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An alternative to Eppo without SQL requirements: Statsig
Your data team just told you it'll take three weeks to set up A/B testing infrastructure. Sound familiar? For many companies, the promise of data-driven experimentation crashes into the reality of SQL requirements, data pipeline complexity, and engineering bottlenecks.
This creates a fundamental tension: product teams need to move fast, but experimentation platforms often demand technical expertise that slows them down. The choice between Statsig and Eppo illustrates this divide perfectly - one platform democratizes testing for everyone, while the other doubles down on SQL-first architecture.
Company backgrounds and platform overview
Vijaye Raji built Facebook's experimentation tools like Deltoid and Scuba before founding Statsig in 2020. He saw how Facebook's internal platforms enabled thousands of experiments - and realized most companies lacked access to similar capabilities. Statsig brings that same infrastructure to everyone.
Eppo takes the opposite approach. They built exclusively for data teams who write SQL daily. You need existing warehouse infrastructure before you can even start. No warehouse? No experiments.
The deployment options reveal each platform's priorities. Statsig offers both hosted cloud and warehouse-native options - teams choose based on their needs and technical capabilities. Start with hosted deployment, then migrate to warehouse-native as you scale. Eppo only provides warehouse-native deployment. There's no quick-start option.
This philosophical split affects everything downstream. Statsig enables PMs, designers, and marketers to run experiments without engineering support. One G2 reviewer noted how "the clear distinction between different concepts like events and metrics enables teams to learn and adopt the industry-leading ways of running experiments" - highlighting how the platform teaches best practices rather than assuming expertise.
Meanwhile, Eppo's SQL-centric approach means every experiment requires data team involvement. Simple A/B tests become multi-week projects as teams coordinate metric definitions, write queries, and debug pipeline issues. The tradeoff: more control for data teams, but significant friction for everyone else.
Feature and capability deep dive
Core experimentation capabilities
Both platforms deliver the statistical rigor you'd expect: CUPED variance reduction, sequential testing, and Bayesian analysis come standard. But implementation differs dramatically. Statsig provides 30+ SDKs that work immediately - install the SDK, wrap your feature in a flag, and start experimenting. Eppo requires SQL configuration through your warehouse before anything works.
The bundling strategy shows another key difference. Statsig includes unlimited feature flags across all tiers; Eppo charges separately for feature management. This matters when you're scaling experimentation programs - Statsig customers run hundreds of experiments monthly without worrying about flag limits.
Statsig processes over 1 trillion daily events with sub-millisecond latency. The platform handles this scale while maintaining real-time capabilities - experiments update instantly, metrics flow in continuously, and alerts fire immediately when things go wrong. Eppo's warehouse-native approach keeps data in your infrastructure but sacrifices real-time feedback. You're trading control for speed.
Both support advanced experimental designs like switchback testing and stratified sampling. The difference lies in accessibility: Statsig's visual interfaces guide users through complex setups, while Eppo requires SQL expertise to configure anything beyond basic A/B tests.
Analytics and reporting functionality
The analytics experience crystallizes each platform's target audience. Statsig built self-serve analytics that non-technical users navigate independently. Product managers create dashboards, define conversion funnels, and analyze user journeys without writing a single SQL query. The platform teaches experimentation best practices through its interface design.
Rose Wang, COO at Bluesky, explained: "Statsig's powerful product analytics enables us to prioritize growth efforts and make better product choices during our exponential growth with a small team." They reached 25 million users while keeping their data team lean - possible because non-technical team members handled their own analysis.
Eppo assumes SQL proficiency for most tasks. Want to analyze funnel conversions? Write a query. Need to segment users? More SQL. This approach works well for data-heavy organizations with dedicated analytics engineers. But it creates dependencies: product teams wait for data teams to answer basic questions.
Both platforms integrate with major warehouses:
Snowflake
BigQuery
Databricks
Redshift
The key difference: Statsig's hosted option processes data without touching your warehouse, while the warehouse-native option keeps everything in your infrastructure. You choose based on privacy requirements and technical resources. Eppo only offers the warehouse path.
