An alternative to Eppo's warehouse-native approach: Statsig

Choosing between experimentation platforms often comes down to a fundamental architectural decision: do you want your data in someone else's cloud or exclusively in your warehouse? This choice shapes everything from implementation timelines to long-term costs.

Eppo built their entire platform around warehouse-native architecture - if you want to run experiments, you need a data warehouse first. Statsig took a different path, offering both hosted cloud and warehouse-native options. The flexibility matters more than you might think, especially when your team needs to ship experiments next week, not next quarter.

Company backgrounds and platform overview

Vijaye Raji built Statsig to recreate Facebook's experimentation infrastructure after leading product development there. The 2020 launch timing was deliberate - companies were rapidly digitizing and needed battle-tested experimentation tools. Today the platform processes over 1 trillion events daily for OpenAI, Notion, and Microsoft.

Datadog acquired Eppo to add A/B testing to their observability suite. The warehouse-native platform appealed to data teams who wanted experiments running directly in Snowflake or BigQuery. Post-acquisition, Eppo functions as Datadog's experimentation layer - tightly integrated with their monitoring ecosystem.

The architectural split reflects different philosophies about data ownership. Statsig offers dual deployment: start with hosted cloud for immediate value, then migrate to warehouse-native when you need it. Eppo commits fully to warehouse architecture. No hosted option exists - your experiments live where your data lives.

This isn't just technical trivia. Don Browning, SVP at SoundCloud, evaluated both approaches: "We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration." The flexibility to choose deployment models based on current needs - not future hypotheticals - drives adoption patterns.

Feature and capability deep dive

Core experimentation capabilities

Statistical rigor matters when you're making million-dollar decisions based on test results. Both platforms deliver:

• CUPED variance reduction for faster experiment conclusions

• Sequential testing to prevent p-hacking

• Bayesian and Frequentist analysis options

• Power calculations and sample size estimation

The difference shows up in execution speed. Statsig's 30+ SDKs deliver sub-millisecond feature flag decisions directly from memory. Eppo queries your warehouse for every flag evaluation - adding 50-500ms of latency depending on warehouse performance. For high-frequency decisions (think search ranking or recommendation systems), that latency adds up.

Analytics and product development tools

Statsig bundles experimentation with product analytics and session replay at no extra cost. You can watch actual user sessions, identify friction points, then launch experiments to fix them - all in one platform. The integration goes deeper than just shared navigation. Metrics defined for analytics automatically become available for experiments. Session replays help debug why certain variants underperformed.

Eppo focuses exclusively on experimentation. You'll integrate separate tools for analytics, monitoring, and debugging. Sumeet Marwaha from Brex explains why consolidation matters: "The biggest benefit is having experimentation, feature flags, and analytics in one unified platform. It removes complexity and accelerates decision-making."

The bundled approach particularly helps smaller teams who can't justify multiple tool subscriptions. But even enterprises benefit from reduced context switching and unified data models.

Developer experience and integration

Getting experiments into production shapes adoption velocity. Statsig's approach prioritizes developer simplicity:

• Edge computing support: Deploy to Cloudflare Workers or Fastly for global latency under 50ms

• Auto-generated TypeScript types: Catch flag typos at compile time

• Pre-built integrations: Connect Segment, Amplitude, or Mixpanel events without custom code

• Real-time debugging: See which users get which variants instantly

One G2 reviewer captured the experience: "Implementing on our CDN edge and in our nextjs app was straight-forward and seamless."

Eppo requires more setup but provides deeper warehouse control. You'll write SQL to define metrics, configure data pipelines, and manage warehouse permissions. The payoff comes in flexibility - any metric you can query becomes experimentable. But that flexibility costs engineering time upfront.

Pricing models and cost analysis

Transparent pricing structures

Statsig publishes exact pricing on their website. The free tier includes:

• 2 million events per month

• 50,000 session replays

• Unlimited feature flags

• Unlimited seats

Beyond free limits, you pay only for analytics events - roughly $50 per million events. Feature flag checks remain free at any volume. A startup with 100K monthly active users typically stays within free limits entirely.

Eppo takes the enterprise sales approach. Vendr data shows annual contracts ranging from $15,050 to $87,250, with a $42,000 median. You'll negotiate with sales for exact pricing. No free tier exists - that minimum $15K commitment hits before you run your first test.

Real-world cost scenarios

Let's model costs for a growing SaaS company:

At 1 million MAU:

• Statsig: ~$1,000/month for full platform access

• Eppo: $3,500-7,000/month plus warehouse compute costs

At 10 million MAU:

• Statsig: ~$5,000/month with predictable linear scaling

• Eppo: $10,000-20,000/month plus growing warehouse bills

Statsig's detailed cost analysis shows 50-80% savings versus competitors at scale. The savings compound when you factor in free feature flags and bundled analytics.

