Replace Eppo's experimentation platform with Statsig

Most data teams evaluating experimentation platforms face a frustrating reality: the tools built for hyperscale companies cost too much and do too little. You end up paying enterprise prices for basic A/B testing while your actual needs - feature flags, analytics, session replay - require separate vendors and contracts.

This creates a familiar pattern. Teams start with one tool for experiments, add another for feature management, then bolt on analytics and session recording. Before long, you're managing four vendors, dealing with data inconsistencies, and watching costs spiral beyond your original budget.

Company backgrounds and platform overview

Statsig emerged from Facebook's experimentation infrastructure when former VP Vijaye Raji founded the company in 2020. The team spent eight months building without customers before gaining traction through former colleagues who understood the value of Facebook-grade testing tools. Today they process over 1 trillion events daily for companies like OpenAI, Figma, and Notion.

Eppo positions itself as a warehouse-native experimentation platform, recently acquired by Datadog's observability suite. Their architecture requires direct integration with customer data warehouses - Snowflake, BigQuery, or Databricks. This appeals to data-mature organizations with strict privacy requirements but limits accessibility for smaller teams.

The platforms target fundamentally different segments. Statsig offers both hosted and warehouse-native options, serving everyone from two-person startups to Fortune 500 enterprises. Eppo exclusively focuses on warehouse-native deployments for organizations with established data infrastructure. This architectural choice shapes everything else: pricing, onboarding time, and who can actually use the platform.

Statsig's evolution reflects real customer demands rather than Facebook replication. When HelloFresh pushed for warehouse capabilities, Statsig pivoted and unlocked growth beyond traditional experimentation. This flexibility helped them beat incumbents like Optimizely while expanding into adjacent products.

Eppo's pricing ranges from $15,050 to $87,250 annually, with most customers paying around $42,000. Their warehouse-first approach eliminates certain privacy concerns but requires significant infrastructure investment before running your first experiment.

Feature and capability deep dive

Experimentation and statistical engines

Both platforms implement CUPED variance reduction to accelerate experiment decisions - a critical feature when you need results in days, not weeks. Statsig extends this foundation with sequential testing, switchback testing, and stratified sampling. These aren't academic exercises; they're essential for marketplace experiments where traditional A/B tests fail.

The statistical philosophy differs sharply between platforms. Statsig provides both Bayesian and Frequentist approaches, letting teams choose based on their needs. Eppo focuses primarily on Frequentist methods with centralized workflows. This matters when:

• Your data science team prefers Bayesian methods for decision-making

• Regulatory requirements demand specific statistical approaches

• Different teams have different statistical training and preferences

Analytics and feature management

Here's where the philosophical divide becomes practical. Statsig bundles unlimited feature flags with analytics at no extra cost. You can deploy a thousand flags without worrying about overage charges. Eppo's pricing model separates these capabilities, potentially tripling costs for teams using both.

Performance tells the real story. Statsig processes 1+ trillion daily events with sub-millisecond latency - the same infrastructure supporting OpenAI's experiments runs your startup's first A/B test. Both platforms support warehouse-native deployments, but only Statsig offers a hosted option when you need to move fast.

One Statsig user on G2 captured the practical benefit: "Having feature flags and dynamic configuration in a single platform means that I can manage and deploy changes rapidly." This isn't just convenience - it's the difference between shipping features in hours versus coordinating across multiple tools.

The integration story reveals another gap. Statsig's 30+ SDKs cover every major programming language plus edge computing scenarios. Install the SDK, add a few lines of code, and you're running experiments. Eppo requires existing feature flag infrastructure or custom integration work, adding weeks to your timeline.

Pricing models and cost analysis

Platform cost structures

The pricing models reveal each company's priorities. Statsig charges only for analytics events and session replays while providing free unlimited feature flags at all tiers. This means your costs scale with actual usage, not arbitrary limits.

Eppo's model combines MAU tracking with warehouse compute costs, creating dual pricing variables that compound unpredictably. Their annual contracts range from $15,050 to $87,250, with the median customer paying $42,000. But that's before considering:

• Warehouse compute costs for experiment analysis

• Data storage for historical metrics

• Engineering time for pipeline maintenance

• Additional tools for features Eppo doesn't provide

Real-world cost scenarios

Let's examine actual usage patterns. For 100K MAU generating 20M monthly events, Statsig costs under $1,000 monthly. Eppo's equivalent setup starts at $3,500 monthly - a 3.5x difference that widens at scale.

