GitLab vs. Optimizely Feature Experimentation

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
GitLab
Score 8.9 out of 10
N/A
GitLab is an intelligent orchestration platform for DevSecOps, where software teams enable AI at every stage of the software lifecycle to ship faster. The platform enables teams to automate repetitive tasks across planning, building, securing, testing, deploying, and maintaining software.
$0
per month per user
Optimizely Feature Experimentation
Score 8.4 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 Free (self-managed)
$0
GitLab Free
$0
GitLab Premium
$29
per month per user
GitLab Premium (self-managed)
$29
per month per user
GitLab Ultimate
Contact Sales
GitLab Ultimate (self-managed)
Contact Sales
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 DetailsGitLab Credits enable flexible, consumption-based access to agentic AI capabilities in the GitLab platform, allowing you to scale AI adoption at your own pace while maintaining cost predictability. Powered by Duo Agent Platform, GitLab’s agentic AI capabilities help software teams to collaborate at AI speed, without compromising quality and enterprise security. If usage exceeds monthly allocations and overage terms are accepted, automated on-demand billing activates without service interruption, so your developers never lose access to AI capabilities they need. Real-time dashboards provide transparency into AI consumption patterns. Software teams can see usage across users, projects, and groups with granular attribution for cost allocation. Automated threshold alerts facilitate proactive planning. Advanced analytics deliver trending, forecasting, and FinOps integration.
More Pricing Information
Community Pulse
GitLabOptimizely Feature Experimentation
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User Ratings
GitLabOptimizely Feature Experimentation
Likelihood to Recommend
8.6
(168 ratings)
8.2
(47 ratings)
Likelihood to Renew
9.0
(6 ratings)
4.5
(2 ratings)
Usability
8.8
(20 ratings)
7.6
(26 ratings)
Performance
9.0
(1 ratings)
-
(0 ratings)
Support Rating
10.0
(12 ratings)
3.6
(1 ratings)
Implementation Rating
-
(0 ratings)
10.0
(1 ratings)
Product Scalability
10.0
(1 ratings)
5.0
(1 ratings)
User Testimonials
GitLabOptimizely Feature Experimentation
Likelihood to Recommend
GitLab
In general it's a good product with a huge number of features. It can do CI/CD checks, deployment, repo hosting, integrations etc. A lot of it can be managed from just the UI but also has an ok API. It does tend to release new features semi-often too which is nice. It can have a steep learning curve for certain features so not a starter-friendly vendor, but great for most businesses.
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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 -
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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.
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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.
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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.
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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
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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
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Optimizely
Competitive landscape
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Usability
GitLab
- As a combination of features and products, GitLab is a really great tool for developers. - Also, it comes with both light and quick changes in a web browser version, as well as pro work in VS Code mode. However, in certain areas, such as large-scale issues/incidents, management can be improved.
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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
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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
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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
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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
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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
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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
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Alternatives Considered
GitLab
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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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
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Scalability
GitLab
I think is very well designed, and like any VCS it works as intended
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Optimizely
had troubles with performance for SSR and the React SDK
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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
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.
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ScreenShots

GitLab Screenshots

Screenshot of What is Intelligent Orchestration for DevSecOps?Screenshot of an overview of GitLab Duo Agent PlatformScreenshot of a new agent creation screen

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