AWS provides Amazon Lex, a chatbot building technology.
$0
Per Speech Request
IBM watsonx Orchestrate
Score 8.4 out of 10
N/A
IBM® watsonx™ Orchestrate® leverages AI to automate complex workflows. The solution helps build, deploy, and manage AI assistants and agents. It offers a catalogue of pre-built agents and tools, low-code agent builder, multi-agent collaboration capabilities, and integrations with enterprise apps.
$500
per month
Pricing
Amazon Lex
IBM watsonx Orchestrate
Editions & Modules
Request and Response
$0.004
Per Speech Request
Stream Conversation
$0.0065
Per Speech Interval
Essential
$500
per month per subscription
Essentials
$500
per month Per subscription
Standard
Enterprise
Standard
Enterprise
per month Per subscription
Offerings
Pricing Offerings
Amazon Lex
IBM watsonx Orchestrate
Free Trial
No
Yes
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
Yes
Entry-level Setup Fee
No setup fee
Optional
Additional Details
—
IBM watsonx Orchestrate can be deployed and run on IBM Cloud, AWS, or on-premises. Prices shown are indicative, may vary by country, exclude any applicable taxes and duties, and are subject to product offering availability in a locale.
We [felt like Amazon Lex was a] trap, because [while] we consider it a credible
tool, to our surprise, it was not [as] efficient [or] as effective as IBM
Watson [Assistant] is. In a jiffy, IBM Watson [Assistant] has taken a special place [in our organization] and we [are not …
Google Cloud Dialogflow, Amazon Lex, and Azure AI Bot Service were assessed before selecting IBM watsonx Assistant. The primary reason we used IBM watsonx Assistant is the ability to remember diverse contexts during the multiphase bookings and to support multiple languages that …
It focus on enterprise level flexibility and it also provides us cloud and on prem both support which helps in integrating with legacy and modern systems. It has better compliance standards than other competitors. Better data governance and cost effective AI model which is very …
IBM Watson Assistant is doing great as other tools directly search from the internet and it need to understand all the algorithm to fetch the right answer. Also design of this is very difficult as they need to much of data feed where as IBM's tool can train by itself using both …
IBM Watson's assistant uses AI that helps to understand and translate the user requirements and conversations to provide fast and accurate results that help to optimize the business processes. It is a highly reliable and secured bot solution as compared to the other similar …
IBM Watson is more affordable as compared to Lex, but that is not the only reason we switched to IBM Watson from Lex. Lex is comparatively less robust and takes more time in model training and sometimes the UI froze when we tried to implement a bunch of commands. IBM has just …
Verified User
Engineer
Chose IBM watsonx Orchestrate
The UI is very clean which makes it easier to understand the product. Also, there are loads of documentation to learn more about how to use this tool for AI. When I was using Watson Studio, it was fairly well-integrated w/ Watson Cloud, allowing one to spin up infrastructure to …
Verified User
Consultant
Chose IBM watsonx Orchestrate
IBM Watson Assistant seems to be easier to learn while also having more functionality. Seems like IBM Watson Assistant can learn intents on fewer utterances than Lex. I also prefer the dialog UI. The UI shows a dialog tree with various drop-downs.
The ease of use working with IBM Watson Assistant is simple when compared with the other players. More scalability, fewer bot building complexities, simple integration capabilities, and multi-language support. Most of all, it comes from the IBM product suite which clearly …
If you wish to quickly deploy multilingual chatbots without having to worry about infrastructure and model training, go for Amazon Lex. It is one of the best general-purpose conversational AI solutions in the market. The cherry on the cake is that it also seamlessly integrates with other AWS services, so you would be good there. Performance monitoring is very easy with AWS. It has support for both text and integration. If you are not a pro-NLP expert, Amazon Lex will make your job really easy.
In our case, it is well-suited for workday integration, which allows us to automate the entire workflow. However, we are still working on the O9 platform integration, which we feel is less appropriate, and integrating the workflow into the platform.
New and improved natural language processing yielding better results helps the assistants understand the intention behind the query.
Preserves context of communication, allowing the customers to establish inquiries on the website and continue on the mobile app without having extra informational input.
Intelligent conversations mean that complex paths that are branched based on the user's inputs allow for a much more natural flow of the conversation than fixed scripts.
I think that it needs to be able to integrate better with the knowledge catalogs. It currently provides a default database, which isn't quite large enough for enterprise use. We can connect that then to an external source, but it'd be nice if we could able just to instantiate one straight away.
Currently we are using to develop chatbots based on client provided flow what kind chatbot required for client either button or free text chatbots. we will decided accordingly flow and develop chatbot using IBM Watson. We will integrated custom components if required which is not present in library. Action flow and dialog flow we are currently in chatbot.
Easy to deploy and very easy to integrate with other AWS services. Automating simple tasks is also very easy with Amazon Lex. We never had NLP experts in our team, but we were still able to deploy chatbots for our support functions with minimal issues. Native integration with other AWS services like S3 and Lambda has been of paramount importance.
With the growing use of AI and chatbots, it's very easy to use, and the conversational language makes it easier than keyword searches in a document. The contextual language processing is impressive. It's easy to integrate into our internal portal. The use of this tool would depend on each company's security and data sensitivity.
To develop chatbots based on client provided flow what kind chatbot required for client either button or free text chatbots. we will decided accordingly flow and develop chatbot using IBM Watson. We will integrated custom components if required which is not present in library. IBM Watson library anyone can easily learn and develop chatbots.
Community support for Amazon Lex is good. Also, since it is an AWS service, the support has a similar standard as other AWS services. We have had a couple of instances of our bots weren't able to interact with our web apps. We reached out to the support team, and they were able to resolve our issue in no time. The documentation from the Amazon Lex team also makes creating chatbots a breeze.
We've rarely had to engage support, but they've always been prompt in responding and very attentive. Support experiences have been extremely positive (but we're mostly happy that we just don't have any cause to routinely need support in the first place!).
Make has more community of workflows to follow that have been redeveloped and are available for download. Selecting WxO is based on our trust level with IBM and the propositions of the Granite model being less biased, more business trained, and the ecosystem allowing for expansion with Assistant and Discovery.
From past 3+ years I am using IBM Watson in our current project easily can implement and manage and monitor user how their using. Is there and update also just update dialog is just enough to change no need to touch any other templates. Multiple language will support, and action and dialog speak recognize chatbot we can create as per client requirement. Overall, as of now good experience with IBM Watson.
By automating tasks that would otherwise require human intervention, organizations may achieve cost savings in terms of labor, especially for handling large volumes of routine inquiries.
Virtual assistants can handle a large number of simultaneous interactions, making them scalable to accommodate growing customer bases and increasing workloads without a linear increase in staffing.