GitLab vs. Optimizely Feature Experimentation

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
GitLab
Score 8.7 out of 10
N/A
GitLab 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
Score 8.7 out of 10
N/A
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.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
NoNo
Entry-level Setup FeeOptionalRequired
Additional Details
More Pricing Information
Community Pulse
GitLabOptimizely Feature Experimentation
Best Alternatives
GitLabOptimizely Feature Experimentation
Small Businesses

No answers on this topic

GitLab
GitLab
Score 8.7 out of 10
Medium-sized Companies
Veracode
Veracode
Score 8.7 out of 10
GitLab
GitLab
Score 8.7 out of 10
Enterprises
Veracode
Veracode
Score 8.7 out of 10
GitLab
GitLab
Score 8.7 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
GitLabOptimizely Feature Experimentation
Likelihood to Recommend
8.8
(0 ratings)
8.9
(0 ratings)
Likelihood to Renew
9.9
(0 ratings)
4.5
(0 ratings)
Usability
9.0
(0 ratings)
7.3
(0 ratings)
Support Rating
9.1
(0 ratings)
-
(0 ratings)
Implementation Rating
-
(0 ratings)
10.0
(0 ratings)
Product Scalability
-
(0 ratings)
5.0
(0 ratings)
User Testimonials
GitLabOptimizely Feature Experimentation
Likelihood to Recommend
It is well-suited for any project that needs VCS. It's an excellent choice for teams that might be remote or have to collaborate across teams. Plenty of features allow for async working. With its dashboards and reporting features, it is also suitable for nontechnical PMs or stakeholders. It allows for very bespoke customization and can most often do much more than you need it to.
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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 -
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Pros
  • 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.
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  • Splitting traffic between variants and enabling you to scale up or down the amount of traffic in each one
  • Giving a standardised report that you can share with a huge number of users
  • Showing a large variety of results/metrics you can then dive into
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Cons
  • 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.
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  • Difficult integration if your data is not front end
  • Costly MAU model needs to be based on experiments not on site visits
  • It's not easy to understand how to build an Experiment
  • Onboarding team is more focused on punching through their slides and not focused on your needs or understanding.
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Likelihood to Renew
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
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Competitive landscape
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Usability
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!
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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
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Reliability and Availability
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
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No answers on this topic
Performance
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
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No answers on this topic
Support Rating
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
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Support was there but it was pretty slow at most times. Only after escalation was support really given to our teams
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Implementation Rating
No answers on this topic
It’s straightforward. Docs are well written and I believe there must be a support. But we haven’t used it
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Alternatives Considered
GitHub is an inferior product from most points of view. We had to use it and the teams finds no positives about it. Everything is a downgrade from our previous GitLab solution. GitLab CI\CD is vastly superior to workflows, for example doing a manual node is just "when : manual" in GitLab while you have to do clickops in GitHub to achieve the same. No overview of code in branches is a minus when we tried to figure out what our colleagues are trying to merge as it looked off.
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In previous companies I've used Monetate which is a similar A/B testing kind of feature experimentation engine that is very similar from my memory, but again, back to the point of these new features of the analytics engine and Opal, it kind of cuts it above Monetate from my experience. Obviously Monetate may have improved since when I lost use it, but from what I can see, yeah.
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Scalability
I think is very well designed, and like any VCS it works as intended
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had troubles with performance for SSR and the React SDK
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Return on Investment
  • 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
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  • 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.
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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