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An alternative to Eppo's feature flagging: Statsig
Choosing between experimentation platforms isn't just about features - it's about finding the right fit for your team's workflow and budget. While Eppo has gained traction with its warehouse-native approach, many teams find themselves caught between high costs and complex infrastructure requirements.
Statsig offers a different path: the same statistical rigor and experimentation capabilities, but with flexible deployment options and transparent pricing. Let's dig into what actually separates these platforms and why teams like OpenAI, Notion, and Brex chose Statsig over alternatives.
Company backgrounds and platform overview
Statsig's story starts inside Facebook. Vijaye Raji built internal tools like Deltoid and Scuba that powered experimentation for billions of users. In 2020, he left to recreate that infrastructure for everyone else. The first eight months were brutal - no customers, just building. Then former Facebook colleagues started signing up. They knew what the platform could do because they'd used the original version.
Eppo positions itself as a warehouse-native experimentation platform. No compromises: your data stays in your warehouse, period. This appeals to data teams who've been burned by black-box vendors. Datadog clearly saw the value, acquiring Eppo to fold experimentation into their observability suite.
The deployment philosophy reveals each platform's priorities. Statsig offers both cloud-hosted and warehouse-native options - start fast with the cloud, migrate to warehouse when you're ready. Eppo commits fully to warehouse integration. No shortcuts, no hosted option. This fundamental choice ripples through everything: pricing, setup time, team requirements.
The acquisition changes Eppo's trajectory. Integration with Datadog's monitoring tools makes sense for infrastructure teams already invested in that ecosystem. Meanwhile, Statsig keeps expanding horizontally - product analytics, session replay, and feature management join core experimentation. Different visions for how product teams should work with data.
"We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration," said Don Browning, SVP at SoundCloud.
Feature and capability deep dive
Core experimentation capabilities
Statistical rigor matters when you're making million-dollar decisions. Both platforms implement CUPED variance reduction - Facebook's technique for detecting smaller effects with less data. But Statsig goes deeper. Sequential testing lets you peek at results safely. Switchback tests handle time-based features. Stratified sampling tackles marketplace dynamics where buyers and sellers interact.
The infrastructure differences become clear at scale. Statsig processes over 1 trillion events daily across its cloud platform. Teams can start experiments immediately, then graduate to warehouse-native deployment when ready. Eppo requires your warehouse from day one. No training wheels. You need Snowflake or BigQuery configured before running your first test.
"Statsig's experimentation capabilities stand apart from other platforms we've evaluated. Statsig's infrastructure and experimentation workflows have been crucial in helping us scale to hundreds of experiments across hundreds of millions of users."
Paul Ellwood, Data Engineering, OpenAI
Feature flagging and release management
Here's where the platforms diverge sharply. Statsig treats feature flags as first-class citizens: unlimited free flags, automated rollbacks, and environment targeting come standard. No per-flag pricing games. Teams implement progressive rollouts, canary deployments, and instant kill switches without budget conversations.
The technical implementation matters. Statsig eliminates gate-check latency while maintaining 99.99% uptime. Your flags evaluate locally on-device, with configuration updates streamed in real-time. Approval workflows and audit logs satisfy compliance teams. Automated rollbacks trigger when metrics tank.
Eppo includes basic feature flagging but treats it as an add-on to experimentation. The documentation barely mentions flags. Advanced patterns like percentage rollouts exist, but without the polish of dedicated platforms. Teams serious about release management typically pair Eppo with LaunchDarkly or similar - doubling tool complexity and cost.
Analytics and reporting functionality
Statsig bundles product analytics, session replay, and experimentation into one platform. This isn't just convenience - it's about maintaining metric consistency. When your experiment shows a 5% revenue lift, you can drill into the exact user sessions that drove it. Build funnels, analyze retention, debug edge cases. All using the same metric definitions.
The unified approach eliminates a common failure mode. Picture this: your experiment platform says conversion increased 3%, but your analytics tool shows no change. Which do you trust? Different event processing, different user identification, different calculation methods. Statsig sidesteps this by using one pipeline for everything.
Eppo assumes you've already solved analytics elsewhere. Connect your warehouse, define metrics in SQL, analyze results. Clean separation for data-mature teams. But most companies aren't there yet. They need help building the analytics foundation, not just running tests on top of it.
"The biggest benefit is having experimentation, feature flags, and analytics in one unified platform. It removes complexity and accelerates decision-making by enabling teams to quickly and deeply gather and act on insights without switching tools."
Sumeet Marwaha, Head of Data, Brex
Pricing models and cost analysis
Transparent pricing structures
Platform pricing usually hides behind "Contact Sales" buttons. Statsig breaks that pattern: unlimited feature flags, MAUs, and seats at every tier. You pay only for analytics events and session replays. The free tier includes 10M events, unlimited flags, and 50K session replays monthly.
Eppo's pricing ranges from $15,050 to $87,250 annually according to customer reports. No public pricing page. No free tier. Every deployment starts with a sales negotiation. The warehouse-native architecture partially explains this - infrastructure costs vary wildly between customers.
