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Statsig vs. LaunchDarkly
LaunchDarkly built feature flags first and then added on experimentation and analytics. As a result, LaunchDarkly's statistical depth and workflow for running experimentation at scale still lags behind a purpose-built platform where experimentation has been the focus since day one, like Statsig.
Statsig's key advantages over LaunchDarkly are:
Metrics integrated into every release
Warehouse-native flexibility
Data integrity you can trust
The industry's most advanced stats engine
Built for the entire product team
Key Differences
01
Metrics integrated into every release
Feature flags should not just control rollouts, they should answer Did it work? Statsig ties every flag and experiment to integrated product analytics with advanced metric configurations and chart types like Metrics Explorer, funnels, and retention. You can double-click into how your metrics are tracking over time as a result of launches, measure business impact, catch regressions early, and ensure every release decision is backed by data your team can trust.
02
Warehouse-native flexibility
Statsig gives you the choice to run experimentation and analytics directly in your own warehouse — Snowflake, BigQuery, Redshift, Databricks, or Athena — or in Statsig’s managed cloud. This flexibility means you can keep data where it already lives or rely on a fully hosted option, while still gaining complete control and scalability without duplicating or moving events.
03
Data integrity you can trust
Reliable feature flagging and experiments start with reliable data and setup. Statsig ensures accuracy with logstream visibility, exposure tracking, and advanced health checks built in. Your team can debug with confidence, validate exposure groups, and make decisions knowing every result is based on a single, trustworthy source of truth.
04
The industry's most advanced stats engine
LaunchDarkly offers basic experimentation capabilities. Statsig provides the most advanced experimentation engine available with sequential testing, stratified sampling, CUPED, switchbacks, and instant flag-to-test conversion. Whether you are running small rollouts or global experiments, you can go beyond basic A/Bs and generate results that are rigorous, reproducible, and directly tied to business impact.
05
Built for the entire product team
LaunchDarkly was designed primarily for developers and DevOps, which can limit adoption across the broader product organization. Statsig was built for the entire product team including engineers, PMs, and data scientists collaborating in one platform. With shared workflows, approval flows, tagging, and data everyone can trust, Statsig breaks down silos so product decisions are made with speed, rigor, and alignment across teams.
Feature Comparison
Feature flagging
Core feature management capabilities to ship safely and control releases.
Feature flags
Decouple code deployment from releases, toggle features on and off
Dynamic configs
Replace hard coded values in your app with config values
Parameter stores
Store text parameters and call them in your app to change them on the fly
Flexible user targeting
Attribute-based, segment-based, environment-based, and custom rules
Built-in metrics & impact measurement
Convert any change you make into a lightweight A/B test
Multi-environment support
Support across multiple environments: dev, staging, prod
Automated gradual rollouts
Percentage-based and scheduled rollouts
SDKs for all major languages
Statsig supports 30+ server & client SDKs
Approval workflows
Support for reviews and other team-level release management workflows
Automated metric alerts and rollbacks
Set alerts and automatically rollback features that have a negative impact
In-product collaboration
Support for team collaboration and discussions within the console
Feature flag lifecycle management
Unified cross-environment view, stale flag alerts and code reference checks
Experimentation
Comprehensive experimentation capabilities for measuring impact and driving growth.
Bayesian & Frequentist methods
Support for both Bayesian and Frequentist methods
Mutually exclusive experiments
Ensure concurrent experiments do not interfere with each other
Holdouts
Create holdout groups not exposed to any experiment treatment
Sequential testing
Prevent early peeking and continuously analyze experiment data
Switchback testing
Measure impact even in the presence of network effects
Multi-armed bandits
Continuously optimize allocation toward high-performing variant
Geo-based experiments
Measure the incremental impact from marketing initiatives
No-code experiments
Create experiments with a visual editor
Essential statistical methodologies
Bonferroni correction, CUPED, Winsorization etc.
Advanced statistical methodologies
Stratified sampling, interaction detection, heterogeneous treatment effects, Benjamini Hochberg procedure etc.
High level of metric flexibility
Percentile, ratio, first/latest value, capped, and more available out-of-the-box
Experiment summaries and reports
Consolidate all experiment details, export, and share as a PDF
Team-based experiment templates
Create and enforce templates at the team or organization level
In-platform collaboration
Support for team collaboration and discussions within the console
Meta-analysis views
Generate meta-level insights from your corpus of experiments
Knowledge base
Maintain a searchable repository of experiment learnings
Built-in product analytics
Dive deeper into metrics and continuously develop new ideas to test
Warehouse native
Native support for popular data warehouses.
Snowflake Support
Support for Snowflake data warehouse
Bigquery Support
Support for Bigquery data warehouse
Redshift Support
Support for Redshift data warehouse
Databricks Support
Support for Databricks data warehouse
Athena Support
Support for Athena data warehouse
Define Metrics with SQL Queries
Ability to define metrics using SQL queries
Cloud deployment option
Support for both cloud and warehouse native deploymnet
* This comparison data is based on research conducted in July 2025.
Loved by customers at every stage of growth
See what our users have to say about building with Statsig
"Statsig's experimentation capabilities stand apart from other platforms we've evaluated. The ease of use, simplicity of integration help us efficiently get insight from every experiment we run. Statsig's infrastructure and experimentation workflows have also been crucial in helping us scale to hundreds of experiments across hundreds of millions of users."
Paul Ellwood
Head of Data Engineering
"We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration. We wanted a complete solution rather than a partial one, including everything from the stats engine to data ingestion."
Don Browning
SVP, Data & Platform Engineering
"Excited to bring Statsig to Whatnot! We finally found a product that moves just as fast as we do and have been super impressed with how closely our teams collaborate."
