In this digital age, being able to solve the queries and problems of customers instantly has become necessary for businesses to establish their brand and consumer loyalty. This is why several companies worldwide are now using artificial intelligence tools to streamline customer interactions and improve operational efficiency by reducing the typical cost of customer service.
Among the artificial intelligence tools used by businesses in enhancing customer service through chatbots and app development are DialogFlow and TensorFlow. In this DialogFlow vs TensorFlow comparison article, we will define the characteristics that make both solutions similar and different at the same time to give you a clear picture of what suits better for your business.
Dialogflow is a chatbot development framework that can be used to come up with conversational for the Internet of Things (IoT) devices, messaging platforms, mobile applications, and websites. Located in Palo Alto, California, the software which was initially known as API.AI was founded by Speaktoit in 2010 before being purchased by Google in 2016 and renamed as Dialogflow a year later.
It also comes with robust Natural Language Understanding (NLU) tools that help developers design a distinctive conversation scenario and corresponding interactions, not to mention its ability to evaluate user interactions proficiently. With the use of these tools, Dialogflow can also understand the user’s intent which allows a business to come up with ideas to create a seamless customer experience.
A leader in the human-to-machine interaction sphere, Dialogflow is perhaps best known for its launch of the Speaktoit assistant, which was the previous assistant component for iOS, Android, and Windows mobile phones. While Google discontinued the Assistant app after it purchased the solution in 2016, this artificial intelligence software still makes it easier for developers to concentrate on the integral components of app development rather than on delineating comprehensive grammar rules.
Moreover, Dialogflow also boasts pre-built agents that empower users to build applications and chatbots faster. They are usually seen as NLU modules that are integrated into the app, service, or website of a client to interpret the requests of multiple customers. Plus, Dialogflow agents can manage and direct the flow of conversations naturally thanks to its customizable interfaces that allow users to modify and reuse entities and intents on their projects.
Other than that, Dialogflow is also built with powerful analytics that lets users view the performance of its chatbots. The software’s analytics provides relevant insights into the usage of your conversational interface to find out more about customer behavior so that you can make the necessary improvements on your chatbot and deliver personalized customer service.
Dialogflow is free to use, though, it provides a paid plan designed for medium to significant business, which is available on a quote basis. Best of all, the software is also optimized for Google Assistant, but users may need expert help when navigating the software because of its complex framework and user interface.
It may also come as no surprise that bugs may occur in the Title and Subtitle of the Carousel Card after saving intents which is why having a bug tracking software may come in handy. You can check the top bug tracking software that works well with Dialogflow to prevent unwanted downtimes.
- Powerful Chatbot Builder
- Intuitive User Interface
- Multiple Third-Party Integrations
- Supports Popular Programming Languages
- Limited Webhooks Available
- Difficult to Use for Beginners
- Slow Customer Support
- Inconsistent NLP Engine Results
TensorFlow is a Python-friendly open-source library that is used by programmers, researchers, and data scientists to build models by using either Application Programming Interfaces (API) or writing codes from scratch. Developed by the Google Brain team, this all-encyclopedic open-source library combines together a myriad of machine learning and neural networking algorithms and models to provide users with an extremely high level and abstract approach to classifying low-level numerical programming.
While it was initially developed to perform integrated neural networks research, the software is general enough to be applicable in a plethora of domains thanks to its ability to construct graphs and write an inner loop that drives computation. Through its ability to create charts, users can gain better computational graph visualizations that allow them to interpret insights using one of the data analysis solutions in this software ranking.
Since it uses the Python framework, TensorFlow can provide a convenient front-end API, which makes it easier to build applications and chatbots using the software while simultaneously executing those in top performance C++. Unlike typical solutions, this versatile open-source library can also train and operate on deep networks for word embeddings, image recognition, handwritten digit classification, and sequence-to-sequence for partial-differential equation, NLU, and machine translation.
Technically, TensorFlow works by enabling developers to come up with dataflow graphs that characterize how information transports through a chart or a collection of processing nodes for easier data visualization. You can learn more about the technicalities, essential features, and functionalities of data visualization by checking this list of data visualization software here. Each node in the graph displays a mathematical operation with every edge or connection between nodes being considered multidimensional tensor or data array.
It should be noted, however, that the actual operations are not conducted in Python. Instead, the libraries of transformation that are available through the software are written and developed as top-performing C++ binaries with Python only being the catalyst in redirecting traffic pieces while providing industry-grade programming abstractions to connect them.
