AB Tasty vs. Optimizely Feature Experimentation

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
AB Tasty
Score 8.8 out of 10
Mid-Size Companies (51-1,000 employees)
AB Tasty is a SAAS application created for e-marketers that enables them to optimize their website and conversion rate without technical knowledge. They can test several versions of their pages to identify which one has the biggest impact on their business objectives, e.g. click-through rate on a call to action button, add-to-cart rate, global conversion rate of their website.N/A
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
AB TastyOptimizely Feature Experimentation
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
AB TastyOptimizely Feature Experimentation
Free Trial
NoNo
Free/Freemium Version
NoYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeRequired
Additional Details
More Pricing Information
Community Pulse
AB TastyOptimizely Feature Experimentation
Considered Both Products
AB Tasty
Chose AB Tasty
AB Tasty is the king when it comes to cost/benefit. It is not the cheapest but not the most expensive. It is a very advanced tool and we like the fact of not having to pay extra fees for more advanced features that we may only use for one test. Also, the chat support …
Chose AB Tasty
We selected AB Tasty as the support is way better than the WYSIWYG editor. We will still work with AB Tasty as in the addition to the solution because a lot of support is provided (roadmap, workshops, evangelization, etc).
Chose AB Tasty
AB Tasty is more of a marketer's tool. It's much better when it comes to QA, handly and reliable. There is a great customer support chat available in 5 minutes.
Chose AB Tasty
We decided on AB Tasty as there were numerous features at this time that other providers did not offer and we felt like being on the right track for the upcoming years and challenges. Also, the price was for sure one of the criteria. AB Tasty offered an attractive package …
Chose AB Tasty
Privy showed our websites down and was very limited. It's great to be able to use ab tests and have more options without loss of speed
Chose AB Tasty
The product roadmap is interesting. Support is quite professional. Features are very useful.
Chose AB Tasty
AB Tasty is great, probably the best optimizing tool you can use. Easy, loads of features, great support from CSM. Great variation editor whereas Monetate and VWO don't have a lot of features/widgets. Optimizely is good but again not a lot of capability without a dev. Whereas …
Chose AB Tasty
We did test couple of platforms before decision, but A/B Tasty support alreasy on Proof of Concept phase was exceptional and price was reasonable, so the decision was fairly easy.
Chose AB Tasty
AB Tasty is much more affordable and provides very similar results. We are happy we made the switch and have seen the same success we had with other platforms on AB Tasty.
Chose AB Tasty
VWO & Optimizely, in my opinion, are for large corporations. They're expensive and very limiting.
Chose AB Tasty
We have not used the AB Tasty heat maps or user recording feature so I'm not too sure how they stack up. As for SiteSpect it is a more intricate testing platform that uses the cloud to run tests, however, it's much more expensive and I think AB Tasty gives more bang for your …
Chose AB Tasty
AB Tasty has very aggressive pricing and a simple-to-use toolset. We feel that AB Tasty gives us what we need and the other vendors don't provide additional value to justify the cost.
Chose AB Tasty
We selected AB Tasty mostly because we got a sense that their dev and customer service teams were going to go above and beyond to help us out. We were right! The cost was also a factor as they came in a small bit lower, but cost wasn't the only factor.
Chose AB Tasty
AB Tasty is far more affordable than most of them, but with the same functionality and better reporting (reporting looks similar to Optimizely). Monetate requires an agency or a dedicated CRO team to manage and run tests, and is best for large companies with a lot of traffic …
Chose AB Tasty
Optimize does not support SPAs, it was a deal breaker for us. AB Tasty supports it very well.
Chose AB Tasty
AB Tasty isn't as sophisticated or advanced as Monetate. The analytics section in Monetate is fabulous. But AB Tasty is great for smaller companies with a smaller budget. And the AB Tasty support has been better.
Chose AB Tasty
We selected AB Tasty over other leading A/B Testing platforms due to its huge list of features, dedicated account support and competitive licensing model.
Chose AB Tasty
AB Tasty has a good price/quality ratio. They are the European leader and have a good reputation. We were satisfied with them so we kept them.
Chose AB Tasty
AB Tasty shares data with many analytics solutions as Google Analytics or At Internet solutions
Chose AB Tasty
Crazy Egg was mainly only used for heat mapping, which it does really well. I haven't used Google Optimize too much yet but it has most of the same functionality as A/B Tasty. Google Analytics is more used for reporting and a deeper look into data. AB Tasty by far has the best …
Chose AB Tasty
It's a different tool - and could probably borrow some of the UI and ease of use from the other tools, but excels in providing prebuilt functionality and more detailed targeting. It also needs a lot more learning - when the other tools are pretty much pick up and go.
Chose AB Tasty
It's just the perfect mix between all tools I have used in the past.
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 …
Features
AB TastyOptimizely Feature Experimentation
Testing and Experimentation
Comparison of Testing and Experimentation features of Product A and Product B
AB Tasty
8.1
Ratings
1% below category average
Optimizely Feature Experimentation
-
Ratings
a/b experiment testing8.10 Ratings00 Ratings
Split URL testing7.30 Ratings00 Ratings
Multivariate testing8.10 Ratings00 Ratings
Multi-page/funnel testing9.00 Ratings00 Ratings
Mobile app testing5.00 Ratings00 Ratings
Test significance8.30 Ratings00 Ratings
Visual / WYSIWYG editor10.00 Ratings00 Ratings
Advanced code editor8.30 Ratings00 Ratings
Page surveys5.00 Ratings00 Ratings
Preview mode7.60 Ratings00 Ratings
Test duration calculator10.00 Ratings00 Ratings
Experiment scheduler9.10 Ratings00 Ratings
Client-side tests9.90 Ratings00 Ratings
Server-side tests7.00 Ratings00 Ratings
Mutually exclusive tests9.10 Ratings00 Ratings
Audience Segmentation & Targeting
Comparison of Audience Segmentation & Targeting features of Product A and Product B
