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Statsig vs. Split
Split is a legacy feature flagging solution, but many users are looking for an advanced platform with modern experimentation, analytics, and more.
Statsig's key advantages over Split are:
Most advanced experimentation, trusted by OpenAI & Notion
'Unscalable' customer obsession & support
Product optimization as a first-class product
Better for teams
Warehouse-Native Experimentation & Analytics
Key Differences
Statsig and Split both offer feature flagging and experimentation platforms that help builders ship better products.
01
Most advanced experimentation, Trusted by OpenAI & Notion
Statsig's experimentation, based on best practices from Facebook, is trusted as the most advanced and battle-tested experimentation platform available. With functionality like CUPED, Meta Analysis, A/A Testing, Stratified Sampling and more, Statsig has all of the bells and whistles for those who need them.
02
'Unscalable' customer obsession & support
Rather than a regular support system with tickets and SLAs, support is the responsibility of the whole company, with a community Slack channel free for everyone, and dedicated Slack channels included for each Enterprise customer.
03
Product optimization as a first-class product
We've prioritized tooling to analyze user behavior and iteratively improve products as much as tooling to ship features throughout our entire experience. Our world-class experimentation and product analytics solutions are unparalleled in their feature sets, and are a fraction of the cost of competitors.
04
Better for teams
One of our core tenets is that we believe building products is a team sport. This is why we don’t charge based on seats: we believe everyone in the company should be in the Statsig Console, looking at dashboards, playing with data, and opting themselves in and out of new features to ensure they’re experiencing beta versions of the product.
05
Warehouse-Native experimentation & analytics
Using Warehouse Native Statsig means that you can define the metrics that back your experiments, feature flags and product analytics (beta) directly on top of your warehouse data, with support for Snowflake, Bigquery, Redshift, Databricks, and Athena. You can also use Statsig's infrastructure to track events, metrics and exposures, and store them in your own warehouse.
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
Out-of-the-box collaboration features in 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 that was conducted in June 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