Top 7 alternatives to Eppo for A/B Testing

Teams exploring alternatives to Eppo typically face similar concerns: limited deployment flexibility, high costs at scale, and lack of integrated analytics capabilities.

Eppo's warehouse-native approach works well for data teams with established infrastructure, but many organizations need more accessible solutions. The platform's SQL-centric workflow and enterprise pricing can create barriers for teams wanting to democratize experimentation across their organization. Strong Eppo alternatives address these limitations while offering comparable statistical rigor, easier implementation paths, and more flexible pricing models that scale with actual usage rather than forcing enterprise commitments.

This guide examines seven alternatives that address these pain points while delivering the A/B testing capabilities teams actually need.

Alternative #1: Statsig

Overview

Statsig delivers enterprise-grade A/B testing capabilities that match—and often exceed—what you'd find in Eppo. The platform processes over 1 trillion events daily while maintaining 99.99% uptime for companies like OpenAI, Notion, and Figma. Unlike Eppo's warehouse-only approach, Statsig offers both warehouse-native and hosted cloud deployment options.

Beyond core experimentation, Statsig bundles feature flags, product analytics, and session replay into one platform. This integration eliminates the context switching that plagues teams using separate tools for each function. The result: faster iteration cycles and more reliable data across your entire product development workflow.

"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

Key features

Statsig's A/B testing engine incorporates advanced statistical methods that rival any enterprise experimentation platform.

Statistical rigor

• CUPED variance reduction cuts experiment runtime by up to 50%

• Sequential testing lets you peek at results without inflating false positives

• Stratified sampling ensures balanced treatment groups across user segments

Experiment management

• Mutually exclusive layers prevent interference between concurrent tests

• Holdout groups measure long-term impact of feature releases

• Automated guardrails roll back features when metrics breach thresholds

Analysis capabilities

• Heterogeneous treatment effects identify which user segments benefit most

• Days-since-exposure cohorts detect novelty effects in your experiments

• Interaction detection reveals how experiments affect each other

Developer experience

• 30+ SDKs cover every major language and framework

• Edge computing support enables sub-millisecond feature evaluation

• Transparent SQL queries show exactly how metrics are calculated

"We transitioned from conducting a single-digit number of experiments per quarter using our in-house tool to orchestrating hundreds of experiments, surpassing 300, with the help of Statsig."

Mengying Li, Data Science Manager, Notion

Pros vs. Eppo

Integrated platform advantage

Statsig combines A/B testing with feature flags, analytics, and session replay in one system. Teams at Brex reduced time spent on experimentation by 50% after consolidating their tools. You'll analyze user behavior, launch experiments, and measure impact without switching between platforms.

Flexible deployment options

While Eppo requires warehouse-native deployment, Statsig offers both hosted and warehouse-native modes. Start with Statsig's hosted solution for immediate value, then migrate to warehouse-native when your data governance requires it. Secret Sales implemented Statsig in days, not months.

Dramatically lower costs

Statsig's pricing analysis shows costs 50-80% lower than competitors at scale. The free tier includes 2M events monthly—enough for meaningful experimentation. Feature flags remain free at any volume, unlike platforms that charge per flag check.

Advanced statistical capabilities

Statsig's stats engine surpasses Eppo with features like multi-armed bandits and switchback testing. The platform automatically applies Bonferroni correction and Benjamini-Hochberg procedures for multiple comparisons. These methods come standard, not as expensive add-ons.

"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

Cons vs. Eppo

Less SQL-centric workflow

Teams who prefer writing custom SQL for every metric might find Eppo's approach more familiar. Statsig provides visual metric builders alongside SQL access. Some data teams prefer Eppo's SQL-first philosophy over Statsig's hybrid approach.

Newer warehouse-native offering

Eppo built warehouse-native from day one, while Statsig added it based on customer demand. Organizations with complex warehouse setups might find Eppo's integrations more mature. Statsig's warehouse-native mode works excellently but has fewer years in production.

Different philosophical approach

Eppo targets data teams exclusively, while Statsig democratizes experimentation across organizations. Companies where only data scientists run experiments might prefer Eppo's specialized focus. Statsig's broader accessibility could feel unnecessary for SQL-heavy teams.

Alternative #2: PostHog

Overview

PostHog takes a different approach than Eppo by combining feature flags, A/B testing, product analytics, and session replay into one open-source platform. The company targets engineers and product teams who want to eliminate tool switching and manage their entire product development stack from a single interface. Unlike Eppo's warehouse-native architecture, PostHog operates as a standalone platform that can be self-hosted or used in the cloud.

