Tech giants such as Google, IBM, Microsoft, Amazon, and Facebook are investing in conversational AI to make developers easily build chatbots. These AI-powered chatbots can automate various routine tasks such as sending emails, searching for information on search engines, etc.
In this research we are focusing on a competitive chatbot platform on market owned by Google, Dialogflow. Other platforms are also compared with Dialogflow to see the advantages and disadvantages of each one.
What is Google Dialogflow?
Dialogflow, formerly api.ai, is a chatbot development platform acquired by Google in 2016. Under Google ownership, its was renamed as Dialogflow.
In 2019, Dialogflow introduced Dialogflow CX, a more advanced version of the platform intended for larger-scale, more complex conversational AI applications. Dialogflow CX brought new features, including a visual flow builder, state-based routing, and a more advanced version of the intent-based framework.
Dialogflow has expanded its capabilities in natural language processing (NLP). Platform leverages Google’s machine learning and AI infrastructure. This includes improvements in entity recognition, intent matching, and multilingual support.
Since 2016, Dialogflow has also improved its integration with Google’s cloud services. They start to offer better integration with services such as:
- Google Cloud Functions
- Firebase
- Cloud Storage
Dialogflow now supports Dialogflow ES and Dialogflow CX as separate products, each designed for different use cases: Dialogflow ES for simple to medium complexity applications and Dialogflow CX for large-scale, enterprise-grade applications.
The main platforms that are compatible with Dialogflow are listed below. It can be integrated to all of the basic conversation platforms, except Whatsapp. Using other platforms such as Dialogflow can be integrated into WhatsApp, too.
- Google Assistant
- Slack
- Twilio
- Skype
- Telegram
- Facebook Messenger
- Amazon Alexa
How does Google Dialogflow work?
On a high level, Dialogflow system works as described below:
- The user sends an input to the application through a device. This input can be a text or a voice message. This message is sent to Dialogflow.
- The incoming message is categorized by Dialogflow and matched with the intents defined by the chatbot developer. Chatbot developer can use training phrases to train the system in intent identification.
- A request is sent to the webhook service for an advanced scan of what action to take for this entry. The responses of the bot can be written directly by the developer or can be selected by Dialogflow. The dialogue system can be externally fed and developed through webhook and external APIs.
- Webhooks are structures that are automatically triggered according to defined actions and return HTTP response.
- Difference between an Application Programming Interface (API) and a webhook: An API needs to be triggered. Webhook is triggered automatically when a certain action takes place.
- The most appropriate action is reported to Dialogflow again as a result of external API and scanning in the database.
- Arranges the Dialogflow answer in order to convey the appropriate answer correctly in the integrated platform.
- Formatting is done in order to give the correct action in the application or device.
- The end-user receives the message.
How does it compare to other chatbot platforms?
Different chatbot development platforms can be evaluated in terms of ease of use, integration options, language support, and fees. Considering all these factors, Dialogflow is one of the most prominent platforms.
We reviewed these platforms:
- Google Dialogflow
- Amazon Lex
- IBM Watson Assistant
- Facebook’s Wit.ai
- Microsoft Azure Bot Service
1.Conversational AI/ NLP capabilities
- Amazon Lex: Amazon generally excels in scenarios where integration with AWS services is necessary. Lex uses deep learning-based language models to process speech and text but the learning curve is high.
- IBM Watson Assistant: IBM Watson Assistant is known for its enterprise-grade applications. Watson’s ability to integrate with other Watson AI services (like Watson Discovery) gives it an advantage in data-heavy applications.
- Wit.ai: Wit.ai’s focus is mostly on developer-centric, open-source NLP. It allows developers to build customizable models. However, its NLP capabilities may not be as advanced out of the box as those of Dialogflow or Watson.
- Microsoft Azure Bot Service: Microsoft’s Azure Bot Service integrates its other enterprise tools, including cognitive services for NLP. It has a strong presence in enterprise environments but may not be as intuitive as Dialogflow for small businesses or those without dedicated technical teams.
2. Ease of use
- Dialogflow: Dialogflow provides interface and simple setup for small businesses and developers. It supports a visual conversation flow builder in Dialogflow.
