ConfigCat allows the user to launch new features and change software configuration without (re)deploying code. ConfigCat SDKs enable easy integration with any web, mobile or backend applications. The ConfigCat website enables non-developers too to switch ON/OFF application features or change software configuration. This way the user can decouple feature launches and configuration from code deployment.
$0
per month
Optimizely Web Experimentation
Score 8.6 out of 10
N/A
Optimizely Web Experimentation empowers teams to conduct experiments (without having to rely on developer resources) in order to test various user interactions, make website changes backed by data, and personalize customer experiences.
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Pricing
ConfigCat
Optimizely Web Experimentation
Editions & Modules
Free
$0.00
per month
Professional
$49.00
per month
Unlimited
$199.00
per month
Dedicated on-premise infra
$1499.00
per month
Dedicated hosted infra
$1499.00
per month
No answers on this topic
Offerings
Pricing Offerings
ConfigCat
Optimizely Web Experimentation
Free Trial
Yes
Yes
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
Yes
Entry-level Setup Fee
No setup fee
Optional
Additional Details
Fair pricing policy: All features available in all plans, even in Free. Simple and predictable prices. No hidden fees. We don't charge for team size. We don't charge for MAUs (monthly active users). Our plans only differ in limitations.
ConfigCat isn't 'battleship' class with tons of bells of whistles when compared to Optimizely or LaunchDarkly. Having said that, it does what we needed it to do and had a roadmap to add any functionality that we have felt as a shortcoming. ConfigCat is also a fraction of the …
If you are looking for an experimentation/feature flag style tool that is quick to adopt and provides enough functionality for light/medium use cases, then this is the tool for you. Additionally, they are growing and expanding their functionality and feature set so they can grow alongside you and your needs. The publicly accessible roadmap is also a great benefit to see where time is being spent on which feature next.
Best for CRO initiatives, including testing variations of landing pages, user flows, and product pages. Optimizely may not be suitable for more complex machine learning models. To analyze the effect of the feature usage the way we will do in our solution. Using the audience to measure the success of the features Also, to the best of my knowledge, Optimizely is the only tool that can do all this.
Powerful Stats Engine that drives conclusivity of outcomes and helps generate trust in results when shared to leadership and stakeholders.
Customizable metrics with various tags, properties, and attributes that allow users flexibility in what and how they architect their Optimizely analytics.
Flexibility for different levels of tech expertise, I live in the tool as an expert JavaScript and front-end developer, someone else might use solely the visual editor to click and make changes without knowledge of how to code.
Google Tag Manager. Our challenge, it's a strange use case, but our challenge is that we don't have Google Tag Manager, so we can't integrate with GA4. And that's been a bit of a bummer. So I would like to be able to integrate with GA4 even without Google Tag Manager.
I rated this question because at this stage, Optimizely does most everything we need so I don't foresee a need to migrate to a new tool. We have the infrastructure already in place and it is a sizeable lift to pivot to another tool with no guarantee that it will work as good or even better than Optimizely
Optimizely Web Experimentation's visual editor is handy for non-technical or quick iterative testing. When it comes to content changes it's as easy as going into wordpress, clicking around, and then seeing your changes live--what you see is what you get. The preview and approval process for sharing built experiments is also handy for sharing experiments across teams for QA purposes or otherwise.
I would rate Optimizely Web Experimentation's availability as a 10 out of 10. The software is reliable and does not experience any application errors or unplanned outages. Additionally, the customer service and technical support teams are always available to help with any issues or questions.
I would rate Optimizely Web Experimentation's performance as a 9 out of 10. Pages load quickly, reports are complete in a reasonable time frame, and the software does not slow down any other software or systems that it integrates with. Additionally, the customer service and technical support teams are always available to help with any issues or questions.
They have a community Slack channel that is open to anyone. They always seem to have people in there, even over the weekends and are always happy to answer any questions you have,
They always are quick to respond, and are so friendly and helpful. They always answer the phone right away. And [they are] always willing to not only help you with your problem, but if you need ideas they have suggestions as well.
The tool itself is not very difficult to use so training was not very useful in my opinion. It did not also account for success events more complex than a click (which my company being ecommerce is looking to examine more than a mere click).
In retrospect: - I think I should have stressed more demo's / workshopping with the Optimizely team at the start. I felt too confident during demo stages, and when came time to actually start, I was a bit lost. (The answer is likely I should have had them on-hand for our first install.. they offered but I thought I was OK.) - Really getting an understanding / asking them prior to install of how to make it really work for checkout pages / one that uses dynamic content or user interaction to determine what the UI does. Could have saved some time by addressing this at the beginning, as some things we needed to create on our site for Optimizely to "use" as a trigger for the variation test. - Having a number of planned/hoped-for tests already in-hand before working with Optimizely team. Sharing those thoughts with them would likely have started conversations on additional things we needed to do to make them work (rather than figuring that out during the actual builds). Since I had development time available, I could have added more things to the baseline installation since my developers were already "looking under the hood" of the site.
At iBinder we searched for and vetted several suppliers of a feature toggle service to handle feature toggling in our production environment. In addition to our functional requirements, it was crucial for us to find a partner that could deliver an EU-compliant service. We finally decided to sign a service agreement with ConfigCat. This has been a real success story for us – in addition to being compliant, ConfigCat delivers an amazing, flexible, and reliable service. They continue to impress by also being very transparent and having a fantastic support and they are very solution oriented and accommodating when it comes to our feature requests etc. We have now used ConfigCat for approximately 2 years and we give our warmest recommendations to anyone who needs a stable, reliable and EU-compliant feature toggle service.
Optimizely Web Experimentation was more robust and able to handle the broad array of sites we run than VWO. It has been a great platform to easily add additional sites onto, but still providing a universal overview of all of them, making management a simple task.
It's incredibly flexible and adapts well to organizations of all sizes, whether you’re running a single site or managing multiple departments and platforms. The ability to deploy experiments seamlessly across different environments is a huge plus, especially for growing businesses. While it’s highly scalable, the last point would depend on the right team leveraging its full potential.
Allowed us to migrate seamlessly from a major customer communication system to another, reducing end-user friction and production bugs by being able to turn features off if they didn't work as intended.
We went from zero experimentation to running 10-20 experiments concurrently across systems. Engineering teams are thinking in an experimentation mindset.