PostHog's autocapture feature automatically tracks user events without manual implementation, reducing the technical overhead that warehouse-native solutions often require. This makes it particularly appealing to smaller teams or startups that need quick setup without extensive data infrastructure.

Key features

PostHog delivers comprehensive product development tools through four main areas of functionality.

Experimentation and A/B testing

• Bayesian statistics engine provides customized analysis for different experiment types

• Local evaluation reduces latency by processing feature flags client-side

• Sequential testing allows you to stop experiments early when results reach significance

Product analytics

• Autocapture automatically tracks clicks, page views, and form submissions without code changes

• Custom event tracking enables detailed funnel and retention analysis

• Cohort analysis helps identify user segments and behavior patterns

Feature management

• Feature flags support percentage rollouts and user targeting

• Real-time flag updates don't require application restarts

• Flag analytics show adoption rates and performance impact

User insights

• Session replay captures actual user interactions for debugging and optimization

• Targeted surveys collect feedback directly within your product

• Heatmaps visualize where users click and scroll on your pages

Pros vs. Eppo

Open-source flexibility

PostHog's open-source nature allows complete customization and control over your experimentation platform. You can modify the codebase, add custom features, and deploy on your own infrastructure without vendor lock-in.

All-in-one platform

Teams can run A/B tests, manage feature flags, analyze user behavior, and replay sessions without switching between multiple tools. This reduces context switching and keeps all product data in one place.

Transparent pricing

PostHog uses volume-based pricing without mandatory sales calls. The pricing model scales with usage rather than requiring enterprise negotiations—a refreshing change from traditional enterprise software.

Quick implementation

Autocapture and simple SDK integration mean you can start collecting data and running experiments within hours. This speed advantage helps teams begin testing immediately rather than waiting for complex warehouse configurations.

Cons vs. Eppo

Limited warehouse integration

PostHog doesn't natively integrate with existing data warehouses like Snowflake or BigQuery. Teams with established data infrastructure may find it difficult to connect PostHog with their current analytics stack.

Fewer advanced statistical features

While PostHog offers Bayesian analysis, it lacks some of Eppo's advanced statistical methods. CUPED variance reduction and sophisticated multiple comparison corrections aren't available, which may frustrate data science teams.

Self-hosting complexity

The self-hosted option requires infrastructure management, monitoring, and maintenance that many teams aren't prepared to handle. This operational overhead can offset the benefits of having full control over the platform.

Less data-focused approach

PostHog prioritizes ease of use over statistical rigor, which may not satisfy teams that need warehouse-native analysis or complex experimental designs. The platform works better for product teams than dedicated data science organizations.

Alternative #3: GrowthBook

Overview

GrowthBook positions itself as a warehouse-native platform that seamlessly integrates with your existing data infrastructure. Unlike PostHog's all-in-one approach, GrowthBook focuses specifically on feature flagging and A/B testing while leveraging your data warehouse as the source of truth.

The platform appeals to teams that want to maintain control over their data while still accessing powerful experimentation capabilities. GrowthBook's self-hosted deployment option makes it particularly attractive for companies in regulated industries or those with strict data governance requirements.

Key features

GrowthBook combines warehouse-native architecture with user-friendly interfaces to democratize experimentation across technical and non-technical teams.

Visual experimentation tools

• Visual A/B test editor allows non-technical users to create experiments without code

• Drag-and-drop interface simplifies test setup and variant creation

• Real-time preview shows changes before experiments go live

Statistical analysis capabilities

• Supports both Bayesian and Frequentist statistical methods for flexible analysis

• Multi-armed bandit testing optimizes traffic allocation automatically

• Sequential testing enables early stopping when results reach significance

Data integration and management

• Warehouse-native architecture connects directly to Snowflake, BigQuery, and other data warehouses

• Custom metric definitions pull data from your existing analytics tables

• Real-time data sync ensures experiments reflect the latest user behavior

Deployment and hosting options

• Self-hosted deployment gives complete control over data and infrastructure

• Cloud-hosted option provides managed service with enterprise security

• Hybrid deployment allows sensitive data to remain on-premises while using cloud features

Pros vs. Eppo

Warehouse-native architecture

GrowthBook matches Eppo's warehouse-native approach, allowing you to run experiments directly on your existing data infrastructure. This eliminates data silos and ensures consistency across your analytics stack.

