Feature Flags vs. Optimizely Feature Experimentation

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Feature Flags
Score 6.0 out of 10
N/A
Feature Flags is an open source feature management tool for Ember.js.N/A
Optimizely Feature Experimentation
Score 8.2 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
Feature FlagsOptimizely Feature Experimentation
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Feature FlagsOptimizely Feature Experimentation
Free Trial
NoNo
Free/Freemium Version
NoYes
Premium Consulting/Integration Services
NoYes
Entry-level Setup FeeNo setup feeRequired
Additional Details
More Pricing Information
Community Pulse
Feature FlagsOptimizely Feature Experimentation
Best Alternatives
Feature FlagsOptimizely Feature Experimentation
Small Businesses
GitLab
GitLab
Score 8.6 out of 10
GitLab
GitLab
Score 8.6 out of 10
Medium-sized Companies
GitLab
GitLab
Score 8.6 out of 10
GitLab
GitLab
Score 8.6 out of 10
Enterprises
GitLab
GitLab
Score 8.6 out of 10
GitLab
GitLab
Score 8.6 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Feature FlagsOptimizely Feature Experimentation
Likelihood to Recommend
-
(0 ratings)
8.3
(48 ratings)
Likelihood to Renew
-
(0 ratings)
4.5
(2 ratings)
Usability
-
(0 ratings)
7.7
(27 ratings)
Support Rating
-
(0 ratings)
3.6
(1 ratings)
Implementation Rating
-
(0 ratings)
10.0
(1 ratings)
Product Scalability
-
(0 ratings)
5.0
(1 ratings)
User Testimonials
Feature FlagsOptimizely Feature Experimentation
Likelihood to Recommend
Open Source
No answers on this topic
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
Open Source
No answers on this topic
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
Open Source
No answers on this topic
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
Open Source
No answers on this topic
Optimizely
Competitive landscape
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Usability
Open Source
No answers on this topic
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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Support Rating
Open Source
No answers on this topic
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
Open Source
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
Open Source
No answers on this topic
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
Open Source
No answers on this topic
Optimizely
had troubles with performance for SSR and the React SDK
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Return on Investment
Open Source
No answers on this topic
Optimizely
  • 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

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