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Statsig vs. Amplitude Experimentation
Amplitude has begun upselling many customers on Experimentation, but some might prefer the power and support of an Experimentation-first platform.
Statsig's key advantages over Amplitude Experimentation are:
Most advanced experimentation, trusted by OpenAI & Notion
'Unscalable' customer obsession & support
Industry-standard feature flagging support
Integration of analytics with experiments and flags
Warehouse-native experimentation & analytics
Key Differences
Statsig and Amplitude both offer product building platforms with Experimentation, Feature Flags, Analytics and more.
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. Additionally, the ability to define metrics on top of your own warehouse data opens up new possibilities for Experimentation.
02
'Unscalable' customer obsession & support
At Statsig, we view support as one of our core competencies. 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. Along with our dedicated customer-facing Enterprise Engineering team, our senior leaders regularly respond to customer inquiries. We also run a flexible sales process with full support for Proof-of-Concepts, and no artificial deadlines.
03
Industry-Standard Feature Flagging Support
Feature Flags are a first-class product on the Statsig Platform, rivaling the best available in the industry. We have full support for Feature Flag templates, automated rollouts, cohorts and more, plus the ability to treat any Flag like an A/B test with only one click.
04
Integration of Analytics with Experiments and Flags
Statsig Product Analytics are built to work with our Experiment and Feature Flag products, allowing you to use your flag/test 'Exposures' as analytical metrics, and breakdown other charts by flag and experiment exposure group. Plus, use our new Session Replay tooling to see exactly how each user is experiencing the product, with Flag and Test groups searchable in each session.
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
Unlimited Secondary Metrics
Track multiple secondary metrics without limits
Experiment Templates
Pre-defined templates for experiments
Team-based Experiment Defaults
Default settings for teams in experiments
Bayesian & Frequentist
Support for both Bayesian and Frequentist experimentation methods
Exportable Experiment Summaries
Share or save experiment summaries
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
Turn Feature Flags into Experiments
Convert feature flags into experiments
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
Meta Analysis
Analyze 'Meta' results across multiple experiments
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
Non-inferiority Tests
Tests to show a treatment is not worse than a control
Flag & Experiment Platform
Features for managing feature flags and experiments together.
Basic Feature Flags
Core feature flagging capabilities
Unlimited Free Feature Flags
No limit on the number of free feature flags
Percentage Rollouts
Gradual rollout of features by percentage
Scheduled Rollouts
Schedule feature rollouts for a specific time, date, or pass percentage
Environments
Differentiate event traffic by specified environments
Metric Alerts
Alerts based on metric changes in a flag group
Flag Lifecycle Management
Tracking the launching, disabling, and clean up of feature flags
Non-Boolean Values
Support for non-boolean flag values
Team-based Defaults
Default settings for teams in flag management
Local Evaluations
Evaluate flags locally on-device
Edge SDKs
Support for edge SDKs
Dynamic Configs
Hosted JSON files that can be changed without redeploying
Feature Flag Rollout Analysis
Analyze like an A/B test each rollout stage of a feature flag
Exposure Events
Track exposure events for flags/ AB tests
Some SDKs
Product Analytics on Experiment Groups
Analyze experiment groups with product analytics
EU-hosting
Support for hosting in the EU
Yes, with Warehouse Native
Warehouse Native Experimentation
Experimentation features built to work natively with data warehouses.
Snowflake Support
Support for Snowflake
Bigquery Support
Support for BigQuery
Redshift Support
Support for Redshift
Databricks Support
Support for Databricks
Athena Support
Support for Athena
Define Metrics with SQL Queries
Define metrics using SQL queries
Flexible Hybrid Cloud/Warehouse Solutions
Analysis only, end-to-end targeting, integrated offline experiments
Not for Experiments
Compatibility with Other Assignment Sources
Integrate with your existing assignment tooling
Customer Support
Customer support options and resources.
Flexible Sales Process
Customizable sales process to meet customer needs
Free Slack Channel for Everyone
No ticket system for support requests or questions
Enterprise Slack Channels
Direct line to Engineers, Data Scientists, and Product team
Extra Cost
Culture of Experimentation Co-Build
Collaborative support to build a culture of experimentation
Direct engineering & data science support
Access to live technical support
Enterprise Support SLA
Service level agreement for enterprise support customers
4 Hr
1 Day
Product Analytics
Features for analyzing product usage and performance.
Autocapture
Capture user events automatically
Dashboards
Customizable dashboards for analytics
Free Group Analytics
Analyze at company, account, or group level at no additional cost
Cohorts
Segment users into cohorts for analysis
Funnels
Analyze user flows through funnels
Retention Analysis
Analyze user retention over time
User Paths
Track the paths users take through your product
Correlation Analysis
Analyze correlations between metrics
Coming Soon
Lifecycle Analysis
Analyze user lifecycle stages
Coming Soon
Stickiness Insights
Insights into user stickiness and engagement
Coming Soon
Formulas
Custom formulas for metric calculations
Query Editor
Create and edit custom queries
Coming Soon
Unlimited Data Retention
How long we'll keep your data
Warehouse Native Product Analytics
Product analytics natively integrated with data warehouses
In Beta
Snowflake Only
* This comparison data is based on research 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