Self-hosting capabilities

The self-hosted option provides greater control over data privacy and compliance than Eppo's cloud-first approach. Companies in regulated industries can maintain full data sovereignty while accessing enterprise-grade experimentation features.

Visual experiment builder

GrowthBook's visual editor democratizes A/B testing beyond data teams. Product managers and marketers can create experiments independently, reducing bottlenecks and accelerating experimentation velocity.

Flexible statistical methods

Support for both Bayesian and Frequentist approaches gives you analytical flexibility that matches your team's preferences. Multi-armed bandit testing provides automated optimization that goes beyond traditional A/B testing methods.

Cons vs. Eppo

Limited advanced statistical features

GrowthBook may lack some of Eppo's sophisticated statistical capabilities like CUPED variance reduction or advanced heterogeneous effect detection. Teams requiring cutting-edge statistical methods might find the platform limiting.

Smaller community and ecosystem

The smaller user base means fewer community resources, integrations, and third-party tools compared to more established platforms. You'll likely rely more heavily on direct support rather than community-driven solutions.

Technical setup requirements

Despite the visual interface, full warehouse-native deployment requires significant technical configuration and ongoing maintenance. Teams without dedicated data engineering resources may struggle with initial setup and optimization.

Enterprise feature gaps

GrowthBook's enterprise capabilities may not match Eppo's advanced governance features. Large organizations might find the platform lacks sophisticated user management and compliance features they require.

Alternative #4: LaunchDarkly

Overview

LaunchDarkly stands as the established leader in feature management, focusing primarily on feature flags and progressive delivery rather than comprehensive experimentation. The platform targets enterprise development teams who need granular control over feature releases with robust governance and compliance features. While LaunchDarkly excels at feature management, it takes a different approach than Eppo's warehouse-native experimentation focus.

LaunchDarkly's strength lies in its mature feature flagging infrastructure and enterprise-grade security capabilities. The platform has built a reputation for reliability and scalability among large organizations that require strict approval workflows and audit trails. Teams seeking integrated A/B testing and analytics capabilities may find LaunchDarkly's experimentation features less comprehensive than dedicated platforms.

Key features

LaunchDarkly provides enterprise-focused feature management with emphasis on control, governance, and scalability across development environments.

Feature flagging and targeting

• Advanced targeting rules with custom attributes and percentage rollouts

• Real-time flag updates without code deployment or application restarts

• Multi-environment support with promotion workflows between dev, staging, and production

Enterprise governance

• Approval workflows for flag changes with customizable review processes

• Audit logs and change tracking for compliance and security requirements

• Role-based access controls with team-specific permissions and restrictions

Developer integrations

• SDKs for 25+ programming languages with edge computing support

• Native integrations with CI/CD pipelines, monitoring tools, and development platforms

• Webhook support for custom integrations and automated workflows

Experimentation capabilities

• Basic A/B testing functionality with statistical significance calculations

• Metric tracking and conversion analysis for feature performance measurement

• Integration with analytics platforms for deeper experiment analysis

Pros vs. Eppo

Mature feature management platform

LaunchDarkly offers the most established feature flagging infrastructure in the market. The platform has proven reliability at enterprise scale with extensive governance features that Eppo doesn't match.

Enterprise-grade security and compliance

The platform provides comprehensive audit trails, approval workflows, and role-based access controls. These features make LaunchDarkly suitable for regulated industries with strict compliance requirements.

Extensive integration ecosystem

LaunchDarkly supports 25+ programming languages and integrates with major development tools. The platform's webhook system enables custom integrations that extend beyond Eppo's current capabilities.

Real-time feature control

Flag changes propagate instantly without code deployments or application restarts. This real-time control gives development teams immediate response capabilities during incidents or rollbacks.

Cons vs. Eppo

Limited experimentation analytics

LaunchDarkly's A/B testing capabilities lack the statistical rigor and advanced analysis features that Eppo provides. The platform focuses more on feature management than comprehensive experimentation workflows.

Not warehouse-native

Unlike Eppo's warehouse-native architecture, LaunchDarkly requires data to flow through their infrastructure. This approach limits integration with existing data warehouses and analytics tools that teams already use.

Higher total cost of ownership

LaunchDarkly's pricing can become expensive with add-ons and increased usage, as noted in feature flag platform cost comparisons. Enterprise features often require additional licensing that increases overall costs.

Complex setup and configuration

The platform's extensive feature set can overwhelm teams seeking simple experimentation tools. LaunchDarkly's enterprise focus means longer implementation times compared to more streamlined alternatives.

