Control your features
Feature Flags are a software development tool that ensures an efficient, low-risk release cycle by enabling or disabling features in real time without deploying new code.
Bucketeer offers advanced features, such as dark launch and staged rollouts, that perform limited releases based on user attributes, devices, and other segments.
With feature flags, you can continuously deploy new features that are still in development without making them visible to the users.
This makes it possible to separate the deployment from the release, which allows teams to manage the feature's entire lifecycle.
Control your features
Feature Flags are a software development tool that ensures an efficient, low-risk release cycle by enabling or disabling features in real time without deploying new code.
Bucketeer offers advanced features, such as dark launch and staged rollouts, that perform limited releases based on user attributes, devices, and other segments.
With feature flags, you can continuously deploy new features that are still in development without making them visible to the users.
This makes it possible to separate the deployment from the release, which allows teams to manage the feature's entire lifecycle.
Better decisions with data
A/B testing is an experimentation process to compare one or multiple versions of an application. It helps your team analyze what performs better, and make better data-driven decisions without relying on intuition or personal experience.
Bucketeer uses the Bayesian probabilities to analyze which variable of your A/B test is likely to perform better. Because it requires a smaller sample size, you can get results faster with lower experimentation costs than a Frequentist probabilities.
Better decisions with data
A/B testing is an experimentation process to compare one or multiple versions of an application. It helps your team analyze what performs better, and make better data-driven decisions without relying on intuition or personal experience.
Bucketeer uses the Bayesian probabilities to analyze which variable of your A/B test is likely to perform better. Because it requires a smaller sample size, you can get results faster with lower experimentation costs than a Frequentist probabilities.
Increased Development Speed
Trunk-based development reduces lead time, speeding up the process from code review to release with the use of feature flags.
Developers can implement a new feature by disabling the flag and deploying it to the main branch at any time.
This helps prevent merge conflicts caused by long-lived branches and reduces code review costs.
This practice is essential for large teams to ensure that a shared branch is always releasable without delaying the QA time and affecting the user.
Increased Development
Trunk-based development reduces lead time, speeding up the process from code review to release with the use of feature flags.
Developers can implement a new feature by disabling the flag and deploying it to the main branch at any time.
This helps prevent merge conflicts caused by long-lived branches and reduces code review costs.
This practice is essential for large teams to ensure that a shared branch is always releasable without delaying the QA time and affecting the user.