Optimizely Opal vs. Rasa

Optimizely Opal

Optimizely Opal

0 Reviews and Ratings

Rasa

Rasa

5 Reviews and Ratings

Overview
Product	Rating	Most Used By	Product Summary	Starting Price
Optimizely Opal	Score 0.0 out of 10	N/A	Optimizely Opal is an agent orchestration platform for marketers, used to create, govern and scale AI—no developers needed. It helps marketers with tasks ranging from personalized content creation and continuous compliance checks, to automated reporting.	N/A
Rasa	Score 8.4 out of 10	Enterprise companies (1,001+ employees)	Rasa is a conversational AI platform from the company of the same name headquartered in San Francisco, enabling enterprises to build customer experiences. Rasa’s platform was built to create enterprise-grade virtual assistants, allowing personalized conversations with customers - at scale. Rasa’s conversational AI platform allows companies to build better customer experiences by lowering costs through automation, improving customer satisfaction, and providing a scalable way to gather customer…	$0

Pricing

Optimizely Opal

Rasa

Editions & Modules

No answers on this topic

Developer Edition: $0
Growth: starting at $35k
Enterprise: Contact Sales

Offerings

Pricing Offerings
Optimizely Opal	Rasa
Free Trial
No	Yes
Free/Freemium Version
No	Yes
Premium Consulting/Integration Services
No	No

Entry-level Setup Fee

No setup fee

No setup fee

Additional Details

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More Pricing Information

Community Pulse
	Optimizely Opal	Rasa
Considered Both Products	Optimizely Opal No answer on this topic	Rasa Verified User Anonymous Chose Rasa Kore.ai and Yellow.ai Incentivized Helpful? JS Jeroen Spriet Owner/BA/FA/Dev/UX/UI Chose Rasa Google Cloud Dialogflow, Botpress and Azure AI Bot Service Incentivized Helpful? Verified User Anonymous Chose Rasa The NLU algorithms are more efficient in Rasa. Creating conversations is much easier. In IBM, the more use cases we created, the more complicated it was to up date the entire model. It was quite common to mess up what had already been done.Rasa has greater scope for use with … Incentivized Helpful? Martin Lukan Senior Data Scientist Chose Rasa Glean - proprietary semantic search algorithms, no backend actions integration IBM Watsonx - complicated dialogue builder, poor separation of no-code and pro-code interfaces ELMOS (agent based) - all logic in code, no dialogue logic in no-code interface possible Incentivized Helpful?

Best Alternatives
	Optimizely Opal	Rasa
Small Businesses	Jasper Score 7.9 out of 10	Jasper Score 7.9 out of 10
Medium-sized Companies	IBM watsonx Orchestrate Score 8.3 out of 10	IBM watsonx Orchestrate Score 8.3 out of 10
Enterprises	IBM watsonx Orchestrate Score 8.3 out of 10	IBM watsonx Orchestrate Score 8.3 out of 10
All Alternatives	View all alternatives	View all alternatives

User Ratings
	Optimizely Opal	Rasa
Likelihood to Recommend	- (0 ratings)	8.4 (0 ratings)
Usability	- (0 ratings)	7.0 (0 ratings)

User Testimonials
	Optimizely Opal	Rasa
Likelihood to Recommend	No answers on this topic	I have been using the platform for over 3 years and I have noticed a very good evolution, in an attempt to reinvent themselves. The support team is amazing, always available to work out with us in achieving the best results. About the technology, the algorithms available in the platform suits most of the cases. Being language agnostic is a very positive point for us, because some big tech platforms have little support for PT-PT language. Incentivized Verified User Anonymous Read full review
Pros	No answers on this topic	Rasa team has Top notch AI knowledge Greate customer support, by listening towards the clients needs. And building future proof solutions around client Business Requirements within dazzling timeframes Incentivized JS Jeroen Spriet Owner/BA/FA/Dev/UX/UI Read full review
Cons	No answers on this topic	Rasa CALM flows and Rasa domain could be made fully independent of the Rasa training process and dynamically retrievable from e.g. a graph DB. This would make the chatbot more flexible. Prompt templates, or at least paths could be referenced in Rasa config. Different policies in the Rasa config could then be configured without code change to use different prompt templates LLM configuration should rather be part of the endpoints, than model configuration. Rasa Studio could support all the functionality of Rasa Pro. Incentivized Martin Lukan Senior Data Scientist Read full review
Usability	No answers on this topic	With the help of dedicated team - documentation and video resources it is relatively easier to build. We prioritized pro-code usage to begin with launch. Incentivized Verified User Anonymous Read full review
Support Rating	No answers on this topic	Rasa support has been very responsive, trying to fix any reported issues ASAP. They've also listened to many requests for improvement. The Rasa features and changelog are well documented Incentivized Martin Lukan Senior Data Scientist Read full review
Alternatives Considered	No answers on this topic	Glean - proprietary semantic search algorithms, no backend actions integration IBM Watsonx - complicated dialogue builder, poor separation of no-code and pro-code interfaces ELMOS (agent based) - all logic in code, no dialogue logic in no-code interface possible Rasa - transparent and simple sharing of objects between no-code and pro-code interfaces. Transparent LLM usage and restrictions. Simple backend integration via Rasa SDK Incentivized Martin Lukan Senior Data Scientist Read full review
Return on Investment	No answers on this topic	Reduced Human Connected Calls Per active User Improved Calls disposed by Voice Agent Reduced call wait times Incentivized Verified User Anonymous Read full review
ScreenShots	Optimizely Opal Screenshots	Rasa Screenshots