Django Waffle vs. Optimizely Feature Experimentation

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Django Waffle
Score 9.7 out of 10
N/A
Django Waffle is an open source feature management tool for Django.N/A
Optimizely Feature Experimentation
Score 7.5 out of 10
N/A
Optimizely Feature Experimentation combines experimentation, feature flagging and built for purpose collaboration features into one platform.N/A
Pricing
Django WaffleOptimizely Feature Experimentation
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Django WaffleOptimizely 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
Django WaffleOptimizely Feature Experimentation
Top Pros

No answers on this topic

Top Cons

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Best Alternatives
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All AlternativesView all alternativesView all alternatives
User Ratings
Django WaffleOptimizely Feature Experimentation
Likelihood to Recommend
10.0
(1 ratings)
7.4
(19 ratings)
Likelihood to Renew
-
(0 ratings)
8.0
(1 ratings)
Usability
-
(0 ratings)
9.0
(1 ratings)
Implementation Rating
-
(0 ratings)
10.0
(1 ratings)
Product Scalability
-
(0 ratings)
5.0
(1 ratings)
User Testimonials
Django WaffleOptimizely Feature Experimentation
Likelihood to Recommend
Open Source
I would recommend that engineers look to try Django Waffle for feature development within a team setting; it really streamlines flipping features on and off for testing. I think the simplicity of Django Waffle is pretty great, but it may be a limitation for those developing far more complex products.
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Optimizely
Optimizely Feature Experimentation works really well for setting up feature flags with an easy UI for turning them on and off or ramping up a gradual rollout. It also works really well to set up split tests where you can split your traffic by percentage as well as almost any custom data attribute you wish to define. This is more for robust features and less for visual changes - Optimzely Edge or Web are better suited for that.
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Pros
Open Source
  • User-friendly syntax
  • Easy installation
  • Lightweight and supported, so not much in the way of bugs
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Optimizely
  • Its ability to run A/B tests and multivariate experiments simultaneously allows us to identify the best-performing options quickly.
  • Optimizely blends into our analytics tools, giving us immediate feedback on how our experiments are performing. This tool helps us avoid interruptions. With this pairing, we can arrive at informed decisions quickly.
  • Additionally, feature toggles enable us to introduce new features or modifications to specific user groups, guaranteeing a smooth and controlled user experience. This tool helps us avoid interruptions.
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Cons
Open Source
  • Not as complex as Gutter, albeit suitable for most product needs that I've encountered
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Optimizely
  • Splitting feature flags from actual experiments is slightly clunky and can be done either as part of the same page or better still you can create a flag on the spot while starting an experiment and not always needing to start with a flag.
  • Recommending metrics to track based on description using AI
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Usability
Open Source
No answers on this topic
Optimizely
All features that we used were pretty clear. They have a good documentation
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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
I found Django Waffle to work seamlessly with my Django-native codebase, and the implementation was extremely fast and intuitive. Gargoyle offers a bit more in the way of building more complicated code segments, but it's also more complex to implement. I use Django Waffle for nearly every project involving feature flipping.
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Optimizely
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 necessary (marketers can build experiments quickly with Optimizely Web without developers' help).
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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
  • Easy to show clients different feature options without rewriting the whole codebase
  • Faster feedback loops during testing
  • Good for synchronizing team effort across the codebase
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Optimizely
  • Experimentation is key to figuring out the impact of changes made on-site.
  • Experimentation is very helpful with pricing tests and other backend tests.
  • Before running an experiment, many factors need to be evaluated, such as conflicting experiments, audience, user profile service, etc. This requires a considerable amount of time.
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ScreenShots

Optimizely Feature Experimentation Screenshots

Screenshot of AI Variable suggestions: AI helps to develop higher quality experiments. Optimizely’s Opal suggests content variations in experiments, and helps to increase test velocity  and improve experiment qualityScreenshot of Integrations: display of the available integrations in-app.Screenshot of Reporting used to share insights, quantify experimentation program performance using KPIs like velocity and conclusive rate across experimentation projects, and to drill down into the charts and figures to see an aggregate list of experiments. Results can be exported into a CSV or Excel file, and KPIs can be segmented using project filters, experiment type filters, and date rangesScreenshot of Collaboration: Centralizes tracking tasks in the design, build, and launch of an experiment to ensure experiments are launched on time . Includes calendar, timeline, and board views in customizable views that can be saved to share with other stakeholdersScreenshot of Scheduling: Users can schedule a Flag or Rule to toggle on/off,  traffic allocation percentages, and achieve faster experimentation velocity and smoother progressive rolloutsScreenshot of Metrics filtering: Dynamic event properties to filter through events. Dynamic events provide better insights for experimenters who can explore metrics in depth for more impactful decisions