An alternative to Eppo for non-data teams: Statsig

Experimentation platforms promise to help teams make better product decisions through data. But here's the reality: most platforms assume you have a team of data engineers and SQL experts on standby. Product managers wait days for metric definitions, marketers can't run their own tests, and engineers spend more time configuring pipelines than shipping features.

This creates a fundamental divide in the experimentation landscape. On one side, warehouse-native platforms like Eppo give data teams complete control - at the cost of accessibility. On the other, platforms like Statsig bet that democratizing experimentation across entire organizations drives better outcomes than perfect technical control.

Company backgrounds and platform overview

Statsig emerged from Facebook's experimentation culture in 2020, founded by Vijaye Raji who helped build Facebook's most successful products. The team took Facebook's internal tools - Deltoid for experimentation and Scuba for analytics - and rebuilt them for everyone else. Today they process over 1 trillion events daily serving billions of users.

Eppo, now part of Datadog, built their platform around a warehouse-native architecture. Everything lives in your data warehouse. Every metric requires SQL. Every experiment needs data team involvement. This approach appeals to companies with mature data infrastructure and dedicated analytics resources.

The philosophical difference shapes everything else. Statsig gives teams self-serve tools that product managers, engineers, and marketers can actually use. No SQL required. No waiting for the data team. As Wendy Jiao from Notion put it: "Statsig enabled us to ship at an impressive pace with confidence."

This accessibility gap becomes obvious in deployment options. Eppo offers only warehouse-native deployment - your data team maintains complete control but carries all the implementation burden. Statsig provides both hosted and warehouse-native options. Small teams start with hosted deployment in minutes. Large organizations with compliance requirements deploy to their warehouse. You choose based on needs, not constraints.

Target audiences tell the same story. Eppo serves enterprises with established data teams and SQL expertise across the organization. Statsig scales from two-person startups to enterprises like OpenAI and Microsoft - because accessibility doesn't mean sacrificing power.

Feature and capability deep dive

Core experimentation capabilities

Both platforms handle the basics: A/B testing, sequential analysis, and CUPED variance reduction. These are table stakes for modern experimentation.

But Statsig goes deeper with advanced methods available to everyone:

• Switchback testing for marketplace experiments where users interact

• Stratified sampling to reduce variance in specific segments

• Multi-armed bandits that automatically allocate traffic to winners

• Holdout groups to measure cumulative impact over time

The real difference shows up in feature flags. Statsig includes unlimited free feature flags at every tier - even the free plan. Eppo charges separately for feature management, treating it as an add-on rather than core functionality. This matters because modern teams need integrated experimentation and release control. You're testing features, not just running abstract experiments.

Analytics and reporting functionality

Eppo's SQL-first metric creation works great if everyone on your team writes SQL. Product managers define conversion metrics. Marketers build cohort analyses. Engineers debug data quality issues.

Reality looks different at most companies. The data team becomes a bottleneck. Simple metric changes take days. Experiments stall waiting for SQL reviews. Statsig solves this with no-code metric builders that non-technical users master in minutes. Complex metrics still support SQL when needed - but most teams discover they rarely need it.

Rose Wang from Bluesky captured this perfectly: "Statsig's powerful product analytics enables us to prioritize growth efforts and make better product choices during our exponential growth with a small team." Small team. Exponential growth. No mention of data engineers.

Platform scope reveals another split. Statsig bundles these capabilities in one interface:

• Experimentation for testing features

• Product analytics for understanding user behavior

• Session replay for debugging issues

• Feature flags for controlled rollouts

Eppo focuses purely on experimentation. You'll need separate tools for analytics, session replay, and feature management. That means different interfaces, inconsistent metrics, and constant context switching. The integrated approach saves more than time - it ensures your experiment metrics match your product metrics match your feature rollout metrics.

Pricing models and cost analysis

Transparent pricing structures

Statsig publishes exact pricing based on analytics events. No sales calls required. No hidden SKUs discovered during renewal. You get:

• Unlimited monthly active users (MAUs)

• Unlimited team seats

• Unlimited feature flags

• 50,000 free session replays monthly

• 2 million free events monthly

Eppo's pricing remains opaque. Market data shows a median of $42,000 annually, ranging from $15,050 to $87,250. You'll need multiple sales conversations to understand your actual costs. Feature flags cost extra. Additional seats might too.

Real-world cost scenarios

The numbers tell a stark story:

Startup (100K MAU):

• Statsig: $0 (free tier covers most startups)

• Eppo: $15,000+ minimum

Growth company (500K MAU, 10M events/month):

• Statsig: ~$1,000/month

• Eppo: $3,500+/month

Enterprise (50M MAU, 1B events/month):

• Statsig: ~$15,000/month with volume discounts

• Eppo: $7,000+/month plus feature flag costs

Statsig's usage-based model typically cuts costs by 50% compared to traditional platforms. But the real savings come from what's included. Those unlimited feature flags? That's often a $50,000+ line item elsewhere. Unlimited seats? Another $20,000+ at other vendors.

Sriram Thiagarajan from Ancestry summed it up: "Statsig was the only offering that we felt could meet our needs across both feature management and experimentation." One platform. One price. No surprises.

Decision factors and implementation considerations

Time-to-value and onboarding complexity

Speed matters when choosing an experimentation platform. Your competition isn't waiting for you to configure data pipelines.

