LaunchDarkly vs. Statsig

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

This is clearly a platform built around experimentation first, and it shows. In this way Statsig is way ahead of the competition of products I've used previously! It's more data science focussed which makes configuration of new experiments complex with a learning curve.

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

• Makes setting up experiments easy
• Really responsive support
• Advanced experimental config for detailed statistical analysis
• Post experiment analysis tools

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

• Complex data science focussed UI

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

For the most part it is pretty easy to use. - There are some quirks with the javascript SDK (getExperiment().getValue?). - The Events vs. Metrics design pattern is complex, and creating new Metrics from Events can be frustrating if you are trying to use event metadata - It's really frustrating not to be able to link Static IDs (before a user signs up) to User IDs, in order to follow users all the way through onboarding, or to log events that occur for signed in users when you are exposing the experiment to users before they've signed up

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

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

Incentivized

The platform didn't go down since we implemented it

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 uncovered several feature releases that were causing a negative impact on our product activation rate by running exclusion experiments

Incentivized