DevCycle vs. Optimizely Feature Experimentation

DevCycle

DevCycle

0 Reviews and Ratings

Optimizely Feature Experimentation

Optimizely Feature Experimentation

81 Reviews and Ratings

Overview
Product	Rating	Most Used By	Product Summary	Starting Price
DevCycle	Score 0.0 out of 10	N/A	DevCycle is a Feature Management Platform designed for developers who strive to or use continuous delivery and deployment that need to implement feature flags and manage their workflow more effectively. To avoid creating unnecessary complexities, bottlenecks, and tech-debt, DevCycle's developer-centric approach consolidates flags across environments and features. This enables development teams to own and control flags for the features they are currently working on without leaving their…	N/A
Optimizely Feature Experimentation	Score 8.3 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

DevCycle

Optimizely Feature Experimentation

Editions & Modules

No answers on this topic

No answers on this topic

Offerings

Pricing Offerings
DevCycle	Optimizely Feature Experimentation
Free Trial
No	No
Free/Freemium Version
Yes	Yes
Premium Consulting/Integration Services
No	No

Entry-level Setup Fee

No setup fee

Required

Additional Details

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More Pricing Information

Community Pulse
	DevCycle	Optimizely Feature Experimentation
Considered Both Products	DevCycle No answer on this topic	Optimizely Feature Experimentation Verified User Anonymous Chose Optimizely Feature Experimentation 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 … Incentivized Helpful? Verified User Anonymous 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. Incentivized Helpful? Verified User Anonymous 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. Incentivized Helpful? Verified User Anonymous 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 Incentivized Helpful? Verified User Anonymous 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 … Incentivized Helpful? DJ Dustin Jansick Project Manager Chose Optimizely Feature Experimentation Because we were looking for a solution to a/b test. Incentivized Helpful? Verified User Anonymous 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. Incentivized Helpful? Verified User Anonymous 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. Incentivized Helpful? Verified User Anonymous 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 … Incentivized Helpful? SP Sergio Palomino Digital Experience & Optimization Team Lead Chose Optimizely Feature Experimentation not too much experience on that to answer this question Incentivized Helpful? Verified User Anonymous Chose Optimizely Feature Experimentation There is a lot more flexibility with Optimizely once you have customized the implementation and better tools. Incentivized Helpful? Verified User Anonymous Chose Optimizely Feature Experimentation WebX and FeatureX work well in pair, they organically complement each other Incentivized Helpful? Verified User Anonymous Chose Optimizely Feature Experimentation Simple interface and ability to create audiences and assign them to experiments. Incentivized Helpful? Verified User Anonymous 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. Incentivized Helpful? Verified User Anonymous 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. Incentivized Helpful? Andrei Beno Director of Product Management Chose Optimizely Feature Experimentation Mixpanel, Google Analytics, Hotjar and A/B Smartly Incentivized Helpful? Verified User Anonymous Chose Optimizely Feature Experimentation With the Netspring acquisition I think it's closer to its competitor's features Incentivized Helpful? Mitchell Berger Senior software engineer 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. Incentivized Helpful? Francis Ashton MsC Email & Crm manager Chose Optimizely Feature Experimentation Because allows us to do test and Learn from Incentivized Helpful? Verified User Anonymous Chose Optimizely Feature Experimentation we wanted a shift with the tool that helps us with managing our data Incentivized Helpful? Verified User Anonymous 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 … Incentivized Helpful? Dimos Papadopoulos Senior Product Manager 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. Incentivized Helpful? Verified User Anonymous 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 … Incentivized Helpful? Verified User Anonymous 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. Incentivized Helpful? Verified User Anonymous 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 … Incentivized Helpful?

Best Alternatives
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Medium-sized Companies	GitLab Score 8.8 out of 10	GitLab Score 8.8 out of 10
Enterprises	GitLab Score 8.8 out of 10	GitLab Score 8.8 out of 10
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User Ratings
	DevCycle	Optimizely Feature Experimentation
Likelihood to Recommend	- (0 ratings)	8.2 (0 ratings)
Likelihood to Renew	- (0 ratings)	4.5 (0 ratings)
Usability	- (0 ratings)	7.6 (0 ratings)
Support Rating	- (0 ratings)	3.6 (0 ratings)
Implementation Rating	- (0 ratings)	10.0 (0 ratings)
Product Scalability	- (0 ratings)	5.0 (0 ratings)

User Testimonials
	DevCycle	Optimizely Feature Experimentation
Likelihood to Recommend	No answers on this topic	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 Andrei Beno Director of Product Management Read full review
Pros	No answers on this topic	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 Incentivized Verified User Anonymous Read full review
Cons	No answers on this topic	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. Incentivized Verified User Anonymous Read full review
Likelihood to Renew	No answers on this topic	Competitive landscape Incentivized Verified User Anonymous Read full review
Usability	No answers on this topic	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 Verified User Anonymous Read full review
Support Rating	No answers on this topic	Support was there but it was pretty slow at most times. Only after escalation was support really given to our teams Incentivized Verified User Anonymous Read full review
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 Incentivized Verified User Anonymous Read full review
Alternatives Considered	No answers on this topic	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. Incentivized Verified User Anonymous Read full review
Scalability	No answers on this topic	had troubles with performance for SSR and the React SDK Incentivized Verified User Anonymous Read full review
Return on Investment	No answers on this topic	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 Dimos Papadopoulos Senior Product Manager Read full review
ScreenShots	DevCycle Screenshots	Optimizely Feature Experimentation Screenshots