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Statsig vs. Optimizely
While Optimizely has shifted toward marketing‑suite priorities, Statsig remains purpose‑built for product experimentation with transparent stats methodologies and scalable, full‑stack infrastructure.
Statsig's key advantages over Optimizely are:
Transparent, trustworthy stats
Warehouse-native experimentation + flexible metrics
Full stack experiment infrastructure for product teams
Integration of analytics with experiments and flags
First-class support from the team building the product
Key Differences
01
Transparent, trustworthy stats
Statsig delivers experiment results you can trust, with complete transparency into the statistical methods behind every number. Our platform supports advanced techniques like CUPED, CURE, stratified sampling, and switchbacks, and gives you full diagnostic tools to validate test setup and rollout. Unlike Optimizely’s black‑box stats engine, Statsig makes it easy to reproduce results in‑house and gain confidence in every experiment readout.
02
Warehouse-native experimentation + flexible metrics
Statsig runs directly on your product data in your warehouse—Snowflake, BigQuery, Redshift, Databricks, or Athena—so you can define metrics once and use them everywhere. This warehouse‑native approach lets you measure downstream product and business impact without duplicating events across platforms. With a flexible metrics catalog, support for any aggregation type, and custom unit types, you can measure exactly what matters for your product.
03
Full stack experiment infrastructure for product teams
Statsig is built for experimentation across your entire product stack—frontend, backend, mobile, and services. Our platform includes server‑side support, remote evaluation, and robust identity resolution to track users across devices and surfaces. With features like global holdouts, logstream diagnostics, RBAC governance, and cross‑surface tracking, you have the infrastructure to scale experimentation across teams while keeping quality and consistency high.
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 Session Replay to see exactly how each user is experiencing the product, with Flag and Test groups searchable in each session.
05
First-class support from the team building the product
Statsig doesn’t treat support as a separate function. Our engineers, PMs, designers, and data scientists are the ones answering questions, hopping on Slack, and jumping into debugging sessions. We stay close to customers by design—ensuring fast answers, deep context, and real product influence with every interaction.
Feature Comparison
Basic Experimentation
The basic features you need to test and 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
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
Bonferroni correction
Adjust significance threshold to account for multiple tests
CUPED
Reduce experiment variance using pre‑experiment data
Winsorization
Limit extreme values to reduce the impact of outliers
In-platform collaboration
Support for team collaboration and discussions within the console
Advanced Experimentation
Advanced features for more complex experimentation needs.
Sequential testing
Method to prevent early-peeking on A/B test results
Switchback tests
Testing method when traditional A/B testing is not possible due to implementation or Network effects
Multi-armed bandit
Explore and Exploit models for optimization
Stratified sampling
Group population into strata to improve estimate accuracy
Interaction detection
Identify when two variables jointly affect an outcome
Heterogeneous treatment effects
Measure how treatment impact varies across different groups
Benjamini Hochberg procedure
Control false discovery rate when running multiple tests
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
High level of metric flexibility
Percentile, ratio, first/latest value, capped, and more available out-of-the-box
Geo-based experiments
Measure the incremental impact from marketing initiatives
Feature Flagging Platform
Comprehensive features for flag management.
Basic Feature Flags
Basic feature flag support
Percentage rollouts
Gradually release a feature to a set percentage of users
Multi-environment support
Support across multiple environments: dev, staging, prod
Edge SDKs
Run feature logic and experiments directly at the network edge
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
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
Feature flag lifecycle management
Unified cross-environment view, stale flag alerts and code reference checks
In-product collaboration
Support for team collaboration and discussions within the console
Flexible user targeting
Attribute-based, segment-based, environment-based, and custom rules
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 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