AB Tasty
8.5
Ratings
0% below category average
Optimizely Feature Experimentation
-
Ratings
Standard visitor segmentation9.10 Ratings00 Ratings
Behavioral visitor segmentation7.40 Ratings00 Ratings
Traffic allocation control9.10 Ratings00 Ratings
Website personalization8.30 Ratings00 Ratings
Results and Analysis
Comparison of Results and Analysis features of Product A and Product B
AB Tasty
8.2
Ratings
2% below category average
Optimizely Feature Experimentation
-
Ratings
Heatmap tool7.30 Ratings00 Ratings
Click analytics7.60 Ratings00 Ratings
Scroll maps8.50 Ratings00 Ratings
Conversion tracking8.10 Ratings00 Ratings
Goal tracking9.00 Ratings00 Ratings
Test reporting8.30 Ratings00 Ratings
Results segmentation8.00 Ratings00 Ratings
Experiments results dashboard9.10 Ratings00 Ratings
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User Ratings
AB TastyOptimizely Feature Experimentation
Likelihood to Recommend
9.0
(0 ratings)
8.9
(0 ratings)
Likelihood to Renew
7.7
(0 ratings)
4.5
(0 ratings)
Usability
9.0
(0 ratings)
7.3
(0 ratings)
Support Rating
7.3
(0 ratings)
-
(0 ratings)
Implementation Rating
-
(0 ratings)
10.0
(0 ratings)
Product Scalability
-
(0 ratings)
5.0
(0 ratings)
User Testimonials
AB TastyOptimizely Feature Experimentation
Likelihood to Recommend
A/B Tasty tool allows us do easy testing without burdening our limited developer resources all the time. Reports are simple enough to interpret. Support has been excellent and proactive, also the onboarding was successful. It is also an advantage that we have the possibility to drive all our traffic to the test versions rather than being limited to specific user amount per month. We can recommend A/B Tasty for testing purposes, as the platform keeps it’s promises, doesn’t require too much technical knowledge and the support is excellent.
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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
  • Easy setup - Simple script insertion into site header.
  • Intuitive interface - It took very little training for our team to understand how to start running A/B tests. The reporting is much more comprehensive, yet easily digestible than other platforms we have used or considered. Confidence scores, flexible KPI inclusions/tracking allow us to understand the results or non-result quickly and with clarity to make decisions on next steps.
  • Customer support - Our CSM and tech support are always helpful and proactive when a question or issue arise. Our CSM keeps us on track when an idea might have dropped off the map due to other priorities, and brings great ideas to the table. We enjoy our monthly touchbases with her when we get to see new functionalities, how other clients successfully used them, and brainstorm ways we can use them for our experience.
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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
  • Sometimes when you're preparing a test the editor "crashes" and you need to re open it. It's just a matter of having to go back to the main dashboard a few times during your preparation.
  • Maybe to have the option to have pre-build templates of pages, buttons and assets that we can use to test experiments.
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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.
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Likelihood to Renew
AB Tasty's tool as well as the support team completely met my goals on A/B Testing. Editing a test is really easy and AB Tasty made the marketing team free to launch nearly any test. Reporting is also easy to set up and give us the information needed to keep improving transformation on landing pages and forms
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Competitive landscape
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Usability
The tool’s usability is excellent, with a smooth and intuitive interface. It is continuously updated with new features and improvements aimed at enhancing the user experience. Setting up experiments and personalizations is straightforward, thanks to a well-structured process that even less experienced users can easily follow. There are sometimes small issues with the QA section which slows the QA process but it is so much better now compared to 2024.
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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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Support Rating
Support is good, but it would be better if it was quicker or if AB Tasty provided a quicker SLA. At times, you require stuff urgently but AB Tasty support isn't as quick as I personally like it to be.
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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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Online Training
There is a vast amount of training available on their online platform, including their university area of which you can get certified for (and then share this with your connections on platforms such as Linkedin). There is also specific hand created training which can be provided by your customer success manager if that is requested by yourself.
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No answers on this topic
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
We selected AB Tasty mostly because we got a sense that their dev and customer service teams were going to go above and beyond to help us out. We were right! The cost was also a factor as they came in a small bit lower, but cost wasn't the only factor.
Read full review
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
No answers on this topic
had troubles with performance for SSR and the React SDK
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Return on Investment
  • This past year we improved our new visitor conversion rates by 73% with very little increases in our ad spend. We've been able to test quickly and infer how those experiments impacted lead generation. Experimentation is all about continually learning and testing.
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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

AB Tasty Screenshots

Screenshot of the reporting interface.Screenshot of the dynamic widget library.Screenshot of AB Tasty's WYSIWYG editorScreenshot of the campaign management dashboardScreenshot of targeting criteria, available so users can target specific user segments

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