Real-world cost scenarios
Let's model costs for a typical B2B SaaS company:
100K monthly active users scenario:
Eppo: Estimated $3,500+ monthly minimum
1M MAU enterprise scenario:
Statsig: Roughly $5,000/month
Traditional platforms: $20,000+ monthly
The gap widens as you scale. Statsig's event-based pricing grows linearly. Most competitors charge by seats, flags, AND volume - costs compound quickly.
Hidden costs and implementation expenses
Eppo's true cost extends beyond the invoice. Running warehouse-native experimentation requires:
Data warehouse compute ($5,000-50,000/month for Snowflake/BigQuery)
Data engineer time for pipeline maintenance
Analytics engineer time for metric definitions
DevOps support for reliability
Most teams spend $10,000+ monthly on these indirect costs. You need dedicated data teams just to keep experiments running. Compare that to Statsig's turnkey deployment. As Wendy Jiao from Notion noted, "a single engineer now handles experimentation tooling that would have once required a team of four."
Decision factors and implementation considerations
Time-to-value and onboarding complexity
Speed matters when building experimentation culture. Teams lose momentum during lengthy deployments. Statsig's SDK approach means Captions ran their first test on day one. Install the SDK, wrap your feature in a flag, launch. Pre-built integrations with Segment, Amplitude, and Mixpanel eliminate complex data plumbing.
Eppo's warehouse-native architecture demands patience. Here's the typical timeline:
Connect warehouse permissions (1-2 days)
Map existing data schemas (3-5 days)
Define core metrics in SQL (1 week)
Configure assignment and exposure logging (1 week)
Run first meaningful experiment (week 3-4)
This assumes you already have clean data pipelines. Most teams don't. Add another month for data cleanup and standardization.
Support quality and documentation resources
When experiments affect revenue, support responsiveness matters. Statsig provides 24/7 Slack support with actual engineers responding. Not support agents reading scripts - people who understand the platform deeply. The CEO occasionally jumps into threads when issues escalate.
Documentation reflects each platform's audience. Statsig covers every feature across 30+ SDKs with runnable code examples. Tutorials walk through common patterns: feature flags, experimentation, progressive rollouts. Video guides supplement written docs.
Eppo's documentation targets data teams comfortable with SQL and warehouse concepts. Fewer code examples, more architectural diagrams. Support happens during business hours through traditional tickets. This matches their enterprise focus but frustrates teams wanting immediate help.
Scalability and enterprise readiness
Platform architecture determines your ceiling. Statsig handles trillions of events daily for customers like OpenAI without breaking a sweat. The infrastructure maintains 99.99% uptime regardless of volume. No configuration tweaks needed - it just scales.
Eppo's scalability depends on your warehouse. Each experiment adds queries against your data. Complex metrics multiply computation. Teams report query optimization becoming a full-time job beyond 100 concurrent experiments. Your warehouse bill grows alongside experiment velocity.
"Statsig's infrastructure and experimentation workflows have been crucial in helping us scale to hundreds of experiments across hundreds of millions of users." — Paul Ellwood, Data Engineering, OpenAI
Bottom line: why is Statsig a viable alternative to Eppo?
The numbers tell the story. Eppo costs $15,050 to $87,250 annually for experimentation alone. Statsig delivers experimentation, feature flags, analytics, and session replay for 50-80% less. The integrated platform eliminates tool sprawl that plagues most product teams.
Notion scaled from single-digit to 300+ experiments per quarter using Statsig's unified workflow. Run experiments, manage features, analyze results - all in one place. No context switching between tools. No debates about metric definitions. Just faster iteration cycles.
"Having experimentation, feature flags, and analytics in one unified platform removes complexity and accelerates decision-making by enabling teams to quickly gather and act on insights without switching tools." — Sumeet Marwaha, Head of Data, Brex
The deployment flexibility matters more than it seems. Statsig offers both cloud and warehouse-native options. Start with the cloud to prove value quickly. Migrate to warehouse-native when your data team is ready. Eppo's warehouse-only approach works for data-mature organizations but excludes everyone else.
Platform reliability becomes critical at scale. Statsig processes 1+ trillion events daily with 99.99% uptime for customers like Microsoft and OpenAI. Advanced statistical methods match or exceed Eppo's capabilities: CUPED, sequential testing, automated guardrails. Plus those unlimited free feature flags that would cost thousands elsewhere.
Bluesky runs 30+ experiments with just a small team thanks to Statsig's accessible interface. You don't need a data science PhD to interpret results. Clear statistical guidance helps teams make confident decisions. Automated alerts catch metric regressions before they impact users.
Closing thoughts
Picking an experimentation platform shapes how your team builds products for years. Eppo works well for data-mature organizations with existing warehouse infrastructure and dedicated analytics teams. But most companies need something more accessible and affordable.
Statsig delivers enterprise-grade experimentation without enterprise complexity or cost. The flexible deployment options, transparent pricing, and integrated feature set explain why teams from startups to OpenAI choose it over alternatives.
Want to explore further? Check out the detailed pricing comparison or see how Notion scaled their experimentation program. The free tier gives you plenty of room to validate the platform with real experiments.
Hope you find this useful!