LaunchDarkly vs. Statsig

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
LaunchDarkly
Score 7.9 out of 10
N/A
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
Statsig
Score 8.9 out of 10
N/A
Statsig is a feature management with feature flags, pulse, holdouts, from the company of the same name in Bellevue.N/A
Pricing
LaunchDarklyStatsig
Editions & Modules
Foundation
$12
per month per Service Connection per month, or $10 per 1k client-side MAU per mo
Enterprise
Custom
Guardian
Custom
Enterprise
Custom
annual pricing
Offerings
Pricing Offerings
LaunchDarklyStatsig
Free Trial
YesNo
Free/Freemium Version
NoYes
Premium Consulting/Integration Services
YesNo
Entry-level Setup FeeOptionalNo setup fee
Additional DetailsDiscount available on the Foundation plan for annual pricing.
More Pricing Information
Community Pulse
LaunchDarklyStatsig
Best Alternatives
LaunchDarklyStatsig
Small Businesses
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GitLab
Score 8.6 out of 10
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Medium-sized Companies
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GitLab
Score 8.6 out of 10
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GitLab
Score 8.6 out of 10
Enterprises
GitLab
GitLab
Score 8.6 out of 10
GitLab
GitLab
Score 8.6 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
LaunchDarklyStatsig
Likelihood to Recommend
10.0
(28 ratings)
8.5
(2 ratings)
Likelihood to Renew
7.0
(1 ratings)
-
(0 ratings)
Usability
7.4
(26 ratings)
6.5
(2 ratings)
Availability
10.0
(1 ratings)
-
(0 ratings)
Performance
8.1
(26 ratings)
-
(0 ratings)
Support Rating
10.0
(1 ratings)
-
(0 ratings)
Implementation Rating
9.0
(1 ratings)
-
(0 ratings)
Configurability
8.0
(1 ratings)
-
(0 ratings)
Ease of integration
8.0
(1 ratings)
-
(0 ratings)
Product Scalability
10.0
(1 ratings)
-
(0 ratings)
Vendor post-sale
8.0
(1 ratings)
-
(0 ratings)
Vendor pre-sale
10.0
(1 ratings)
-
(0 ratings)
User Testimonials
LaunchDarklyStatsig
Likelihood to Recommend
LaunchDarkly
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.
Read full review
Statsig
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.
Read full review
Pros
LaunchDarkly
  • 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.
Read full review
Statsig
  • Makes setting up experiments easy
  • Really responsive support
  • Advanced experimental config for detailed statistical analysis
  • Post experiment analysis tools
Read full review
Cons
LaunchDarkly
  • 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.
Read full review
Statsig
  • Complex data science focussed UI
Read full review
Likelihood to Renew
LaunchDarkly
It fits out business case
Read full review
Statsig
No answers on this topic
Usability
LaunchDarkly
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.
Read full review
Statsig
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
Read full review
Reliability and Availability
LaunchDarkly
No issue with availability at all
Read full review
Statsig
No answers on this topic
Performance
LaunchDarkly
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.
Read full review
Statsig
No answers on this topic
Support Rating
LaunchDarkly
The overall support is very responsive
Read full review
Statsig
No answers on this topic
Implementation Rating
LaunchDarkly
Yes I do.
Read full review
Statsig
No answers on this topic
Alternatives Considered
LaunchDarkly
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.
Read full review
Statsig
Read full review
Scalability
LaunchDarkly
The platform didn't go down since we implemented it
Read full review
Statsig
No answers on this topic
Return on Investment
LaunchDarkly
  • 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.
Read full review
Statsig
  • We uncovered several feature releases that were causing a negative impact on our product activation rate by running exclusion experiments
Read full review
ScreenShots

LaunchDarkly Screenshots

Screenshot of regression detection and automated incident response at the feature level. This connects critical metrics to the release process so that every change is monitored - even the smallest releases, where issues would previously have been obscured by noise in the wider system metrics.Screenshot of where track the progression of a feature flag across a series of phases, where each phase consists of one or more environments.Screenshot of how to target groups of contexts individually or by attribute. Contexts are people, services, machines, or other resources that encounter feature flags in a product.Screenshot of where to design experiments that measure business-critical user flows and provide results specific to those product funnels, and measure multi-step user journeys. This is used to determine whether conversions are succeeding, with all metrics visible in one place.