Statsig gets teams running experiments in days:

1. Install a pre-built SDK (10 minutes)

2. Create metrics using visual builders (30 minutes)

3. Launch your first experiment (same day)

Eppo requires a different timeline:

1. Map your data warehouse schema (1-2 weeks)

2. Write SQL for every metric (ongoing)

3. Configure assignment tables (1 week)

4. Test data pipeline accuracy (1-2 weeks)

5. Launch your first experiment (3-6 weeks)

One G2 reviewer noted about Statsig: "It has allowed my team to start experimenting within a month." That's conservative - most teams run experiments within a week.

Support quality and documentation depth

When experiments break at 3 AM, support responsiveness matters. Statsig provides direct Slack access where engineers get answers in minutes - sometimes from the founders themselves. Their AI support bot handles common questions instantly while comprehensive docs cover edge cases.

Both platforms offer enterprise support tiers. But here's the key difference: Statsig's self-serve model means you rarely need support. Transparent SQL queries show exactly how metrics calculate. Detailed error messages explain what went wrong. Visual debuggers help trace experiment assignment.

Eppo's warehouse-native approach shifts support burden to your team. When metrics return unexpected results, you debug SQL. When pipelines fail, you fix them. When assignment logic breaks, you own the resolution.

Data governance and security requirements

Some teams need complete data control. Healthcare companies face HIPAA requirements. Financial services navigate SOC2 compliance. European companies manage GDPR constraints.

Eppo's warehouse-native architecture keeps all data in your infrastructure. Nothing leaves your warehouse. This appeals to security teams but demands significant technical investment:

• Maintaining metric definitions across teams

• Ensuring pipeline reliability

• Managing compute costs

• Debugging data quality issues

Statsig offers flexibility:

• Hosted deployment: Start immediately, migrate later if needed

• Warehouse-native: Same data control as Eppo

• Hybrid approach: Feature flags hosted, analytics in warehouse

The key question: does your team have the SQL expertise to maintain a warehouse-native deployment? If not, Statsig's hosted option provides enterprise security without the operational burden.

Scale considerations and pricing transparency

Traffic spikes shouldn't break your experimentation platform - or your budget. Statsig processes over 1 trillion events daily with predictable performance at any scale. Their infrastructure handles Black Friday traffic surges and viral app launches without breaking a sweat.

Pricing scales predictably too. You pay for analytics events, not MAUs or flag evaluations. A viral TikTok video won't trigger overage charges. Feature flag checks remain free whether you have 1,000 or 100 million users.

Eppo's pricing structure creates uncertainty at scale. Limited public data shows costs ranging from $15,050 to $87,250 annually - but what happens when you 10x your user base? Without transparent pricing, you're negotiating blind.

Neither platform charges for additional seats, enabling organization-wide adoption. But Statsig's accessible interface means those seats actually get used. Marketing runs their own experiments. Support teams test help center changes. Sales tries new onboarding flows. Democratization only works when people can actually use the tools.

Bottom line: Why Statsig works for non-data teams

The experimentation platform market splits into two camps. Platforms like Eppo assume every team has data engineers and SQL expertise. They optimize for technical control over accessibility.

Statsig takes the opposite bet: democratizing experimentation drives better outcomes than perfect technical control. Wendy Jiao from Notion confirmed this approach works: "Statsig enabled us to ship at an impressive pace with confidence," she noted, explaining that a single engineer now handles experimentation tooling that once required a team of four.

The economics make this accessibility gap impossible to ignore. Statsig starts at $0 with 2 million free events monthly. Eppo starts at $15,050 annually - before adding feature flags or additional capabilities. That's not just a price difference. It's the difference between every team running experiments and only data-mature enterprises affording them.

Beyond cost, integration matters. Eppo focuses purely on experimentation, leaving you to source:

• Product analytics from Amplitude or Mixpanel

• Feature flags from LaunchDarkly or Split

• Session replay from FullStory or LogRocket

• User targeting from a CDP or custom solution

Statsig bundles everything at no extra charge. Unlimited feature flags, 50K free session replays, and comprehensive analytics ship with experimentation. One SDK. One interface. One set of metrics across your entire product stack.

Even warehouse-native deployment - Eppo's main selling point - works differently between platforms. Eppo's warehouse-native means experimentation metrics live in your warehouse. Statsig's warehouse-native extends to feature flags, product analytics, and session replays. Your data team maintains control while your product team gains autonomy.

The choice comes down to your team's reality. If you have dedicated data engineers, SQL expertise across the organization, and patience for multi-week implementations, Eppo might work. But if you want product managers running their own experiments, marketers testing campaigns independently, and engineers shipping features without waiting for data team reviews, Statsig delivers that today.

Closing thoughts

Experimentation shouldn't require a PhD in data science or a team of SQL experts. The best insights often come from teams closest to users - product managers testing new flows, marketers optimizing campaigns, support teams improving help content. When experimentation platforms gatekeep behind technical complexity, organizations miss these opportunities.

Statsig's approach proves that accessibility and power aren't mutually exclusive. You can have enterprise-scale infrastructure, advanced statistical methods, and warehouse-native deployment while still letting anyone on your team run experiments. The 50% cost savings are nice. The ability to actually use the platform you're paying for? That's transformative.

If you're evaluating experimentation platforms, here are some resources to dig deeper:

• Statsig's guide to experimentation costs breaks down pricing models across vendors

• Their feature flag cost comparison shows hidden expenses in bundled platforms

• The customer stories demonstrate how teams from Notion to Microsoft actually use the platform

Hope you find this useful!