Developer experience and implementation
Speed matters when shipping experiments. Statsig teams typically launch their first test within hours - the platform handles infrastructure complexity automatically. Developers appreciate the zero-latency performance and automatic rollback capabilities when metrics degrade.
The SDK ecosystem covers every major platform with open-source implementations. Edge computing support means experiments work at CDN scale. Feature flags activate instantly without additional configuration. Reddit discussions about A/B test infrastructure consistently highlight the complexity of building these capabilities - complexity that Statsig abstracts away.
Eppo's implementation timeline stretches from days to weeks. First, coordinate with data teams to set up pipelines. Then define metrics in SQL. Debug inevitable warehouse permission issues. Configure experiment allocation logic. Each step requires specialized knowledge and cross-team coordination.
The ongoing maintenance burden differs too. Statsig's hosted infrastructure updates automatically - new features appear without any work from your team. Eppo requires you to maintain warehouse queries, optimize performance as data grows, and troubleshoot pipeline failures. Your data team becomes responsible for experimentation infrastructure.
Pricing models and cost analysis
Transparent pricing structures
Pricing transparency matters when budgeting for experimentation platforms. Statsig scales exclusively with analytics events - you pay for what you measure, not who uses it. This means unlimited monthly active users, unlimited feature flag checks, and no per-seat pricing at any tier.
The free tier includes substantial resources:
50K session replays monthly
Unlimited feature flags
Complete experimentation capabilities
No time limits or trial periods
Eppo's pricing data shows annual costs from $15,050 to $87,250, with median spending around $42,000. The platform doesn't publish detailed pricing tiers publicly - you'll negotiate custom contracts based on your usage patterns.
Real-world cost scenarios
Let's model costs for a typical B2B SaaS company:
100K monthly active users
50 experiments per quarter
10 feature flags in production
With Statsig's generous free tier, this company pays nothing while accessing the full platform. The same company enters Eppo's enterprise pricing immediately - likely starting at $15,000+ annually before any negotiation.
The bundled platform approach multiplies savings. Instead of separate subscriptions for analytics ($2,000/month), feature flags ($1,000/month), and experimentation ($3,000/month), Statsig includes everything. Brex reported over 20% cost savings after consolidating to this unified approach.
Hidden costs tell the complete story. Eppo's warehouse-native architecture requires:
Existing data warehouse ($$$ compute costs)
SQL-proficient engineers ($150K+ salaries)
Ongoing maintenance time (10-20 hours monthly)
Statsig's hosted option eliminates these infrastructure costs entirely. The warehouse-native option works with your existing setup but doesn't require it. You're not forced into expensive technical decisions before proving experimentation value.
Decision factors and implementation considerations
Time-to-value and onboarding complexity
Product teams measure success in shipped features, not configured pipelines. Statsig recognizes this reality - teams launch experiments within days using visual interfaces and pre-built SDKs. Non-technical members create tests independently without SQL knowledge or engineering dependencies.
Notion scaled from single-digit to 300+ experiments per quarter after adopting Statsig. Their four-person experimentation team now handles tooling that previously required dedicated infrastructure engineers. The acceleration came from removing technical barriers between ideas and implementation.
Eppo's warehouse-native architecture creates different dynamics. You need:
Existing data warehouse infrastructure
SQL-proficient team members
Data pipeline setup before any experiments
This sequential dependency chain means product teams wait for data teams at every step. Simple questions like "What's our conversion rate for this variant?" require writing and debugging SQL queries rather than clicking through a dashboard.
Scalability and enterprise readiness
Both platforms handle massive scale - but implementation paths diverge significantly. Statsig processes trillions of events daily for companies like OpenAI and Microsoft. The platform maintains 99.99% uptime with sub-millisecond evaluation latency regardless of volume.
Paul Ellwood from OpenAI noted: "Statsig's infrastructure and experimentation workflows have been crucial in helping us scale to hundreds of experiments across hundreds of millions of users." They achieve this scale without managing experimentation infrastructure internally.