This added plugin makes it easier for TensorFlow applications and chatbots to run on GPUs, CPUs, Android, and iOS, not to mention on a local machine or even on Google Cloud using the TensorFlow Processing Unit silicon. Besides that, the resulting models developed by the software can also be deployed virtually anywhere where they will be used to serve predictions.
TensorFlow is also free to use and caters to multiple customer types, including small and mid-sized businesses, large enterprises, and freelancers. Furthermore, the software follows a unique approach that enables tracking the training progress of your chatbots’ models and monitoring multiple metrics.
- Library Management
- Comprehensive Graphs
- Lack of Symbolic Loops
- No Support for Windows
- No GPU Support for Nvidia
- Slow Computation Speed
While Dialogflow and TensorFlow each have a unique structure, both are products of Google, which means that it has the advantages of quick updates, seamless performance, and constant new releases with innovative. Nevertheless, both solutions still differ in so many ways, especially in pricing and usability, among others. Outlined in this Dialogflow vs TensorFlow comparison section is a detailed overview of the differences of both solutions.
Usability is a critical quality indicator for interactive systems as it provides users with a gist of which solutions are easy to use, efficient, error-free, and useful. Both Dialogflow and TensorFlow each have a unique development process in building applications specifically chatbots with the latter providing a myriad of customization options to write code from scratch.
With TensorFlow, users can perform more complex tasks with ease thanks to its multiple methodologies that are based on the Natural Language Processing (NLP) platform. These NLP methodologies include Tf-IDF, Word2Vec, and Bag of Words. It also boasts user-friendly features like scalability, debugging, library management, pipelining, and graphs, not to mention a neural network architecture to develop apps and chatbots.
Meanwhile, Dialogflow is a bit easier to use compared to TensorFlow due mainly to its ability to create a conversational flow in the software’s interface. Dissimilar to TensorFlow, this software primarily uses intents and contexts to build a chatbot conversation.
Intents build the bridge between user queries and the necessary action that needs to be taken while contexts are string values used for differentiating user requests and questions. Although it has specific understanding limitations, Dialogflow may provide more usability to beginners with its quick hands-on feature, whereas TensorFlow may take users some time to get used.
Another factor that needs to be taken in serious consideration in the selection of chatbot builder software is third-party integrations. Both Dialogflow and TensorFlow are brimming with integrations that make it easier for users to expand the software’s functionalities and improve their overall experience.
As part of Google, Dialogflow integrates with popular conversation platforms such as Facebook Messenger, Slack, Google Assistant, and a robust API that allows users to create custom integrations. Other software integrations include Hangouts Chat, LINE, Kik, Telegram, Skype, Twitter, Viber, Genesys, AudioCodes, SignalWire, and Voximplant.
Not to be outdone is TensorFlow, which sports a flexible ecosystem that allows the software to be integrated with any third-party solutions using a customizable API. This way, businesses can come up with web applications and chatbots that will seamlessly integrate with the TensorFlow interface all because of the advent of data sharing and customized APIs.
These customized APIs also make it more straightforward for TensorFlow users to deploy innovative experiments and algorithms while keeping the same server architecture. While TensorFlow may seem like the obvious route when it comes to integrations, it should be noted that DialogFlow does also have a customized API integration, not to mention specified apps that are guaranteed to work well with the solution.
Dialogflow and TensorFlow also differ significantly in the pricing department. While Dialogflow does offer a free standard edition plan, users can also opt for the enterprise edition, which is available on a quote basis.
The Enterprise edition package is geared with a service-level agreement, unlimited text requests, cloud support packages, and unlimited Google Cloud Speech Requests. This is a total upgrade from the Standard plan which only limits users to 15,000 voice requests per month and three queries per second.
In contrast, TensorFlow does not have any enterprise pricing plans and is entirely free. However, it does not provide support for Windows and often requires fundamental knowledge of calculus, linear algebra and perhaps, machine learning techniques.
Choosing The Best Software
In this Dialogflow vs TensorFlow comparison article, we talked about how each solution would stack up against one another and in the business environment, in general. While it does have its shortcomings, both solutions more than makeup for its losses by providing users with a high-level approach to software and chat or development.
A framework like Dialogflow is more user-friendly and is perfect for small business applications. In contrast, TensorFlow may work best with organizations that have an expert professional to handle the development process. Nonetheless, both solutions can build a capable chatbot that is robust and scalable enough to meet the evolving needs of customers and ensure a higher return on investments.