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
Considered Both Products
GitLab
Chose GitLab
As mentioned earlier, the features like chart visualization sets it apart from the others. Other than that, GitLab is open source while other are not and comparatively more secure that its other counterparts. Also, GitLab supports adding other types of attachments which is not …
Chose GitLab
When i was using the other platform, Some time i face down time, But GitLabs its not happening for single time. GitLab is having easy user interference as compared to other platforms. Pull Request, Code review, Issue tracking, Merging, Access control and User Roles is having a …
Chose GitLab
GitLab is miles ahead of the competition. In so many words, having a simple UI with robust security and the ability to conduct Git actions takes the cake. The competitions like to say they can do these things easily but their products are more confusing and hard to use.
Chose GitLab
GitLab allows a self-hosted version that is easy to setup and configure. It is also open-source as compared to GitHub. The integrated CI/CD tools is a plus, since we do not have to worry about those tools, unlike Github. The All-in-one solution of GitLab made sense for our …
Chose GitLab
Gitlab offers the best support for CI/CD pipelines and the highest degree of customisation for workflows, permissions, and integrations. The integration of BitBucket with JIRA is better than GitLab but CI/CD features are limited in comparison. GitLab's built-in Container …
Chose GitLab
Gitlab provides basic functionality like any other git tool. Some features of push and pull requests , clone and merge is handled equally well. It lacks in AI features which are there in GitHub and setup processes are difficult. Cost difference is the only concern while …
Chose 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" …
Chose GitLab
It's much simpler than the competitors. The one important feature Gitlab stand out is the CI/CD pipeline. GitHub required integration with external CI tools but Gitlab has this feature built-in. Compare to Jenkins and Teamcity, It's easy to use without any additional Plugins. …
Chose 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 …
Chose GitLab
I tried Github in the past, and it was really similar as GitLab. But we prefer use GitLab for the cicd part, easier to handle
Chose GitLab
i have more exoerence in GitLab rather than bitbucket . As personally , it is good for me to understand how things is going on. i have used personally and also in organisation . It is great for developer to see there 3 months ago code and also can come up with new solution to …
Chose GitLab
Because with Visual Studio code, it was very easy for us to install GitLab in it and have easy access through the terminal and due to GitLab, it was easy to implement codes regarding API and AWS services to make our software better. Gitlens and all features help to check the …
Chose GitLab
GitLab provides a far superior platform due to it's great integration and CI/CD focus. And while in the beginning the UI might look a bit overwhelming with use you will find it way more useful than it's competitors. The variables and settings also make way more sense and it's …
Chose GitLab
My feedback may not be important here because when I joined the company they already had GitLab and we still use it due to the ability to do CI/CD Integration, deployments, debugging, code owners approval, and Jira integration. So far we have not had any major blocker that has …
Chose GitLab
Github is more open-source first and enterprise-first second in their approach. The reverse is true for GitLab. Both are exceptional products and it highly depends on the specific needs of an org
Chose GitLab
Used Jenkins to connect with GitLab for building and deployment of microservices. GitLab helps in easy integration.
Chose GitLab
GitLab has a open-source community and great documentation that provides support resources and community contributions. AWS CodeCommit is used for integration with other AWS services in the AWS ecosystem and also have a low community and support compared to GitLab hence …
Chose GitLab
It was a management decision to use GitLab over other tools. It integrates well with RBAC using Terraform. Runners are easy to setup. Almost all the features the organization used before are available in GitLab.
Chose GitLab
For small projects or companies that do work on a few only code repositories selecting one of the git code hosting services like GitLab, GitHub, Bitbicket etc does not make a big difference.

But, if you are on a code development company that handles too many repositories and …
Chose GitLab
Software delivery is the key objective and GitLab made it much easier to hit the group quickly. It worked well with automation, and integrations with other SDLC tools used in the Organization and it is really easy to use. It's widely adopted and has the power to deliver what we …
Chose GitLab
1. Easier to use and setup then any other product.
2. CI/CD is also easier in GitLab
Chose GitLab
GitLab's online IDE and code modification is much better compared to Stash. While pricier than Bitbucket, Gitlab also provides CI CD configuration better than BB.
Chose GitLab
GitLab is rich in features and access control if compared with GitHub also GitHub stands no where in CI/CD if compared with GitLab.