- Amazon Lex: Lex requires more technical expertise in configuring AWS services. It can be difficult for non-technical users to fully utilize the platform.
- IBM Watson Assistant: IBM Watson offers a user-friendly interface but can still be complex for those unfamiliar with conversational AI. It is designed more for large-scale applications than for non-technical users.
- Wit.ai: Wit.ai is mainly focused on developers, so it does not offer as user-friendly an interface for non-technical users. This makes it less ideal for businesses that do not have a technical team.
- Microsoft Azure Bot Service: The Azure Bot Service integrates well with the broader Microsoft ecosystem, which could be beneficial for businesses already using Microsoft’s tools.
3.Integration capabilities
- Dialogflow: Dialogflow supports a wide range of integrations, including Google Assistant, Facebook Messenger, Slack, Telegram, and others. It can also integrate with custom platforms via APIs or webhooks.
- Amazon Lex: Lex offers integrations with AWS services like Lambda and Alexa but has limited out-of-the-box integrations with third-party platforms compared to Dialogflow.
- IBM Watson Assistant: IBM Watson offers integrations with several messaging platforms and business systems, including custom integrations through APIs and webhooks. However, it does not have as many pre-built integrations as Dialogflow.
- Wit.ai: Wit.ai lacks direct integration with major platforms like Facebook Messenger or Slack. Instead, developers need to build custom solutions using libraries in various programming languages such as Python or JavaScript.
- Microsoft Azure Bot Service: Azure Bot Service integrates well with Microsoft’s ecosystem (e.g., Teams, Office 365) and offers connectors to platforms like Facebook Messenger, Skype, and Slack. However, its integrations with non-Microsoft platforms may require additional work or custom connectors.
4.Language support
- Dialogflow: Dialogflow supports more than 20+ languages, including English, Spanish, French, and others. It also supports regional variations of languages.
- Amazon Lex: Currently, Amazon Lex primarily supports English.
- IBM Watson Assistant: IBM Watson Assistant supports 10+ languages in its beta version, including English, French, German, and Spanish.
- Wit.ai: Wit.ai supports over 50 languages, which makes it a great choice for developers targeting a global audience. However, the quality of NLP might not be as consistent across all supported languages.
- Microsoft Azure Bot Service: Azure Bot Service offers support for 30+ languages.
5.Cost
- Dialogflow: Dialogflow has a free tier that supports small and medium-sized businesses. The enterprise version is priced at $0.002 per request. There is also a pay-as-you-go pricing model for users who need more extensive usage.
- Amazon Lex: Amazon Lex offers a free tier for the first year of use, after which pricing starts at $0.004 per voice request and $0.00075 per text request.
- IBM Watson Assistant: IBM Watson offers a free plan with 10,000 messages per month. After that, pricing starts at $0.0025 per message.
- Wit.ai: Wit.ai offers free access to its platform for both personal and commercial use, which makes it a good choice for startups or small businesses with limited budgets.
- Microsoft Azure Bot Service: Azure Bot Service offers a free tier for up to 10,000 messages per month, with a paid plan starting at $0.0005 per message.
6. Use Cases and Target Audience
Each platform serves different types of businesses and use cases. Here’s an overview:
- Dialogflow: Ideal for small and medium-sized businesses looking for a user-friendly platform to build conversational AI without needing extensive technical skills. It is also well-suited for developers working on more complex applications.
- Amazon Lex: Best suited for businesses already using AWS services or those that need strong integration with voice applications.
- IBM Watson Assistant: Targeted at large enterprises with complex needs, including integration with other IBM AI services.
- Wit.ai: Best for developers who need more flexibility in creating highly customizable conversational AI systems.
- Microsoft Azure Bot Service: Best for organizations that already use Microsoft tools and need tight integration with them, or those with enterprise-scale chatbot needs.
You can also check out our guide on chatbot companies which also includes chatbot developers and developers of smaller platforms to provide an overview of the chatbot ecosystem.
If you have questions about how chatbots and Dialogflow can help your business, feel free to ask us:
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