LaunchDarkly vs. Optimizely Feature Experimentation

If a new feature should be added but unsure of how it will actually work or how users will accept the new enhancement or change, this tool allows you test and measure initial results. This saves so much time and energy knowing the results before it is deployed and might have low user adoption or acceptance.

Incentivized

To my experience, Optimizely should be used across all core software releases in platforms/apps and products that are customer facing regardless whether b2c or b2b. Even in cases of internal platforms with thousands of users, testing features or pre-releasing and monitoring functionality is key. Good to note that it is less appropriate in cases where minor releases, backend software improvements are referred to.

Incentivized

• A/B or Multi Variant Testing as a methodology to gather insight from customer usage. Experimentation as a feature within LaunchDarkly offers information around the success of one variant over another and whether the experiment has reached statistical significance.
• Being able to decouple deployment of code from the release of a feature is hugely valuable.
• Development teams are empowered to manage features within their production applications for reliability or testing purposes.

Incentivized

• 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.

Incentivized

• Limited number of users on cheaper plans that is limiting our ability to audit log who is making changes.
• Some of our engineers are confused between flags and segments and have set up items incorrectly.
• Better documented support for React with Typescript.

Incentivized

• Running Experiments Exclusively with user profile service. If one experiment is already allocated with the user profile, another experiment in the exclusion group should not trigger the experiment.
• Ability to reevaluate audience condition with user profile service, in case audience condition do not match remove override from user profile override. Differentiate between qe override and user profile override.
• Ability to connect multiple IDs to achieve consistent experimentation across devices.
• Any way to achieve optimizely web capability using Optimizely Feature Experimentation.

Incentivized

It fits out business case

Incentivized

It's very easy to create new feature flags and set them properly. It is more difficult to get LaunchDarkly integrated within a distributed system so that flags can be used. Especially on stateless servers where gating features by user is not easy. Overall though, it is very easy to get started and I like how simple it is to use.

Incentivized

All features that we used were pretty clear. They have a good documentation

Incentivized

No issue with availability at all

Incentivized

From what I have seen, LaunchDarkly integrates well with your code and also services you might have in your tech ecosystem. We use Jenkins for automation and we were able to use it to build pipelines to automate the control of LaunchDarkly toggles in our code.

Incentivized

The overall support is very responsive

Incentivized

Yes I do.

Incentivized

It’s straightforward. Docs are well written and I believe there must be a support. But we haven’t used it

Incentivized

LaunchDarkly stood out to us because it put control of the application within the hands of our engineers. We didn't want to allow business users to manipulate the production site via a third-party tool. Instead, our focus was on delivering faster as an engineering team.

Incentivized

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).

Incentivized

The platform didn't go down since we implemented it

Incentivized

had troubles with performance for SSR and the React SDK

Incentivized

• Improved developer experience with some teams moving to Trunk-based Development.
• Increased deployment frequency due to smaller code releases.
• Validation of the technical and business value of work is achieved more quickly through smaller pieces of work and through experimenting with a small group of users before a feature gets to 100% of customers.

Incentivized

• 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