Flagship.io vs. Optimizely Feature Experimentation

The Flagship technical teams are always available and reactive to help us with our problems. The onboarding provided by the teams was very smooth. The interface is easy to use and very user-friendly. The feature flag management and progressive roll-out are features that are very useful/helpful, but it is still something that requires some more onboarding in our teams for it to become an important part of our processes.

It is a great tool when you have apps written in different languages and don't have common data storage for these applications, but you like to enable/disable functionality in multiple services at a time. It is well suited for blue/green deployments, too.

Incentivized

• Interface is very user friendly
• Technical support always available and helpful
• Feature flag management and Progressive roll-out

• 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

• Collectively improve Flutter compatibility and integration

• 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

Incentivized

I think setting up experiments is very straightforward. It's also very easy to get started on the code side. I think if someone was new to Optimizely Feature Experimentation there could be some confusion between a flag and an experiment. I still get confused sometimes by if I turned the right thing on or off.

Incentivized

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

Incentivized

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 when we saw that other big organisations are partnering with you.

Incentivized

had troubles with performance for SSR and the React SDK

Incentivized

• We have not yet implemented enough use cases to be able to mention the return on investment but we are working towards constructing more use cases to be able to work on this aspect.

• 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.

Incentivized