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Statsig vs. Posthog
Both are powerful, both have rave reviews, and many of the features overlap. Besides that cute hedgehog, what’s the difference?
Statsig's key advantages over Posthog are:
The all-in-one platform built to scale with you
Product optimization as a first-class product
'Unscalable' customer obsession
Better for teams
Your warehouse or ours
Key Differences
PostHog and Statsig both offer all-in-one platforms that help builders be more data-driven in how they develop products, ranging from product analytics and session replay to feature flagging and experimentation.
01
The all-in-one platform built to scale with you
Getting up and running on a platform quickly is incredibly valuable. Once your product is growing, the last thing you want to do is have to re-platform and swap out the tooling stack your team has come to know and love. Statsig powers companies ranging from startups watching individual user sessions as they roll out their first beta through to the likes of OpenAI, Flipkart, and Atlassian running experiments with millions of enrolled users.
02
Product optimization as a first-class product
We've prioritized tooling to ship and iteratively improve products as much as tooling to analyze user behavior throughout our entire experience. Our world-class feature flagging and experimentation solutions are unparalleled in their feature sets, and are a fraction of the cost of competitors.
03
'Unscalable' customer obsession
At Statsig, we view support as an entirely different ballgame. We don’t have a regular customer support team. We have a Customer Engineering team. Our engineers, product managers, data scientists, and designers are our customer support team. Everyone at the company participates in Slack support, hops on calls with customers, and interacts with our users daily.
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
Your warehouse or ours
A major Statsig differentiator is our first-class support for two different deployment models: Cloud and Warehouse Native. Already started building your tech stack on Snowflake or GCP? No problem. Statsig Warehouse Native offers all the power of Statsig, sitting within your Warehouse on top of your canonical metrics catalog.
Feature Comparison
Platform
Both Statsig and PostHog offer robust platform features, with a few notable differences between the two platforms.
A/B testing
Test changes and analyze impact
Feature flags
Roll out features safely; toggle features for cohorts or individuals
Product analytics
Track events and conversion; analyze user behavior
Dynamic config
Replace hard-coded values in your app with config values
Session replays
Watch real users use your product; debug behavior
User surveys
Ask users for qualitative feedback and gather responses
Heatmaps
Visualize the most interacted parts of your application
Notebooks
Collaborate on analysis in shareable notebooks
Open source
Code publicly accessible
Web Analytics
Auto-capture key website engagement and performance events
Low/no-code Experimentation
For less technical teams to run experiments in tandem with Eng/Product teams
Warehouse Native Option
Use our warehouse or bring your own
Data warehouse experiments
Run A/B tests natively on data in your warehouse
Dog-Driven Product Development
Secretly run by a team of very very good doggos
Experimentation
Both Statsig and Posthog enable A/B/n testing at all levels of the stack. A few differences to note:
- Statsig’s Stats Engine offers advanced statistical methods such as pre-experiment bias detection, sequential testing, and Bonferroni correction that help you run more accurate experiments on smaller sample sizes, faster. You can also leverage things like long-term holdouts that enable you to capture cumulative effects across multiple experiments on a population over time.
- Posthog offers a basic experimentation solution with significantly fewer stats corrections to ensure accuracy and speed of experimentation. Posthog experiments are also built on top of their feature flagging infrastructure which requires creation of a feature flag first to power each experiment.
Custom goals
Customize metrics that a test tracks
Secondary metrics
Monitor impact on unrelated metrics
Statistical significance calculation
Calculate if changes make a statistically significant impact
Split testing
Split participants into groups
Multivariate (A/B/n) testing
Test multiple variants of a change
Recommended run time
Calculate the recommended run time for your experiments
Statistics engine
How the results of an experiment are calculated
Bayesian, Frequentist
Bayesian
Holdout testing
Withhold multiple features to measure cumulative impact
Partial
Multi-armed bandit
Optimize tests automatically by allocating traffic to the best performing variant.
Mutually exclusive experiments
Isolate user groups for simultaneous, independent experiments
Bonferroni correction
Includes a correction when tests are being performed simultaneously
Advanced Experimentation
Switchback tests, stratified sampling
Exportable Experiment Summary
In-Console & exportable PDF summarizing experiment performance & takeaways
Meta-Analysis
Analyze velocity and topline metric impact across all your experiments
Segments of Interest
Automatically identify cohorts with anomalous behavior in feature rollouts and experiments
Experiment Templates
Codify best practices and standardize experiment setup
Custom Queries
Analyze experiment or feature roll-out lifts on-demand
Feature Flags
Both Statsig and Posthog offer robust feature flagging solutions, with a few notable differences between the two platforms:
- Statsig supports boolean flags via our Feature Gates product, and non-boolean flags via Dynamic Configs. We also offer the ability to easily monitor and alert on any metric regressions with every progressive rollout. Not monitoring any regression metrics? Then your gate exposures are free.
