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Statsig vs. Optimizely
For teams with non-web based use cases (product, infra, etc), a website optimization platform like Optimizely may not suit their needs. Product optimization is a first-class product with Statsig.
Statsig's key advantages over Optimizely 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 Optimizely both offer product building platforms with Feature Flags and Experimentation.
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.
03
Easy analysis—not just for data scientists
Build a culture or experimentation by making data more accessible. Empower team members with modern UI, collaboration tools, and no-code customer queries. Double-click on surprising results, create new Custom Metrics to track more granular results in future launches, or pin charts to your own Dashboards to check in on daily- all within a single platform.
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
Bayesian
Support for Bayesian experimentation methods
Frequentist
Support for Frequentist experimentation methods
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
Non-inferiority Tests
Tests to show a treatment is not worse than a control
Flag & Experiment Platform
Comprehensive features for flag and experiment management.
Basic Feature Flags
Basic feature flag support
Unlimited Free Feature Flags
Unlimited free feature flags
Unlimited Seats
Support for unlimited seats
Unlimited MAU
Support for unlimited MAU
Percentage Rollouts
Support for percentage rollouts
Environments
Support for multiple environments (dev, staging, prod)
Edge SDKs
Support for edge SDKs
Dynamic Configs
Support for dynamic configurations
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
* This comparison data is based on research that was 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