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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    GitHub

    Score9.2 out of 10
    N/AGitHub is a platform that hosts public and private code and provides software development and collaboration tools. Features include version control, issue tracking, code review, team management, syntax highlighting, etc. Personal plans ($0-50), Organizational plans ($0-200), and Enterprise plans are available.

    $4

    per month per user

    Optimizely Feature Experimentation

    Score8.4 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
    GitHubOptimizely Feature Experimentation
    Editions & Modules
    Team
    $40
    per year per user
    Enterprise
    $210
    per year per user
    No answers on this topic
    Offerings
    Pricing Offerings
    GitHubOptimizely Feature Experimentation
    Free Trial
    YesNo
    Free/Freemium Version
    YesYes
    Premium Consulting/Integration Services
    NoYes
    Entry-level Setup FeeNo setup feeRequired
    Additional Details——
    More Pricing Information
    Community Pulse
    GitHubOptimizely Feature Experimentation
    Considered Both Products
    GitHub
    No answer on this topic
    Optimizely
    No answer on this topic
    Key User Insights
    Would buy again
    96%
    Would buy again
    47 Answers
    89%
    Would buy again
    41 Answers
    Delivers good value for the price
    98%
    Delivers good value for the price
    46 Answers
    97%
    Delivers good value for the price
    33 Answers
    Happy with the feature set
    98%
    Happy with the feature set
    48 Answers
    91%
    Happy with the feature set
    42 Answers
    Lived up to sales and marketing promises
    95%
    Lived up to sales and marketing promises
    38 Answers
    88%
    Lived up to sales and marketing promises
    21 Answers
    Implementation went as expected
    100%
    Implementation went as expected
    42 Answers
    80%
    Implementation went as expected
    28 Answers
    Features
    GitHubOptimizely Feature Experimentation
    Version Control Software Features
    Comparison of Version Control Software Features features of GitHub and Optimizely Feature Experimentation
    Feature
    GitHub
    9.4
    10 Ratings
    7% above category average
    Optimizely Feature Experimentation
    -
    Ratings
    Branching and Merging9.710 Ratings00 Ratings
    Version History9.710 Ratings00 Ratings
    Version Control Collaboration Tools9.89 Ratings00 Ratings
    Pull Requests9.710 Ratings00 Ratings
    Code Review Tools8.89 Ratings00 Ratings
    Project Access Control9.210 Ratings00 Ratings
    Automated Testing Integration8.810 Ratings00 Ratings
    Issue Tracking Integration8.910 Ratings00 Ratings
    Branch Protection9.89 Ratings00 Ratings
    Best Alternatives
    GitHubOptimizely Feature Experimentation
    Small Businesses
    Git
    Score10 out of 10
    No answers on this topic
    Medium-sized Companies
    Git
    Score10 out of 10
    No answers on this topic
    Enterprises
    Perforce P4
    Score7.5 out of 10
    No answers on this topic
    All AlternativesView all alternativesView all alternatives
    User Ratings
    GitHubOptimizely Feature Experimentation
    Likelihood to Recommend
    9.9
    (131 ratings)
    8.2
    (47 ratings)
    Likelihood to Renew
    10.0
    (1 ratings)
    4.5
    (2 ratings)
    Usability
    9.4
    (10 ratings)
    7.6
    (26 ratings)
    Support Rating
    8.8
    (26 ratings)
    3.6
    (1 ratings)
    Implementation Rating
    -
    (0 ratings)
    10.0
    (1 ratings)
    Product Scalability
    -
    (0 ratings)
    5.0
    (1 ratings)
    User Testimonials
    GitHubOptimizely Feature Experimentation
    Likelihood to Recommend
    GitHub
    GitHub is an easy to go tool when it comes to Version Controlling, CI/CD workflows, Integration with third party softwares. It's effective for any level of CI/CD implementation you would like to. Also the the cost of product is also very competitive and affordable. As of now GitHub lacks capabilities when it comes to detailed project management in comparison to tools like Jira, but overall its value for money.
    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
    Read full review
    Pros
    GitHub
    • Version control: GitHub provides a powerful and flexible Git-based version control system that allows teams to track changes to their code over time, collaborate on code with others, and maintain a history of their work.
    • Code review: GitHub's pull request system enables teams to review code changes, discuss suggestions and merge changes in a central location. This makes it easier to catch bugs and ensure that code quality remains high.
    • Collaboration: GitHub provides a variety of collaboration tools to help teams work together effectively, including issue tracking, project management, and wikis.
    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
    GitHub
    • Not an easy tool for beginners. Prior command-line experience is expected to get started with GitHub efficiently.
    • Unlike other source control platforms GitHub is a little confusing. With no proper GUI tool its hard to understand the source code version/history.
    • Working with larger files can be tricky. For file sizes above 100MB, GitHub expects the developer to use different commands (lfs).
    • While using the web version of GitHub, it has some restrictions on the number of files that can be uploaded at once. Recommended action is to use the command-line utility to add and push files into the repository.
    Read full review
    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
    Read full review
    Likelihood to Renew
    GitHub
    GitHub's ease of use and continued investment into the Developer Experience have made it the de facto tool for our engineers to manage software changes. With new features that continue to come out, we have been able to consolidate several other SaaS solutions and reduce the number of tools required for each engineer to perform their job responsibilities.
    Read full review
    Optimizely
    Competitive landscape
    Incentivized
    Read full review
    Usability
    GitHub
    GitHub is a clean and modern interface. The underlying integrations make it smooth to couple tasks, projects, pull requests and other business functions together. The insights and reporting is really strong and is getting better with every release. GitHub's PR tooling is strong for being web based, i do believe a better code editor would rival having to pull merge conflicts into local IDE.
    Incentivized
    Read full review
    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
    Read full review
    Support Rating
    GitHub
    There are a ton of resources and tutorials for GitHub online. The sheer number of people who use GitHub ensures that someone has the exact answer you are looking for. The docs on GitHub itself are very thorough as well. You will often find an official doc along with the hundreds of independent tutorials that answers your question, which is unusual for most online services.
    Incentivized
    Read full review
    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
    GitHub
    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
    GitHub
    While I don't have very much experience with these 2 solutions, they're two of the most popular alternatives to GitHub. Bitbucket is from Atlassian, which may make sense for a team that is already using other Atlassian tools like Jira, Confluence, and Trello, as their integration will likely be much tighter. Gitlab on the other hand has a reputation as a very capable GitHub replacement with some features that are not available on GitHub like firewall tools.
    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
    Read full review
    Scalability
    GitHub
    No answers on this topic
    Optimizely
    had troubles with performance for SSR and the React SDK
    Incentivized
    Read full review
    Return on Investment
    GitHub
    • Team collaboration significantly improved as everything is clearly logged and maintained.
    • Maintaining a good overview of items will be delivered wrt the roadmap for example.
    • Knowledge management and tracking. Over time a lot of tickets, issues and comments are logged. GitHub is a great asset to go back and review why x was y.
    Incentivized
    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.
    Incentivized
    Read full review
    ScreenShots

    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