Rami Khalaf
Product Engineering Manager
"Statsig has enabled us to quickly understand the impact of the features we ship."
Shannon Priem
Lead PM
"I know that we are able to impact our key business metrics in a positive way with Statsig. We are definitely heading in the right direction with Statsig."
Partha Sarathi
Director of Engineering
"Working with the Statsig team feels like we're working with a team within our own company."
Jeff To
Engineering Manager
"[Statsig] enables shipping software 10x faster, each feature can be in production from day 0 and no big bang releases are needed."
Matteo Hertel
Founder
"Statsig has been an amazing collaborator as we've scaled. Our product and engineering team have worked on everything from advanced release management to custom workflows to new experimentation features. The Statsig team is fast and incredibly focused on customer needs - mirroring OpenAI so much that they feel like an extension of our team."
Chris Beaumont
Data Scientist
"The ability to easily slice test results by different dimensions has enabled Product Managers to self-serve and uncover valuable insights."
Preethi Ramani
Chief Product Officer
"We decreased our average time to decision made for A/B tests by 7 days compared to our in-house platform."
Berengere Pohr
Team Lead - Experimentation
"Statsig is a powerful tool for experimentation that helped us go from 0 to 1."
Brooks Taylor
Data Science Lead
"We've processed over a billion events in the past year and gained amazing insights about our users using Statsig's analytics."
Ahmed Muneeb
Co-founder & CTO
"Leveraging experimentation with Statsig helped us reach profitability for the first time in our 16-year history."
Zachary Zaranka
Director of Product
"Statsig enabled us to test our ideas rather than rely on guesswork. This unlocked new learnings and wins for the team."
David Sepulveda
Head of Data
"Brex's mission is to help businesses move fast. Statsig is now helping our engineers move fast. It has been a game changer to automate the manual lift typical to running experiments and has helped product teams ship the right features to their users quickly."
Karandeep Anand
President
"We only had so many analysts. Statsig provided the necessary tools to remove the bottleneck. I know that we are able to impact our key business metrics in a positive way with Statsig. We are definitely heading in the right direction with Statsig."
Partha Sarathi
Director of Engineering
"Statsig has been a game changer for how we combine product development and A/B testing. It's made it a breeze to implement experiments with complex targeting logic and feel confident that we're getting back trusted results. It's the first commercially available A/B testing tool that feels like it was built by people who really get product experimentation."
Joel Witten
Head of Data
"We realized that Statsig was investing in the right areas that will benefit us in the long-term."
Omar Guenena
Engineering Manager
"Having a dedicated Slack channel and support was really helpful for ramping up quickly."
Michael Sheldon
Head of Data
"Statsig takes away all the pre-work of doing experiments. It's really easy to setup, also it does all the analysis."
Elaine Tiburske
Data Scientist
"We thought we didn't have the resources for an A/B testing framework, but Statsig made it achievable for a small team."
Paul Frazee
CTO
"We use Statsig's analytics to bring rigor to the decision-making process across every team at Wizehire."
Nick Carneiro
CTO
"We've successfully launched over 600 features behind Statsig feature flags, enabling us to ship at an impressive pace with confidence."
Wendy Jiao
Staff Software Engineer
"We chose Statsig because it offers a complete solution, from basic gradual rollouts to advanced experimentation techniques."
Carlos Augusto Zorrilla
Product Analytics Lead
"We have around 25 dashboards that have been built in Statsig, with about a third being built by non-technical stakeholders."
Alessio Maffeis
Engineering Manager
"Statsig beats any other tool in the market. Experimentation serves as the gateway to gaining a deeper understanding of our customers."
Toney Wen
Co-founder & CTO
"We finally had a tool we could rely on, and which enabled us to gather data intelligently."
Michael Koch
Engineering Manager
"At Notion, we're continuously learning what our users value and want every team to run experiments to learn more. It's also critical to maintain speed as a habit. Statsig's experimentation platform enables both this speed and learning for us."
Mengying Li
Data Science Manager
"At OpenAI, we want to iterate as fast as possible. Statsig enables us to grow, scale, and learn efficiently. Integrating experimentation with product analytics and feature flagging has been crucial for quickly understanding and addressing our users' top priorities."
Dave Cummings
Engineering Manager, ChatGPT
"Statsig has helped accelerate the speed at which we release new features. It enables us to launch new features quickly & turn every release into an A/B test."
Andy Glover
Engineer
"We knew upon seeing Statsig's user interface that it was something a lot of teams could use."
Laura Spencer
Chief of Staff
"The beauty is that Statsig allows us to both run experiments, but also track the impact of feature releases."
Evelina Achilli
Product Growth Manager
"Statsig is my most recommended product for PMs."
Erez Naveh
VP of Product
"Statsig helps us identify where we can have the most impact and quickly iterate on those areas."
John Lahr
Growth Product Manager
"With Warehouse Native, we add things on the fly, so if you mess up something during set up, there aren't any consequences."
Jared Bauman
Engineering Manager - Core ML
"In my decades of experience working with vendors, Statsig is one of the best."
Laura Spencer
Technical Program Manager
"Statsig is a one-stop shop for product, engineering, and data teams to come together."
Duncan Wang
Manager - Data Analytics & Experimentation
"Engineers started to realize: I can measure the magnitude of change in user behavior that happened because of something I did!"
Todd Rudak
Director, Data Science & Product Analytics
"For every feature we launch, Statsig saves us about 3-5 days of extra work."
Rafael Blay
Data Scientist
"I appreciate how easy it is to set up experiments and have all our business metrics in one place."
Paulo Mann
Senior Product Manager