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Digital.ai Release vs. Optimizely Feature Experimentation

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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Digital.ai Release

    Score8.7 out of 10
    N/ADigital.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…N/A

    Optimizely Feature Experimentation

    Score8.4 out of 10
    N/AOptimizely Feature Experimentation unites feature flagging, A/B testing, and built-in collaboration—so marketers can release, experiment, and optimize with confidence in one platform.N/A
    Pricing
    Digital.ai ReleaseOptimizely Feature Experimentation
    Editions & Modules
    No answers on this topic
    No answers on this topic
    Offerings
    Pricing Offerings
    Digital.ai ReleaseOptimizely Feature Experimentation
    Free Trial
    YesNo
    Free/Freemium Version
    NoYes
    Premium Consulting/Integration Services
    NoYes
    Entry-level Setup FeeNo setup feeRequired
    Additional Details——
    More Pricing Information
    Community Pulse
    Digital.ai ReleaseOptimizely Feature Experimentation
    Considered Both Products
    Digital.ai (formerly XebiaLabs, CollabNet VersionOne, and Arxan)
    No answer on this topic
    Optimizely
    No answer on this topic
    Key User Insights
    Would buy again
    No answers on this topic
    89%
    Would buy again
    41 Answers
    Delivers good value for the price
    No answers on this topic
    97%
    Delivers good value for the price
    33 Answers
    Happy with the feature set
    No answers on this topic
    91%
    Happy with the feature set
    42 Answers
    Lived up to sales and marketing promises
    No answers on this topic
    88%
    Lived up to sales and marketing promises
    21 Answers
    Implementation went as expected
    No answers on this topic
    80%
    Implementation went as expected
    28 Answers
    User Ratings
    Digital.ai ReleaseOptimizely Feature Experimentation
    Likelihood to Recommend
    9.0
    (2 ratings)
    8.2
    (47 ratings)
    Likelihood to Renew
    -
    (0 ratings)
    4.5
    (2 ratings)
    Usability
    9.0
    (1 ratings)
    7.6
    (26 ratings)
    Support Rating
    4.0
    (1 ratings)
    3.6
    (1 ratings)
    Implementation Rating
    -
    (0 ratings)
    10.0
    (1 ratings)
    Product Scalability
    -
    (0 ratings)
    5.0
    (1 ratings)
    User Testimonials
    Digital.ai ReleaseOptimizely Feature Experimentation
    Likelihood to Recommend
    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.
    Incentivized
    Read full review
    Optimizely
    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 -
    Incentivized
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    Pros
    Digital.ai (formerly XebiaLabs, CollabNet VersionOne, and Arxan)
    • Cross-team release workflow control using email, texts, scripts allow our release management to be truly a 360 process.
    • XL Release allowing our Jenkins toolchain to control the beginning of release trains which is very powerful.
    • XL release allows us to expose the business process flow for anyone to read direct at the source which runs the process instead of a separate vision.
    Incentivized
    Read full review
    Optimizely
    • 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.
    Incentivized
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    Cons
    Digital.ai (formerly XebiaLabs, CollabNet VersionOne, and Arxan)
    • Pagination of data - across tool.
    • User Roles Management API can be improved.
    • Case insensitive ID's are treated differently making user face login and access issues.
    • Dependency on Universal template/custom plugins creation should be reduced.
    • Code versioning of templates is very difficult.
    • Better error handling.
    • Futures Timeout Issues.
    Incentivized
    Read full review
    Optimizely
    • 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
    Incentivized
    Read full review
    Likelihood to Renew
    Digital.ai (formerly XebiaLabs, CollabNet VersionOne, and Arxan)
    No answers on this topic
    Optimizely
    Competitive landscape
    Incentivized
    Read full review
    Usability
    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.
    Incentivized
    Read full review
    Optimizely
    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
    Incentivized
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    Support Rating
    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.
    Incentivized
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    Optimizely
    Support was there but it was pretty slow at most times. Only after escalation was support really given to our teams
    Incentivized
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    Implementation Rating
    Digital.ai (formerly XebiaLabs, CollabNet VersionOne, and Arxan)
    No answers on this topic
    Optimizely
    It’s straightforward. Docs are well written and I believe there must be a support. But we haven’t used it
    Incentivized
    Read full review
    Alternatives Considered
    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.
    Incentivized
    Read full review
    Optimizely
    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
    Incentivized
    Read full review
    Scalability
    Digital.ai (formerly XebiaLabs, CollabNet VersionOne, and Arxan)
    No answers on this topic
    Optimizely
    had troubles with performance for SSR and the React SDK
    Incentivized
    Read full review
    Return on Investment
    Digital.ai (formerly XebiaLabs, CollabNet VersionOne, and Arxan)
    • XL release has improved our consistency of release process, removing multiple days worth of manual status checking and coordination.
    • XL release has allowed us to increase the number of beta releases we can support due to simplifying our release actions.
    Incentivized
    Read full review
    Optimizely
    • 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.
    Incentivized
    Read full review
    ScreenShots

    Optimizely Feature Experimentation Screenshots

    Screenshot of Feature Flag Setup. Here users can run A/B and multi-armed bandit tests, as well as:

- Set up a single feature flag to test multiple variations and experiment types
- Enable targeted deliveries and rollouts for more precise experimentation
- Roll back changes quickly when needed to ensure experiment accuracy and reduce risks
- Increase testing flexibility with control over experiment types and delivery methodsScreenshot of Audience Setup. This is used to target specific user segments for personalized experiments, and:

- Create and customize audiences based on user attributes
- Refine audience segments to ensure the right users are included in tests
- Enhance experiment relevance by setting specific conditions for user groupsScreenshot of Experiment Results, supporting the analysis and optimization of experimentation outcomes. Viewers can also:

- examine detailed experiment results, including key metrics like conversion rates and statistical significance
- Compare variations side-by-side to identify winning treatments
- Use advanced filters to segment and drill down into specific audience or test dataScreenshot of A Program Overview. These offer insights into any experimentation program’s performance. It also offers:

- A comprehensive view of the entire experimentation program’s status and progress
- Monitoring for key performance metrics like test velocity, success rates, and overall impact
- Evaluation of the impact of experiments with easy-to-read visualizations and reporting tools
- Performance tracking of experiments over time to guide decision-making and optimize strategiesScreenshot of AI Variable Suggestions. These enhance experimentation with AI-driven insights, and can also help with:

- Generating multiple content variations with AI to speed up experiment design
- Improving test quality with content suggestions
- Increasing experimentation velocity and achieving better outcomes with AI-powered optimizationScreenshot of Schedule Changes, to streamline experimentation. Users can also:

- Set specific times to toggle flags or rules on/off, ensuring precise control
- Schedule traffic allocation percentages for smooth experiment rollouts
- Increase test velocity and confidence by automating progressive changes