GitLab CI/CD is mature, robust and far better than GitHub actions in all means.
If I talk about quality of support and reponse time of GitLab it is …
Optimizely Feature Experimentation
Chose Optimizely Feature Experimentation
I wasn’t part of the team that selected Optimizely, but its integrations with other tools were a big plus for us in making our decision. It was more expensive, however.
Chose Optimizely Feature Experimentation
We have not used any other similar tools, we evaluated both Kameleoon and VWO. With the combination of price, features, and expandability, we moved forward with Optimizely Feature Experimentation.
Chose Optimizely Feature Experimentation
Google optimize is great that it is an add on to an existing Analytics implementation, but only has a web version. Optimizely has the SDK so better option for testing new features
Chose Optimizely Feature Experimentation
We selected Optimizely as it was easy to use/understand, had clearly defined SLAs for keeping the platform up and was regarded as resilient within the industry. We needed something at our point in our experimentation journey that could be used for Product testing at scale and …
Chose Optimizely Feature Experimentation
Optimizely Feature Experimentation has similar features to Amplitude. As a matter of fact it looks like Amplitude copied Optimizely. However, Amplitude did not mimic the nomenclature issues.
Chose Optimizely Feature Experimentation
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.
Chose Optimizely Feature Experimentation
In other companies, all of the feature flag controls were done locally and it got messy after a while. There was no much control on who was doing what. With Optimizely Feature Experimentation, it is clear what feature flags are enabled and which ones are not. It is easier to …
Chose Optimizely Feature Experimentation
not too much experience on that to answer this question
Chose Optimizely Feature Experimentation
There is a lot more flexibility with Optimizely once you have customized the implementation and better tools.
Chose Optimizely Feature Experimentation
WebX and FeatureX work well in pair, they organically complement each other
Chose Optimizely Feature Experimentation
Simple interface and ability to create audiences and assign them to experiments.
Chose Optimizely Feature Experimentation
Optimizely offered both web experimentation (WSYWIG editor for nontechnical marketing folks) and Feature Experimentation. That made the decision easier to get max value across different stakeholder groups.
Chose Optimizely Feature Experimentation
Optimizely FX is the only tool I've used that specifically allows for testing in the back-end. Most front end tools are great for simple tests, but there comes a time when you need to go a level deeper and that's not possible with front-end tools.
Chose Optimizely Feature Experimentation
Mixpanel, Google Analytics, Hotjar and A/B Smartly
Chose Optimizely Feature Experimentation
I prefer Optimizely Feature Experimentation to web experimentation. I think it's more straightforward to set up and as an engineer, I like being able to have more control from the code side.
Chose Optimizely Feature Experimentation
we wanted a shift with the tool that helps us with managing our data
Chose Optimizely Feature Experimentation
We haven't evaluated other products. We have an in-house product that is missing a lot of features and is very behind from making the test process easier.

Instead of evolving our in-house product with limited resources, we decided to go with Optimizely Feature Experimentation …
Chose Optimizely Feature Experimentation
Overall, Optimizely Feature Experimentation is an industry leader in terms of experimentation across web and mobile. For apps I would say amplitude does slightly a better job as it is tailored to that niche.
Chose Optimizely Feature Experimentation
Optimizely Feature Experimentation is better for building more complex experiments than Optimizely Web. However, Optimizely Web is much easier to kickstart your experimentation program with as the learning curve is much lower, and dedicated developer resources are not always …
Chose Optimizely Feature Experimentation
Optimizely Feature Experimentation is less of a point solution than LaunchDarkly, so LD has a few extra features, but Optimizely offers a much greater solution for experimentation, personalization etc.
Chose Optimizely Feature Experimentation
Feature experimentation is much more robust and allows more granular control over the decisions you want to make. While Optimizely Feature Experimentation is nice and can be delivered via Optimizely Feature Experimentation's UI, its still not ideal because its brittle and can …
Chose Optimizely Feature Experimentation
Google Tag Manager was less flexible for the business and required the Google Analytics tool for analysis and metric tracking. Optimizely allows the building of use cases. Optimizely provides real-time data and metrics that are easier to use. GTM provides tracking …
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
8.9
(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.
Read full review
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