Computer programs have traditionally only been able to use data found or stored in a structured form. However, thanks to the development of increasingly advanced AI programs and chatbots that can understand unstructured text inputs with the use of Natural Language Processing APIs, computers can now be programmed to draw insights and respond to customer needs using the vast amounts of data that are generated daily in the form of casual human conversations, messages, emails, comments, and even tweets. This is despite the fact that such inputs don’t always follow any sort of fixed rules, the kinds that computers have always needed for decision-making and information processing.

There are many high-quality NLP libraries readily available for things such as sentiment analysis, the categorization of conversational topics, and the labeling of content. Such libraries allow chatbot programmers to innovate without having to reinvent things already done in the past from scratch. And because NLP APIs that provide the foundation for any effective chatbot program are the starting point for any chatbot build, here are the best ones you should consider looking into.

Microsoft LUIS

Microsoft’s LUIS (an acronym for Language Understanding Intelligent Service) provides an API that does both intention as well as entity recognition/categorization for your chatbot. LUIS comes with a list of pre-built intents and entities for easy plugging and replication as per your needs.

To build an end-to-end conversational chatbot, you can use Microsoft’s Bot framework which allows the scripting of loose dialogues that provide the underpinning business logic and direction of conversational flow for your chatbot using AI callbacks. When deployed, these dialogues allow LUIS to recognize, and then appropriately respond to, conversational commands and queries.

The free version allows up to 10,000 transactions per month at no cost. The basic version allows 10 transactions per second at a cost of $0.75 per 1000 transactions. is the chatbot development API that was acquired by Facebook back in 2015. The way Wit APIs work is by using domain-specific use cases called stories, and the system’s AI engine simply learns how conversations flow by ‘studying’ example after example of user inputs and bot responses. This chatbot engine is currently available in 11 languages, with close to 40 more slated for release sometime soon. And since all stories that are built by other developers are publicly available for everyone, you can easily copy another developer’s story to get a quick start on your chatbot project. is totally free, but your stories and software logic/libraries are public and available for everyone to view and use. is somewhat similar to, but it has been around for much longer and therefore is a relatively more mature platform.

Chatbot and machine learning applications in are organized using agents, which are similar to stories in There are a handful of out-of-the-box agents that are ready for use as soon as you launch, and these agents can be used for services such as online booking, reservations, authentication, shopping, and the like. For developing new agents or creating new chatbot tasks, you have to define new dialog types. Those new dialogs are programmed to either process end-user inputs or are designed to use decision trees when conversing with a client. can integrate with Facebook, Slack, Amazon’s Alexa, and more, and is currently available in 14 languages, but please note that most of the pre-defined agents come in English.

Pricing ranges from a free version to business and enterprise versions that range from $89 to $899 monthly.

Amazon Lex

With Amazon Lex, you can build conversational interfaces directly into any application using voice or text. By using Automatic Speech Recognition (ASR) for converting speech into text, and by using Natural Language Understanding (NLU) for recognizing the intent of the text, you can build applications that provide highly engaging user experiences, complete with lifelike conversational interactions. Lex provides the same deep learning technologies that are behind Amazon Alexa, enabling developers to quickly and easily build sophisticated, conversational chatbots.

Lex scales automatically, meaning you no longer have to worry about managing infrastructure, and you only pay for what you use. There are no upfront commitments, and no minimum fees either. Pricing is $0.004 per voice request and $0.00075 per text request. is much like, but with a few important differences. Unlike with, there are no automatic responses, and you have to create your own user conversation flow. However, as is the case with Microsoft LUIS, you are provided with intent and entity libraries that allow you to classify inputs for your chatbot to understand. Also, like and some of the other APIs reviewed here, you are allowed to use intents, entities and other categorizations that have been developed by the tech community at large, but you can also opt for an enterprise version that can be used to keep your own logic, libraries, and programming private. is free for developers, as long as you share your code publicly on Github. If you want to develop and then maintain private code, enterprise plans are available via written request to the company.

A growing and thriving chatbot development community has a snowball effect: the more people working in this field means more scripts, libraries, and repos from which to choose, thereby making future advancements and new developments easier and faster in the coming. The five APIs reviewed here are the most popular NLP APIs in use today, and they each have a substantial developer community behind them.

About the article

A general informational post that outlines top Natural Language Processing APIs for building AI apps

Article purpose

Demonstrate expertise in NLP APIs through vendor comparison

Target audience

Mid-funnel audience that already has a basic understanding of how NLP APIs work and is more interested in exploring implementation options.

About the Client

Funded Tech startup focused on building NLP based interactive chatbots for customer service automation