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Does anyone know the specific differences and features among the three, Or if one has more features/more flexible to use as a developer?
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We have recently published an evaluation study of seven NLU API-enabled services: API.ai, Amazon Lex, Microsoft LUIS, IBM Watson Conversation, wit.ai, Recast.ai and Snips.ai.
A brief summary of our findings:
In my opinion Luis is more robust and can extract entities in different languages. I've tested in api.ai and dutch did not work for me. If you need english only then any one of them should be fine but if you need to support more languages then better test those languages as well before getting stuck with one service. Bing speech to text is ok but i think to get more robust solution you will need another microsoft service that cleans voice and noise.
wit.ai
vsapi.aivsluis.aiUpdate: API.AI is now Dialogflow. Learn more here.
This blogpost has a really good analysis and comparison of Luis, Wit.ai, Api.ai, Amazon Alexa and IBM Watson services. It also has a nice background on why you would want to build a conversational bot in the first place and some of the challenges that come with that. It's written by the people behind YumiBot (a bot that gives you price quotes for app development).
The general gist is that Wit.ai and Luis are great choices if you're experimenting and just want to get something out for free. Api.ai has a great service and user experience but isn't free. Same with IBM Watson, the latter priced more for enterprise work. Alexa's API is great but only works with Alexa (but given that they have a huge userbase, isn't a bad deal).
Their advice is also to not rely too much on one provider:
I hope this helped a little! I think the best way to make a choice is to just give these services a try. Given that a lot of them are still heavy under development and adding features/changing pricing models, you should try coming at them with a specific use-case and see which one can get you to where you need the quickest.
i am going to answer the last part of your question around flexibility and being a developer, IMO it finally comes down to what you are looking for in these platforms.
If you are a developer using NodeJS or .Net, LUIS.ai has an extensive library and well defined code snippets and example to spin up a decent bot pretty quickly. The intents and entity recognition is a bit below par compared to google, but if you are Microsoft Shop, there are a lot of 1-click integrations to O365, Teams, Skype, cortana etc. The cons for LUIS.ai is their service seems very unstable, as of this writing their LUIS.ai website is not functioning rejecting connections and it has been more than a week, where cortana integration is not working. So the platform is still a work in progress.
Api.ai, from a pure NLU perspective is better than Luis.ai, the followup intents are very easy to setup, the speech priming is vastly superior to Luis.ai(even after speech priming). The cons i would say, it the connectabilty and also the API to build a bot are a bit more complicated than building an MSBot based chat bot.
Another platform open source platform that is gaining traction is RASA NLU. https://rasa.com/. Comparatively the entity recognition and the ranking is still a bit sketchy of large datasets, but its open sources and if you want to get your hands dirty, you can fork their github platform and improve it.
From a pure development perspective, its easier to fly up a chatbot in the MS platform(using luis.ai or qnamaker.ai ), but be prepared to have challenges as they work on stabilizing the platform.
-Kartik
One aspect of this question is how efficient are these tools at understanding natural language. In a recent benchmark we (Snips, a French AI company) just published, we have tested the built-in natural language engines of Alexa (Amazon), SiriKit (Apple), Luis (Microsoft), and API.ai (Google).
We tested their ability to understand natural queries like “Find me a salad bar I can go to for my lunch meeting”, “Order a cab for 6 people”, as well as 326 other queries.
The overall conclusion is that all solutions are imperfect.
More precisely, they all have similar levels of noise in their responses (between 60% and 90% precision), but there are significant differences in the breadth of language they can support. From this perspective, Luis performs the most poorly: on every use case we tested, it understood less than 14% of the queries. API.ai performs better, although not very reliably: it understands between 0 and 80% of the queries we tested, depending on the use cases. The highest levels of recall can be observed for Alexa (42% and 82% recall) and Siri (61% recall).
More details, and the raw data behind these results can be found in our blog post, Benchmarking Natural Language Understanding Systems