LaunchDarkly provides a feature management platform that enables DevOps and Product teams to use feature flags at scale. This allows for greater collaboration among team members, and increased usability testing before full-scale feature deployment.
$12
per month per Service Connection per month, or $10 per 1k client-side MAU per mo
Optimizely Feature Experimentation
Score 8.7 out of 10
N/A
Optimizely Feature Experimentation unites feature flagging, A/B testing, and built-in collaboration—so marketers can release, experiment, and optimize with confidence in one platform.
N/A
Pricing
LaunchDarkly
Optimizely Feature Experimentation
Editions & Modules
Foundation
$12
per month per Service Connection per month, or $10 per 1k client-side MAU per mo
Enterprise
Custom
Guardian
Custom
No answers on this topic
Offerings
Pricing Offerings
LaunchDarkly
Optimizely Feature Experimentation
Free Trial
Yes
No
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
Optional
Required
Additional Details
Discount available on the Foundation plan for annual pricing.
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.
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 -
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.
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.
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
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.
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.
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.
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.
We have a huge, noteworthy ROI case study of how we did a SaaS onboarding revamp early this year. Our A/B test on a guided setup flow improved activation rates by 20 percent, which translated to over $1.2m in retained ARR.
