LaunchDarkly vs. Optimizely Feature Experimentation

Great for rolling out features slowly for beta testing in production. I would say it is less well suited for toggling features permanently for users as this requires more integration with our backend and billing systems that would be a lot of work to set up.

Incentivized

Based on my experience with Optimizely Feature Experimentation, I can highlight several scenarios where it excels and a few where it may be less suitable. Well-suited scenarios: - Multi-Channel product launches - Complex A/B testing and feature flag management - Gradual rollout and risk mitigation Less suited scenarios: - Simple A/B tests (their Web Experimentation product is probably better for that) - Non-technical team usage -

Incentivized

• Feature Flag Management: It's like magic. With a flip of a switch, you can manage feature rollouts to visitors or accounts across the web and mobile applications!
• Segmentation: Create a segment of visitors or accounts and then use that to target a feature flag rule. Really easy to use and saves so much time.
• Ease of Use: Seamless copy/paste functionality, really clear status indicators so you can find what is on and for whom.

Incentivized

• Splitting traffic between variants and enabling you to scale up or down the amount of traffic in each one
• Giving a standardised report that you can share with a huge number of users
• Showing a large variety of results/metrics you can then dive into

Incentivized

• It would be nice to see a feature flag's settings against all environments at once.
• It would be to have a "array" type flag for related but different settings (eg, enableA and enableB could be enable: [a, b]).
• It would be nice have customizable columns on the Users page (eg, if I want to inject a new meta data).

Incentivized

• Difficult integration if your data is not front end
• Costly MAU model needs to be based on experiments not on site visits
• It's not easy to understand how to build an Experiment
• Onboarding team is more focused on punching through their slides and not focused on your needs or understanding.

Incentivized

It fits out business case

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Competitive landscape

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

Easy to navigate the UI. Once you know how to use it, it is very easy to run experiments. And when the experiment is setup, the SDK code variables are generated and available for developers to use immediately so they can quickly build the experiment code

Incentivized

No issue with availability at all

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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

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Support was there but it was pretty slow at most times. Only after escalation was support really given to our teams

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Yes I do.

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

Incentivized

Rollout is another dedicated feature flag tool that can be used to manage features. LaunchDarkley offers all the features of an enterprise level tool, unlike Rollout, reserves the security features for the Enterprise plan. Out of box integrations are limited but they do have a well documented REST API.

Incentivized

In previous companies I've used Monetate which is a similar A/B testing kind of feature experimentation engine that is very similar from my memory, but again, back to the point of these new features of the analytics engine and Opal, it kind of cuts it above Monetate from my experience. Obviously Monetate may have improved since when I lost use it, but from what I can see, yeah.

Incentivized

The platform didn't go down since we implemented it

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had troubles with performance for SSR and the React SDK

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• 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