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Statsig vs. LaunchDarkly
LaunchDarkly has led the feature flag industry for a decade. Those looking to do more than flip toggles may want to upgrade to a more modern platform.
Statsig's key advantages over LaunchDarkly are:
Modern platform with 30+ SDKs across every major stack
Metrics integrated into your launches from day one at no extra cost
Product optimization as a first-class product
Proven reliability, handling over 1 trillion events per day
Affordability: Most LaunchDarkly customers cut their bill in half upon switching
Key Differences
Statsig and LaunchDarkly both offer Feature Flagging and Experimentation platforms that help builders ship better products.
01
The Modern Platform for Feature Management and Experimentation
Statsig has redefined feature flagging with a modern, warehouse-native platform designed for today’s engineering teams. With 30+ SDKs for every major stack and native support for tools like Snowflake, BigQuery, Redshift, and Databricks, Statsig integrates seamlessly with your data warehouse for comprehensive control and measurement.
02
Integrated Metrics—Turn Any Feature Flag into an A/B Test
Why stop at just controlling rollouts? Statsig makes it effortless to turn any feature flag into an A/B test with built-in metrics—at no extra cost. Launch with confidence using advanced techniques like CUPED, Meta-Analysis, Stratified Sampling, and A/A Testing to measure impact and drive data-driven decisions from day one.
03
Trusted by the Fastest-Growing Startups
The fastest-growing companies, including OpenAI and Notion, trust Statsig for both feature management and experimentation. Built on industry best practices pioneered at Facebook, Statsig provides enterprise-grade capabilities with a focus on reliability, flexibility, and ease of use for teams of all sizes.
04
Built for Scale—Proven Infrastructure for Massive Workloads
Statsig’s infrastructure is designed to handle over 1 trillion events per day with enterprise-grade reliability and performance. Whether you’re running global experiments or managing complex rollouts, Statsig ensures uptime and scalability without compromising on speed or accuracy.
05
Unmatched Value with Usage-Based Pricing
Statsig delivers powerful experimentation and feature management at a fraction of the cost of competitors. With free access for self-serve teams and a predictable flat rate for enterprise, most LaunchDarkly customers cut their bill in half when they switch to Statsig—without sacrificing functionality or support.
Feature Comparison
Basic Experimentation
The basic features you need to measure feature impact.
Primary Metrics
Track core metric performance across variants
Experiment Templates
Pre-defined templates for experiments
Team-based Experiment Defaults
Default settings for teams in experiments
Bayesian
Support for Bayesian experimentation methods
Frequentist
Support for Frequentist experimentation methods
Recommended Run Times
Recommended durations for running experiments
Holdouts
Ability to create holdout groups not exposed to any experiment treatments
Mutually Exclusive Experiments
Ensure experiments do not interfere with each other
Cloud Hosted Option
Cloud hosted experimentation supported
Warehouse Native Experimentation
Support for experimentation directly in your data warehouse
No-code experiments
Create experiments without coding
Advanced Experimentation
Advanced features for more complex experimentation needs.
CUPED
Method to reduce experiment runtime and increase accuracy with historical data
Switchback Tests
Testing method when traditional A/B testing is not possible due to implementation or Network effects
Stratified Sampling
Assign experiment subjects intelligently across groups
Sequential Testing
Method to prevent early-peeking on A/B test results
Multi-armed Bandit
Explore and Exploit models for optimization
Winsorization
Reduce the influence of outliers
Bonferroni Correction
Adjust for multiple comparisons
A/A Tests
Run tests assessing if your Experimentation program is set up correctly
Flag & Experiment Platform
Comprehensive features for flag and experiment management.
Unlimited Seats
Support for unlimited seats
Unlimited MAU
Support for unlimited MAU
Basic Feature Flags
Basic feature flag support
Unlimited Free Feature Flags
Unlimited free feature flags
Percentage Rollouts
Support for percentage rollouts
Scheduled Rollouts
Support for scheduled rollouts
Environments
Support for multiple environments (dev, staging, prod)
Metric Alerts
Alerting for metrics
Flag Lifecycle Management
Manage the lifecycle of flags
In-Console Collaboration
Support for collaboration within the console
Approval Flows
Support for approval workflows
SDKs for all Major Languages
Support for SDKs in all major programming languages
Edge SDKs
Support for edge SDKs
No-code Dynamic Configs
Support for no-code dynamic configurations
Impact Measurement/Analyses
Measurement and analysis of impact
Feature Gate Rollout Analysis
Analysis of feature gate rollouts
Change Logs History with Revert
Change logs history with revert option
Warehouse Native Experimentation
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
Flexible Hybrid Cloud/Warehouse Solutions
Support for hybrid cloud and warehouse solutions
Compatibility with Other Assignment Sources
Compatible with other assignment sources
* This comparison data is based on research conducted in July 2024.
Why the best build with us
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.
OpenAI
Dave Cummings
Engineering Manager, ChatGPT
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.
Brex
Karandeep Anand
President
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.
Notion
Mengying Li
Data Science Manager
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.
SoundCloud
Don Browning
SVP, Data & Platform Engineering
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.
Ancestry
Partha Sarathi
Director of Engineering