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.
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Pricing
GitLab
Optimizely 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
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GitLab Ultimate (self-managed)
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Offerings
Pricing Offerings
GitLab
Optimizely Feature Experimentation
Free Trial
Yes
No
Free/Freemium Version
Yes
Yes
Premium Consulting/Integration Services
Yes
Yes
Entry-level Setup Fee
Optional
Required
Additional Details
GitLab 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.
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.
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 -
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.
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
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
- 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.
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
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
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
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
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.
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
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.