Infrastructure choices determine where your team focuses energy. Statsig's hosted option removes:
Warehouse query optimization
Pipeline failure debugging
Performance tuning as data grows
Security patch management
Eppo requires your team to handle all these responsibilities. As experimentation programs grow, infrastructure maintenance consumes increasing engineering time. You're building a platform team instead of shipping features.
Team structure and technical requirements
Your current team composition predicts platform success. Statsig works for organizations without dedicated data engineers or widespread SQL expertise. Marketing teams run campaign tests, designers test UI variations, and product managers optimize conversion funnels - all without technical support.
Bluesky reached 25 million users with minimal data engineering resources. They ran 30+ experiments in seven months by giving everyone experimentation access through Statsig's self-serve interfaces. No SQL required.
Eppo assumes different organizational capabilities:
Data engineers managing warehouse infrastructure
Analysts writing complex SQL queries
Technical product managers comfortable with code
Non-technical teams depend entirely on these specialists for experiment setup, metric definitions, and result analysis. This creates bottlenecks as experimentation programs expand - every new test requires data team involvement.
Cost structure and pricing transparency
Philosophical differences extend to pricing models. Statsig charges based on analytics events only - feature flags and gate checks remain free at unlimited scale. This predictable model helps teams budget effectively as usage grows. You know exactly what drives costs.
Eppo's pricing ranges significantly, from $15,050 to $87,250 annually based on recent purchase data. The warehouse-native approach adds operational expenses:
Compute resources for query processing
Storage costs as experiment data accumulates
Engineering time for maintenance and optimization
Teams must model total cost of ownership beyond license fees. A $40,000 Eppo contract might require $100,000+ in infrastructure and personnel costs annually. Statsig's all-inclusive pricing eliminates these hidden expenses.
Bottom line: why is Statsig a viable alternative to Eppo?
SQL requirements create artificial barriers between teams and experimentation. While Eppo's warehouse-native approach demands technical expertise, Statsig offers both warehouse-native and hosted options. Product managers, marketers, and designers run tests independently - no SQL required.
The unified platform eliminates expensive tool sprawl. Teams consolidate analytics, feature flags, and experimentation into one system. Statsig's pricing analysis demonstrates 50%+ savings compared to purchasing separate solutions. Sumeet Marwaha from Brex confirmed: "The biggest benefit is having experimentation, feature flags, and analytics in one unified platform. It removes complexity and accelerates decision-making."
Scale doesn't require complexity. The platform processes over 1 trillion events daily for enterprise customers. Yet Bluesky launched 30 experiments in 7 months with just a handful of engineers. The same infrastructure that powers OpenAI's experiments works perfectly for smaller teams.
Transparent pricing aligns with actual usage. The generous free tier includes 50K session replays, unlimited feature flags, and complete experimentation capabilities. Compare this to Eppo's $15,050+ starting price with a median around $42,000 annually. Statsig scales with events, not seats or MAUs - you control costs as you grow.
The choice ultimately depends on your team's technical capabilities and experimentation goals. Companies with dedicated data teams and existing warehouse infrastructure might appreciate Eppo's SQL-centric control. But if you want to democratize experimentation across your organization - letting everyone test ideas without technical barriers - Statsig provides the clearer path forward.
Closing thoughts
Experimentation platforms should accelerate learning, not create technical bottlenecks. The SQL requirement that defines many platforms often becomes the very thing that limits their adoption. Statsig's approach - offering both technical flexibility and non-technical accessibility - reflects a more pragmatic view of how modern product teams actually work.
If you're evaluating experimentation platforms, consider who will actually use them day-to-day. Will your product managers wait for data teams to write SQL queries? Can your designers test UI changes independently? The answers to these questions matter more than feature comparisons.
For teams ready to explore further, check out Statsig's interactive demo or dive into their documentation to see the platform in action. The best experimentation platform is the one your entire team will actually use.
Hope you find this useful!