Digital.ai Release, formerly XebiaLabs XL Release, is a release management tool designed for enterprises that enables users to control and track releases, standardize processes, and bake compliance and security into software release pipelines. As a release orchestration tool, Digital.ai Release works specifically for continuous delivery, and enables teams across an organization to model and monitor releases, automate tasks within IT infrastructure, in order to cut release times and improve…
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Optimizely Feature Experimentation
Score8.4 out of 10
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Optimizely Feature Experimentation unites feature flagging, A/B testing, and built-in collaboration—so marketers can release, experiment, and optimize with confidence in one platform.
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Pricing
Digital.ai Release
Optimizely Feature Experimentation
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Digital.ai Release
Optimizely Feature Experimentation
Free Trial
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Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
Yes
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No setup fee
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Community Pulse
Digital.ai Release
Optimizely Feature Experimentation
Considered Both Products
Digital.ai (formerly XebiaLabs, CollabNet VersionOne, and Arxan)
Digital.ai (formerly XebiaLabs, CollabNet VersionOne, and Arxan)
Mainly used in release management where all deployments are well managed and processed further based on the approval system. Complete enterprise-level solution with minor difficulties which need to be added to product improvement features. Integration with other CI-CD tools makes it easier to perform tasks in terms of release and deployments.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Based on my experience with Optimizely Feature Experimentation, I can highlight several scenarios where it excels and a few where it may be less suitable. Well-suited scenarios: - Multi-Channel product launches - Complex A/B testing and feature flag management - Gradual rollout and risk mitigation Less suited scenarios: - Simple A/B tests (their Web Experimentation product is probably better for that) - Non-technical team usage -
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
It is easy to use any of our product owners, marketers, developers can set up experiments and roll them out with some developer support. So the key thing there is this front end UI easy to use and maybe this will come later, but the new features such as Opal and the analytics or database centric engine is something we're interested in as well.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Would be nice to able to switch variants between say an MVT to a 50:50 if one of the variants is not performing very well quickly and effectively so can still use the standardised report
Interface can feel very bare bones/not very many graphs or visuals, which other providers have to make it a bit more engaging
Doesn't show easily what each variant that is live looks like, so can be hard to remember what is actually being shown in each test
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Digital.ai (formerly XebiaLabs, CollabNet VersionOne, and Arxan)
The tool is easy to use, easy to navigate and learn. Manages releases with proper approvals in a systematic manner. Though it needs minor improvements in terms of pagination (data loading), access management, but, overall the tool helps in increasing productivity and less time for production deployments.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Easy to navigate the UI. Once you know how to use it, it is very easy to run experiments. And when the experiment is setup, the SDK code variables are generated and available for developers to use immediately so they can quickly build the experiment code
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
Digital.ai (formerly XebiaLabs, CollabNet VersionOne, and Arxan)
Support is not good at all. To this day, I have to mail my queries and their support site does not log in for me (me alone). But, upon contacting many times, no one helps with a proper response. Though good thing is, I get a proper response over mail too. But, being informative about the tool and not on the issues faced by users outside of the process to get support should also be addressed equally. Which is currently missing in support.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Digital.ai (formerly XebiaLabs, CollabNet VersionOne, and Arxan)
XL release is simpler to configure and deploy to the organization than other change management platforms I have used. That simplicity has minor drawbacks requiring you to fit into a limited set of control methods but that exercise helped us simplify a needlessly onerous process.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
When Google Optimize goes off we searched for a tool where you can be sure to get a good GA4 implementation and easy to use for IT team and product team. Optimizely Feature Experimentation seems to have a good balance between pricing and capabilities. If you are searching for an experimentation tool and personalization all in one... then maybe these comparison change and Optimizely turns to expensive. In the same way... if you want a server side solution. For us, it will be a challenge in the following years
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
We have improved various metrics throughout the course of our experimentation program with Optimizely and therefore sharing numbers is tricky. Essentially we only implement versions of the product that perform the best in terms of CVR, revenue/visitor, ATV, average order value, average basket size and so forth dependent on the north star we are trying to move with each release.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info