- Posthog combines both boolean and non-boolean flags under their core feature flagging product, which is billed by request volume. To catch any metric regressions with your feature rollout, you will need to convert your flag into an experiment.
Free flags
Free feature flags if no regression metrics tracked
Boolean flags
Simple flags returning true or false
Multivariate flags
Flags with multiple customizable values
Payloads
Flags with string, number, or JSON payloads
Percentage rollouts
Target percentages of a group
Custom targeting
Target users based on user properties, custom contexts
Scheduling
Schedule flags to turn on or off
Partial
Environments
Manage flags for dev, staging, prod
Bootstrapping
Flags available on frontend application load
Early access management
Manage betas, test features
Customizable Environments
Create and configure custom environment tiers
Regression Metrics
Measure and alert on key metrics being regressed by a new feature rollout
Metric Alerts
Configure threshold-based alerts for new feature and experiment rollouts
Flag Lifecycle Management
Automated alerts for stale and fully rolled out flags
Target Applications
Optimize SDK initialize payload by specifying which keys flags are targeted at
Product Analytics
Both Statsig and PostHog offer full-featured product analytics designed with the entire product and engineering team in mind. Statsig product analytics also integrates with our Feature Flag, Experimentation, and Session Replay products to let teams leverage product data through the entire development lifecycle.
Warehouse Native Product Analytics
Perform product analytics natively on data in your existing warehouse
Autocapture
Capture events without manual logging
Dashboards
Combine insights into shareable dashboards
Graphs and trends
Build custom insights and visualizations
Free Group Analytics
Aggregate events based on entities such as companies and organizations
Cohorts
Group and analyze users based on shared properties and behaviors
Funnels
Track users through a sequence of events
Retention analysis
Visualize which users stay, for how long
User paths
Track user flows and where they drop-off
Correlation analysis
Suggested events and properties that lead to success or failure
Coming Soon
Lifecycle analysis
Understand who is dormant, churning, and thriving
Coming Soon
Stickiness insights
See how many times users perform an event in a period of time
Coming Soon
Formulas
Use custom formulas to calculate unique insights
Unlimited data retention
Store your product analytics data, forever
Integrations
Both Statsig and Posthog support integrations that enable easy data import and export from third-party platforms. Statsig also supports integrations with platforms like Segment and Google Analytics, as well as CDNs like Cloudflare and Fastly.
Imports
Import data from source
Exports
Export data to other sources
Zapier
Trigger Zapier automations
Sentry
Connect to Sentry data
Datadog
Capture flag data in Datadog
Slack
Alerts for Slack
Microsoft Teams
Alerts for Microsoft Teams
Segment
Send data to Segment, receive data from Segment
Google Analytics
Send logs collected on platform to Google Analytics
Cloudflare
Save config specs into Cloudflare Workers KV
Fastly
Save config specs into Fastly
Webhook
Send logs collected on platform to anywhere
Vercel Edge Config
Save config specs into Vercel Edge Config
Github Code References
Find code references to flags within your Github projects
Security, Compliance, and Controls
While both Statsig and Posthog have built-in tablestakes privacy and compliance features, Statsig exposes more flexible permissions via features like role-based access controls, team configuration, and native change approval flows.
User privacy options
Anonymize users, drop personal data
History, audit logs
Manage and view flag edits and related users
GDPR-ready
Can be compliant with GDPR
HIPAA-ready
Can be compliant with HIPAA
SOC 2
Soc 2 security certification
2FA
Enforce login with two-factor authentication
SAML/SSO
Use SAML or single sign-on authentication
Approvals
Require approvals to change flags
Permissioning
Control who can edit and modify flags
Role Based Access Controls
Create and configure custom environment tiers
Teams
Create and configure custom environment tiers
Experiment Policy
Enforce standard configurations for experiments project-wide
Gate Settings
Configure standard feature rollout settings
Change Reviews
Require reviews for feature or experiment changes before landing in production
Templates
Codify best practices and standardize config setup
* 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