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Statsig vs. Optimizely: Why Modern Enterprises Are Choosing Statsig for Experimentation
Optimizely has been the default for experimentation in large organizations until recently. But modern enterprise teams are moving onto more advanced requirements, and are shifting to modern platforms like Statsig. Here are six reasons why.
1. Transparency in Statistical Methods
A/B testing drives product roadmaps and success. When a statistics engine is a “black box,” it’s harder to trust experimental results — especially subtle differences that hinge on small details like variance reduction, sampling methods, or how p-values are calculated.
Optimizely has a proprietary, largely opaque stats engine. Many data scientists complain that they cannot replicate or easily audit the math behind Optimizely’s significance calculations. In contrast, Statsig’s experimentation framework is transparent, well-documented, and agnes with industry best practices.
With Statsig, users can choose from Bayesian or frequentist approaches, see exactly how confidence intervals are derived, and crucially replicate the analysis if they want to, and thoroughly investigate any surprising results. Our platform supports advanced techniques like CUPED, Stratified Sampling, Switchback Testing, out of the box, with completely visible SQL, and clearly explaining how the correction is applied. This transparency fosters trust across product, data, and executive teams.
2. Test Any Metrics, Not Just Conversions
Optimizely’s roots are in web-focused marketing experiments like testing call-to-action buttons, page layouts, or ad campaigns. Many of its metrics and templates are oriented toward conversion events, often making it cumbersome to measure product-specific outcomes such as multi-step engagement funnels, retention, or session behaviors.
Statsig, founded by ex-Facebook engineers, was built for modern product development from day one. It integrates feature flags, analytics, and experimentation into a single workflow. Teams can define or adjust metrics on the fly or even after the experiment has started. Because Statsig captures raw product events in a warehouse-native manner, organizations can analyze everything from high-level revenue conversions down to granular user flows. It’s akin to choosing a flexible car that can drive on any road, whereas Optimizely can feel like a trolley locked to a particular track.
3. Strong Support for Feature Tests and Developer Workflows
Optimizely does offer a “full-stack” product, but historically it’s been geared more toward front-end changes. Many engineering teams find that building server-side or code-driven experiments in Optimizely is cumbersome, especially if they need to quickly iterate or run concurrent tests across multiple services.
Statsig’s approach is deeply developer-centric. Feature flags, experiment assignment, and analytics logic can all live in code (or be configured in an intuitive UI that syncs with code repositories). This makes it straightforward to test new product features at scale. Engineers aren’t forced into “visual editing” tools that are helpful for marketing but slow for code-driven releases. Companies like Atlassian and OpenAI have adopted Statsig precisely because it slips neatly into modern CI/CD pipelines, lowering friction in shipping and testing new features.
4. Predictable Pricing and Lower Total Cost
Optimizely’s pricing is famously opaque. Enterprises often pay six-figure annual contracts, with cost surprises if they exceed certain experiment or user limits. Smaller teams or new divisions within a large company can feel priced out.
Statsig, by contrast, publicizes a usage-based model that starts free for millions of events each month and scales predictably. There’s no per-seat fee. A large enterprise can open the platform to many engineers, data scientists, and product managers without adding extra costs. In effect, Statsig encourages organization-wide experimentation, while Optimizely’s licensing structure can discourage that.
5. Real-Time Support and Customer-Centric Development
Optimizely has enterprise-level support, but some users report feeling like “just another ticket.” Statsig’s approach is notably more hands-on. Enterprise customers get direct Slack channels with Statsig’s engineering team, who help troubleshoot, advise on experiment design, and even build new features in response to specific needs. This close collaboration can be a game changer in fast-moving product organizations.
Several case studies — particularly from HelloFresh and Figma — highlight how Statsig’s engineers worked directly with them to roll out new capabilities like warehouse-native experimentation.
6. Warehouse-Native Implementation for Full Data Control
Modern data teams increasingly rely on centralized data warehouses like Snowflake, BigQuery, or Redshift to power analytics and decision-making. But most experimentation platforms, including Optimizely, require sending data out to their systems for analysis — creating data silos, introducing latency, and limiting flexibility.
Statsig takes a fundamentally different approach. With warehouse-native experimentation, you can run experiments where your data already lives. This means:
No need to duplicate or pipe data into external tools
Analysts can query experiment results directly alongside product, revenue, or marketing data
You retain complete ownership of event data and metrics logic
Auditability and governance are dramatically improved
Statsig’s Warehouse Native product lets teams export assignment logs, metric definitions, and statistical outputs into their own environment. It's fully transparent and SQL-accessible, empowering data scientists to customize analyses, build bespoke dashboards, or even re-run historical experiments on new metrics — all without being locked into a proprietary UI.
For modern enterprises that prioritize data control, flexibility, and velocity, this native integration with your warehouse is a game-changer — and a key reason why leading teams are making the switch from Optimizely.
Summary
For many marketing-driven teams, Optimizely’s visual editor and digital experience suite can still be a solid fit. But for modern enterprise customers who want to run robust product-led experiments, maintain full control over their data, and trust that their A/B results are statistically sound, Statsig’s platform offers compelling advantages:
Transparent statistics
Flexible, product-oriented metrics and analytics
Seamless integration for feature flagging and code-driven tests
Predictable pricing that scales with usage
Hands-on support from an engineering-led team
Warehouse-native implementation
These benefits, backed by the experiences of organizations like OpenAI, Figma, Notion, and Atlassian, explain why so many enterprises today are choosing Statsig over Optimizely.
References
The top 4 Optimizely alternatives for experimentation | Statsig
How Statsig Grows: The Story, Growth Motion, Product, and Market
Note: External sources mentioned above include user reviews, vendor documentation, and independent industry data.