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Migrate from VWO Data360 to Statsig
Enterprise teams running VWO Data360 face a critical decision: stay with a platform that charges per user and locks data behind proprietary interfaces, or migrate to modern infrastructure. The answer becomes clear when you examine the architectural differences. VWO's modular approach fragments your data across separate products - testing, insights, and personalization each require their own implementation.
Statsig emerged from Facebook's internal experimentation tools to solve this exact problem. The platform processes over 1 trillion events daily while giving teams complete visibility into their data through warehouse-native deployment and transparent SQL queries.
Company backgrounds and platform overview
VWO started as a website optimization tool, gradually adding modules to become a digital experience platform. The company serves enterprise clients like Amazon and Disney, but its architecture reflects those incremental additions - each product operates independently with separate data silos.
Statsig took a different path. Founder Vijaye Raji assembled a team in 2020 specifically to recreate Facebook's experimentation infrastructure for external companies. The result is a unified platform where experimentation, feature flags, analytics, and session replay share the same data foundation. This integration eliminates the reconciliation headaches that plague modular systems.
The technical implications are significant. VWO runs exclusively on Google Cloud Platform, requiring all customer data to flow through their servers. Statsig offers both hosted and warehouse-native options - you can run the entire platform within your own Snowflake, BigQuery, or Databricks environment while maintaining full functionality.
Scale tells the real story. VWO handles typical enterprise volumes adequately, but performance degrades with extreme traffic. Statsig currently processes over 1 trillion events daily across customers like OpenAI and Microsoft. The infrastructure difference becomes apparent when you need billions of flag evaluations or real-time analysis of massive datasets.
Feature and capability deep dive
Experimentation capabilities
VWO provides A/B testing through a visual editor - the classic approach where marketers create variations by clicking and dragging. It works well for simple tests, but technical teams quickly hit limitations. Complex experiments require workarounds or custom code that defeats the purpose of visual editing.
Statsig brings statistical rigor from day one. CUPED variance reduction accelerates decision-making by 30-50% on average. Sequential testing lets you peek at results without invalidating statistics. Most importantly, the warehouse-native option means experiments run directly in your data infrastructure - no data leaves your environment.
Reddit discussions comparing platforms consistently highlight this architectural difference. Teams handling sensitive data can't use cloud-only platforms like VWO. Financial services, healthcare, and government contractors need complete data sovereignty that only warehouse-native deployment provides.
Analytics and reporting
VWO Data360 offers custom tracking and visitor profiles - standard analytics features wrapped in a proprietary interface. The real limitation appears when you need to validate calculations or build custom analyses. You're stuck with whatever VWO decides to show you.
Statsig takes transparency seriously. Every metric calculation exposes its underlying SQL with one click. You see exactly how numbers are computed, can verify statistical methods, and build custom queries on top of the raw data. The unified metrics catalog ensures consistency - the same metric definition works across experiments, feature flags, and dashboards.
"Implementing on our CDN edge and in our nextjs app was straight-forward and seamless," noted one Statsig user on G2.
Processing capacity reveals another gap. Statsig handles 1 trillion+ daily events with sub-second query times. VWO's infrastructure works for typical volumes but struggles when you need real-time analysis of high-frequency events. Game developers, social platforms, and streaming services often discover these limits only after implementation.
Developer experience
Both platforms offer SDKs, but the implementation philosophy differs completely. VWO assumes developers will integrate once and hand control to marketers. Statsig recognizes that modern product development requires continuous collaboration between technical and business teams.
The evidence shows up in the details:
30+ open-source SDKs from Statsig versus closed-source options from VWO
Edge computing support for global deployments
Native integration with modern development workflows
Direct access to raw experiment data
VWO's 30-day data retention on lower tiers forces artificial constraints on analysis. Statsig provides unlimited retention - you own your data and can analyze historical experiments years later. This matters when you need to understand long-term impact or validate earlier decisions.
Pricing models and cost analysis
Pricing structure comparison
VWO charges based on monthly tracked users (MTUs), then adds fees for each product module. A typical enterprise needs VWO Testing, Data360, and Personalization - three separate line items on your invoice. The exact costs remain hidden behind sales conversations, making budget planning difficult.
Statsig uses transparent event-based pricing. You pay for what you track, not how many users visit. Feature flags remain free at any scale - a critical difference for engineering teams. Here's what actually affects your bill:
Analytics events (with generous free tier)
Session replays (optional)
Warehouse compute (if using warehouse-native)
The math becomes clear at scale. VWO's pricing structure penalizes growth - more users mean higher costs regardless of actual usage. Statsig rewards efficiency through volume discounts beyond 20M events monthly.
Real-world cost scenarios
Consider a SaaS company with 100K monthly active users generating standard analytics events. VWO places this squarely in Enterprise territory with custom pricing. Add multiple products and you're looking at significant annual contracts before seeing any value.
