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GitLab vs. Optimizely Feature Experimentation

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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    GitLab

    Score8.7 out of 10
    N/AGitLab DevSecOps platform enables software innovation by aiming to empower development, security, and operations teams to build better software, faster. With GitLab, teams can create, deliver, and manage code quickly and continuously instead of managing disparate tools and scripts. GitLab helps teams across the complete DevSecOps lifecycle, from developing, securing, and deploying software. Differentiators, as described by Gitlab: Simplicity: With GitLab, DevSecOps can…

    $0

    per month per user

    Optimizely Feature Experimentation

    Score8.8 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
    GitLabOptimizely Feature Experimentation
    Editions & Modules
    GitLab Essential
    $0
    per month per user
    GitLab Premium
    $29
    per month per user
    GitLab Ultimate
    $99
    per month per user
    No answers on this topic
    Offerings
    Pricing Offerings
    GitLabOptimizely Feature Experimentation
    Free Trial
    YesNo
    Free/Freemium Version
    YesYes
    Premium Consulting/Integration Services
    YesYes
    Entry-level Setup FeeOptionalRequired
    Additional Details——
    More Pricing Information
    Community Pulse
    GitLabOptimizely Feature Experimentation
    Considered Both Products
    GitLab
    No answer on this topic
    Optimizely
    No answer on this topic
    Key User Insights
    Would buy again
    98%
    Would buy again
    125 Answers
    88%
    Would buy again
    37 Answers
    Delivers good value for the price
    100%
    Delivers good value for the price
    114 Answers
    97%
    Delivers good value for the price
    30 Answers
    Happy with the feature set
    98%
    Happy with the feature set
    126 Answers
    93%
    Happy with the feature set
    39 Answers
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    81 Answers
    90%
    Lived up to sales and marketing promises
    19 Answers
    Implementation went as expected
    97%
    Implementation went as expected
    101 Answers
    84%
    Implementation went as expected
    27 Answers
    User Ratings
    GitLabOptimizely Feature Experimentation
    Likelihood to Recommend
    8.9
    (142 ratings)
    8.9
    (29 ratings)
    Likelihood to Renew
    9.9
    (4 ratings)
    4.5
    (2 ratings)
    Usability
    9.0
    (5 ratings)
    7.3
    (8 ratings)
    Support Rating
    9.1
    (11 ratings)
    -
    (0 ratings)
    Implementation Rating
    -
    (0 ratings)
    10.0
    (1 ratings)
    Product Scalability
    -
    (0 ratings)
    5.0
    (1 ratings)
    User Testimonials
    GitLabOptimizely Feature Experimentation
    Likelihood to Recommend
    GitLab
    GitLab is good if you work a lot with code and do complex repository actions. It gives you a very good overview of what were the states of your branches and the files in them at different stages in time. It's also way easier and more efficient to write pipelines for CI\CD. It's easier to read and it's easier to write them. It takes fewer clicks to achieve the same things with GitLab than it does for competitor products.
    Incentivized
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    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
    GitLab
    • GitLab excels in managing code versions, allowing easy tracking of changes, branch management, and merging contributions.
    • It helps maintain code stability and reliability, saving time and effort in the development or research workflow.
    • Powerful code review features, enabling collaboration and feedback among team members.
    • Robust project management features, including issue tracking, kanban boards, and milestones.
    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
    Read full review
    Cons
    GitLab
    • CI variables management is sometimes hard to use, for example, with File type variables. The scope of each variable is also hard to guess.
    • Access Token: there are too many types (Personal, Project, global..), and it is hard to identify the scope and where it comes from once created.
    • Runners: auto-scaled runners are for the moment hard to put in place, and monitoring is not easy.
    Incentivized
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    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
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    Likelihood to Renew
    GitLab
    I really feel the platform has matured quite faster than others, and it is always at the top of its game compared to the different vendors like GitHub, Azure pipelines, CircleCI, Travis, Jenkins. Since it provides, agents, CI/CD, repository hosting, Secrets management, user management, and Single Sign on; among other features
    Incentivized
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    Optimizely
    Competitive landscape
    Incentivized
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    Usability
    GitLab
    I find it easy to use, I haven't had to do the integration work, so that's why it is a 9/10, cause I can't speak to how easy that part was or the initial set up, but day to day use is great!
    Incentivized
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    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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    Reliability and Availability
    GitLab
    I've never had experienced outages from GItlab itself, but regarding the code I have deployed to Gitlab, the history helps a lot to trace the cause of the issue or performing a rollback to go back to a working version
    Incentivized
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    Optimizely
    No answers on this topic
    Performance
    GitLab
    GItlab reponsiveness is amazing, has never left me IDLE. I've never had issues even with complex projects. I have not experienced any issues when integrating it with agents for example or SSO
    Incentivized
    Read full review
    Optimizely
    No answers on this topic
    Support Rating
    GitLab
    At this point, I do not have much experience with Gitlab support as I have never had to engage them. They have documentation that is helpful, not quite as extensive as other documentation, but helpful nonetheless. They also seem to be relatively responsive on social media platforms (twitter) and really thrived when GitHub was acquired by Microsoft
    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
    Read full review
    Implementation Rating
    GitLab
    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
    GitLab
    Gitlab seems more cutting-edge than GitHub; however, its AI tools are not yet as mature as those of CoPilot. It feels like the next-generation product, so as we selected a tool for our startup, we decided to invest in the disruptor in the space. While there are fewer out-of-the-box templates for Gitlab, we have never discovered a lack of feature parity.
    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
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    Scalability
    GitLab
    I think is very well designed, and like any VCS it works as intended
    Incentivized
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    Optimizely
    had troubles with performance for SSR and the React SDK
    Incentivized
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    Return on Investment
    GitLab
    • GitLab cut down our spent on container, package and infrastructure registry
    • Best thing is we can now have everything in single platform which cost effective too
    • Quality of support is really good and they do have emergency support team as well which is great
    Incentivized
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    Optimizely
    • We have a huge, noteworthy ROI case study of how we did a SaaS onboarding revamp early this year. Our A/B test on a guided setup flow improved activation rates by 20 percent, which translated to over $1.2m in retained ARR.
    Incentivized
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    ScreenShots

    GitLab Screenshots

    Screenshot of GitLab, a comprehensive DevSecOps platform.Screenshot of Security DashboardScreenshot of Merge Request

    Optimizely Feature Experimentation Screenshots

    Screenshot of Feature Flag Setup. Here users can run flexible 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