Sriram Thiagarajan, CTO at Ancestry, validated this math in practice: "Statsig was the only offering that we felt could meet our needs across both feature management and experimentation." The unified pricing model simplified budgeting for their massive user base.

Decision factors and implementation considerations

Time-to-value and onboarding complexity

Speed to first experiment separates platforms dramatically. With Statsig's hosted option:

1. Install SDK (30 minutes)

2. Create feature gate (5 minutes)

3. Define success metrics (15 minutes)

4. Launch experiment (instant)

Total time: under 2 hours from signup to live experiment.

Eppo's warehouse-native approach requires:

1. Configure warehouse permissions and schemas

2. Set up data pipeline for event collection

3. Define metrics in SQL

4. Install SDK and configure feature flags

5. Wait for data to accumulate before launching

Realistic timeline: 2-4 weeks with dedicated engineering resources.

The complexity gap matters most for teams without existing data infrastructure. If you already have a mature warehouse setup with clean event data, Eppo's requirements feel manageable. Starting from scratch? You'll spend months building prerequisites before running a single test.

Scalability and enterprise readiness

Statsig's infrastructure handles OpenAI's experimentation needs - that's the definition of enterprise scale. The platform processes 1+ trillion daily events with 99.99% uptime. Automatic scaling handles traffic spikes without manual intervention. Your experiments keep running whether you have thousands or billions of users.

Paul Ellwood from OpenAI confirms: "Statsig's infrastructure and experimentation workflows have been crucial in helping us scale to hundreds of experiments across hundreds of millions of users."

Eppo's scalability depends on your warehouse configuration. You control performance by adjusting compute resources. But that control comes with responsibility - poorly optimized queries can tank experiment performance or explode costs. One customer reported their monthly Snowflake bill tripling after implementing comprehensive experimentation.

Data governance and security requirements

Regulated industries often mandate that data never leaves company-controlled infrastructure. Eppo's warehouse-only model satisfies these requirements by default. Your experiment data lives in the same warehouse as your production data, inheriting all existing security controls.

Statsig addresses governance through flexibility:

• Hosted option: SOC 2 Type II certified with enterprise security features

• Warehouse native: Full data control matching Eppo's model

• Hybrid deployment: Use hosted for non-sensitive features, warehouse for PII

This optionality lets teams start fast with hosted deployment, then selectively migrate sensitive experiments to warehouse-native as needed. Notion scaled from single-digit to 300+ experiments quarterly using this hybrid approach.

Why teams choose Statsig over warehouse-only alternatives

The fundamental question isn't whether warehouse-native is better - it's whether you need to choose at all. Statsig provides both options, letting your deployment model evolve with your needs. Start with hosted cloud to ship experiments today. Move to warehouse-native when data governance demands it. Or run both simultaneously for different use cases.

This flexibility enables rapid experimentation program growth. Teams launch their first test in hours, not weeks. The bundled analytics and session replay eliminate tool sprawl. Transparent pricing prevents budget surprises as you scale.

Brex's data team captured the practical impact: feature flags, experimentation, and analytics in one platform accelerated their entire product development cycle. They ship faster because they're not constantly context-switching between tools.

The infrastructure proves itself at scale - processing over a trillion events daily for companies like Microsoft and OpenAI. Advanced statistical methods come standard. You get the same platform whether you're a 10-person startup or a Fortune 500 enterprise.

Cost predictability seals the deal for many teams. Pay only for what you use, with feature flags remaining free at any volume. No surprise enterprise contracts or warehouse compute bills derailing your experimentation roadmap.

Closing thoughts

Choosing an experimentation platform shapes how quickly your team can validate ideas and ship improvements. Warehouse-native platforms like Eppo work well for organizations with mature data infrastructure and strict governance requirements. But forcing everyone into that model creates unnecessary friction.

Statsig's dual deployment approach - hosted cloud or warehouse-native - gives you options. Most teams benefit from starting with the hosted platform to prove experimentation value, then selectively moving sensitive workloads to their warehouse. The flexibility to choose based on actual needs, not hypothetical requirements, accelerates experimentation adoption.

If you're evaluating platforms, consider your current infrastructure honestly. Do you have a functioning data warehouse with clean event data? How quickly do you need to ship your first experiment? What's your tolerance for ongoing maintenance and optimization? The answers guide you toward the right architectural choice.

For deeper comparisons and migration guides, check out Statsig's experimentation platform comparison or their detailed pricing calculator. Hope you find this useful!