The same company pays predictable rates with Statsig. At 100K MAU with typical event volumes, you'd likely stay within the free tier for experimentation while paying modest amounts for session replays. The bundled approach means one system to integrate, one vendor to manage, one invoice to process.
"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," said Don Browning, SVP at SoundCloud.
Feature flag platform costs highlight the starkest difference. A company running 1M daily flag checks saves thousands monthly by choosing Statsig's free flags over VWO's metered approach. Those savings compound when you factor in reduced integration complexity and unified data management.
Decision factors and implementation considerations
Migration complexity and time-to-value
Moving from VWO Data360 to Statsig requires planning, but the unified architecture simplifies the process. Instead of migrating multiple products separately, you implement one SDK that handles everything. Statsig customers report launching production experiments within days of starting integration.
The migration typically follows this pattern:
Install Statsig SDK alongside existing VWO implementation
Mirror key metrics and experiments in both systems
Validate data consistency and statistical calculations
Gradually shift traffic to Statsig
Sunset VWO once confidence is established
VWO's modular structure actually helps here - you can migrate products independently. Start with experimentation, add feature flags, then bring over analytics. Each step provides immediate value without requiring a massive cutover.
Support and documentation quality
VWO tiers support by pricing level: Starter plans wait 12 hours for email responses while Enterprise gets 4-hour turnaround. The model assumes enterprise customers need more help, but reality often proves opposite - technical teams want quick answers to specific questions.
Statsig provides direct Slack access to their engineering team for all customers. Response times average under 30 minutes during business hours. More importantly, the answers come from people who built the system, not tier-one support reading scripts.
Documentation philosophy reflects each company's approach. VWO treats implementation details as proprietary, limiting what customers can learn. Statsig exposes everything: statistical formulas, SQL queries, infrastructure diagrams. This transparency helps teams debug issues independently and build internal expertise.
Enterprise readiness and scale
Scale requirements separate platforms quickly. Statsig processes over 1 trillion events daily for customers like OpenAI and Microsoft. The infrastructure handles billions of users without performance degradation - critical for consumer applications and global platforms.
VWO serves traditional enterprise needs well but shows strain at extreme volumes. Users on Reddit report query timeouts and dashboard delays when event volumes spike. The architecture wasn't designed for modern data scales.
Warehouse-native deployment provides the ultimate differentiator for regulated industries. Financial services, healthcare, and government contractors can run Statsig entirely within their own infrastructure. VWO's cloud-only model simply can't meet these compliance requirements, eliminating them from consideration.
"The biggest benefit is having experimentation, feature flags, and analytics in one unified platform. It removes complexity and accelerates decision-making," said Sumeet Marwaha, Head of Data at Brex.
Security-conscious organizations particularly value Statsig's approach. Your data never leaves your environment, yet you get full platform capabilities including real-time experimentation and advanced statistics. This architectural advantage explains why Brex reduced time spent by data scientists by 50% after migrating.
Bottom line: why migrate from VWO Data360 to Statsig?
The decision comes down to architecture and economics. VWO Data360 locks your analytics behind proprietary interfaces while charging based on user counts. Statsig provides complete data transparency with SQL access and scales pricing based on actual usage.
Technical teams gain immediate advantages:
Unified metrics across experimentation and feature flags
One-click visibility into all calculations
Warehouse-native options for complete data control
Free feature flags at unlimited scale
OpenAI processes over 1 trillion events daily through Statsig while maintaining 99.99% uptime. This isn't theoretical scale - it's proven infrastructure handling real production workloads. VWO works well for traditional enterprise volumes but can't match this level of performance.
The unified platform eliminates the data reconciliation headaches that plague modular systems. Instead of correlating metrics across VWO Testing, Data360, and Personalization, everything shares the same foundation. Teams spend time analyzing results rather than debugging data discrepancies.
Cost predictability seals the deal for most organizations. Statsig's transparent pricing means no surprise invoices when you succeed and grow. Feature flags remain free forever, removing a major expense category entirely. The savings often pay for the entire platform compared to VWO's modular pricing.
Closing thoughts
Migrating from VWO Data360 to Statsig represents more than switching vendors - it's adopting modern experimentation infrastructure. The combination of warehouse-native deployment, transparent pricing, and unified architecture solves the fundamental limitations of legacy platforms.
For teams ready to explore migration, start with these resources:
Statsig's migration guide for technical implementation details
Customer case studies showing real migration outcomes
Free tier signup to test the platform with your own data
The path from VWO's modular approach to Statsig's unified platform is well-traveled by companies like OpenAI, Microsoft, and Brex. Each found that modern product development requires integrated tools, transparent data access, and infrastructure that scales with success.
Hope you find this useful!