Consider a typical SaaS company with 500K monthly visitors running 50 experiments across 100 feature flags. Statsig's total cost remains under $5,000 monthly including 50K free session replays. The same setup with Eppo exceeds $15,000 monthly before adding session replay capabilities through another vendor.

Don Browning, SVP at SoundCloud, explained their decision: "We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration." The bundled approach eliminates hidden costs from maintaining multiple vendor relationships and data pipelines.

Decision factors and implementation considerations

Onboarding and time-to-value

Speed matters when your team needs experiment results. Statsig's 30+ SDKs with edge computing support enable same-day implementation for most tech stacks. Install the SDK, wrap your feature in a gate, and you're collecting data within hours.

Eppo's warehouse-native approach demands existing infrastructure and clean data pipelines. Typical onboarding takes 2-4 weeks minimum with data engineers setting up initial connections. You get powerful analysis capabilities but sacrifice the ability to move quickly.

The difference becomes stark for smaller teams. A two-person startup can implement Statsig over a weekend and run their first experiment Monday morning. That same team would spend months preparing infrastructure for Eppo - if they have the expertise at all.

Support and scalability

Both platforms handle enterprise scale differently. Statsig provides 99.99% uptime SLA for all customers with infrastructure supporting 2.5 billion monthly experiment subjects. Every customer - from solo developers to Microsoft - gets the same battle-tested platform.

Support accessibility varies dramatically. Statsig includes Slack access and AI-powered assistance on all tiers. You get help when you need it, not when your contract allows it. Eppo's support model focuses on enterprise customers with dedicated success teams, potentially leaving smaller teams waiting for responses.

Technical requirements and team expertise

Your existing setup determines which platform fits better. Statsig works with any tech stack: React apps, iOS native, backend Python services. The platform handles data processing, so product managers can launch experiments without engineering support.

Eppo demands specific prerequisites:

• Mature data warehouse (Snowflake, BigQuery, or Databricks)

• Clean event tracking already implemented

• SQL expertise for metric definitions

• Data engineering resources for ongoing maintenance

These requirements create powerful analysis capabilities but limit who can actually create and manage experiments. Your data team becomes a bottleneck for every product decision.

Bottom line: why Statsig offers a better path forward

Statsig delivers Facebook-grade experimentation infrastructure at a fraction of Eppo's cost. While Eppo's pricing ranges from $15,050 to $87,250 annually, Statsig provides transparent usage-based pricing that scales with your actual needs.

The platform eliminates tool sprawl by combining four essential products in one interface. Instead of managing separate contracts for experimentation, feature flags, analytics, and session replay, everything integrates seamlessly. This unified approach saves money and engineering time while reducing data inconsistencies.

Sumeet Marwaha, Head of Data at Brex, captured the practical impact: "The biggest benefit is having experimentation, feature flags, and analytics in one unified platform. It removes complexity and accelerates decision-making."

Implementation speed sets Statsig apart. With 30+ SDKs and warehouse-native options, teams report launching first experiments within days. The generous free tier includes 2 million events monthly, unlimited feature flags, and 50K session replays - enough for most startups to validate product-market fit without spending anything.

Performance at scale proves the platform's maturity. Processing over 1 trillion events daily with 99.99% uptime, Statsig supports customers from two-person startups to OpenAI and Microsoft. The infrastructure handles your growth without performance degradation or pricing surprises.

Closing thoughts

Choosing an experimentation platform shapes how quickly your team can validate ideas and ship improvements. While Eppo serves a specific niche of warehouse-native enterprises, most teams need a more flexible solution that grows with them. Statsig's combination of accessible pricing, bundled features, and proven scale makes it the practical choice for teams serious about experimentation.

For deeper technical comparisons and migration guides, check out Statsig's documentation or explore their interactive demo environment. The platform offers a generous free tier, so you can validate the fit before committing to any contracts.

Hope you find this useful!