial setup of rasa is tedious and a lot of installations are required
Knowledge of python and how conversational chatbot works is required
Though there is flexibility in deployment, one should have knowledge about the
deployment process
Our Verdict
Google Dialogflow and Rex help you building a chatbot easily and it does not
require any knowledge of AI. But If you want to have a wide range of
customizations and more control over how you build your chatbot, Rasa would
be the better option.

6. Founded by an ex-Googler, Ideas2IT started its journey as a high-end product
engineering partner for Silicon Valley startups. Ideas2IT has produced 150+ top-quality
applications for 100+ clients such as Microsoft, Oracle and Opportun. Ideas2IT oers
specialist capabilities in the domains of Data Science, IIoT, Blockchain, Cloud-based
SaaS, Robotic Process Automation, Frontend, Backend & Fullstack Development and
Intelligent Chatbots.
To know more, talk2us@ideas2it.com or visit www.ideas2it.com
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