Alternative #5: Optimizely

Overview

Optimizely positions itself as a comprehensive Digital Experience Platform that extends far beyond basic A/B testing capabilities. The platform combines experimentation, personalization, and content management into a single enterprise-focused solution designed for large organizations with complex digital ecosystems.

Unlike warehouse-native alternatives, Optimizely operates as a hosted platform that integrates deeply with marketing and ecommerce tools. This approach makes it particularly appealing to marketing teams and large enterprises that need sophisticated digital experience management alongside their A/B testing infrastructure.

Key features

Optimizely's feature set spans multiple disciplines, from basic experimentation to advanced personalization and content management.

Experimentation capabilities

• Visual editor enables non-technical teams to create A/B tests without coding

• Sequential testing methodology provides statistically rigorous results for complex experiments

• Multivariate testing supports sophisticated experimental designs across multiple variables

Digital experience management

• Content management system integrates directly with experimentation workflows

• Personalization engine delivers targeted experiences based on user segments

• Campaign orchestration coordinates multiple touchpoints across customer journeys

Enterprise infrastructure

• Professional services team provides dedicated implementation and optimization support

• Advanced targeting capabilities segment users across multiple dimensions

• Integration marketplace connects with major marketing and analytics platforms

Analytics and reporting

• Real-time results dashboard tracks experiment performance across all variants

• Statistical significance calculations use industry-standard methodologies

• Custom reporting enables teams to analyze results through multiple lenses

Pros vs. Eppo

Comprehensive digital experience suite

Optimizely provides tools for content management, personalization, and experimentation in one platform. This integration eliminates the need to manage multiple vendors for digital experience initiatives.

No-code experiment creation

The visual editor allows marketing teams to create and launch A/B tests without engineering support. This capability significantly reduces time-to-market for marketing experiments and campaigns.

Enterprise-grade support and services

Optimizely offers dedicated customer success teams and professional services for implementation. Large organizations benefit from hands-on guidance and strategic consulting throughout their experimentation journey.

Strong marketing tool integrations

Native integrations with major marketing platforms streamline campaign management and data flow. Teams can leverage existing marketing infrastructure without complex custom integrations.

Cons vs. Eppo

High complexity and cost for experimentation-only needs

Organizations seeking only A/B testing capabilities may find Optimizely's comprehensive suite overwhelming and expensive. The platform's pricing reflects its broad feature set, making it prohibitive for smaller teams.

Limited data warehouse integration

Optimizely's hosted architecture doesn't provide the same level of data control as warehouse-native solutions. Teams with strict data governance requirements may find this limitation challenging.

Resource-intensive implementation

The platform's complexity requires dedicated resources for setup, management, and optimization. Organizations without sufficient technical and strategic resources may struggle to realize full value from the investment.

Marketing-focused approach

Optimizely's emphasis on marketing use cases may not align with product teams focused on feature experimentation. The platform's interface and workflows prioritize marketing campaigns over product development cycles.

Alternative #6: Amplitude

Overview

Amplitude started as a product analytics platform and expanded into A/B testing capabilities. The platform combines behavioral analytics with experimentation tools in a single interface. Enterprise customers favor Amplitude for its comprehensive data visualization and user journey mapping features.

Unlike warehouse-native solutions, Amplitude operates as a hosted platform that ingests your data. This approach works well for teams wanting an all-in-one analytics solution without managing infrastructure.

Key features

Amplitude's strength lies in combining deep product analytics with A/B testing functionality.

Event-based analytics

• Track user actions across web, mobile, and server-side applications

• Build custom events and properties for detailed behavioral analysis

• Create funnel analysis to identify conversion bottlenecks

User journey mapping

• Visualize complete user paths through your product

• Identify drop-off points and optimization opportunities

• Segment users based on behavioral patterns and engagement levels

Cohort analysis and segmentation

• Group users by shared characteristics or behaviors

• Track retention and engagement metrics over time

• Create dynamic segments that update automatically based on user actions

A/B testing integration

• Run experiments directly within the analytics platform

• Connect test results to behavioral data for deeper insights

• Measure experiment impact across multiple user journey stages

Pros vs. Eppo

Unified analytics and experimentation

Amplitude eliminates context switching between separate analytics and A/B testing tools. You can analyze user behavior and run experiments in the same platform.

Powerful visualization capabilities

The platform excels at creating charts, dashboards, and reports that make complex data accessible. Non-technical team members can explore data without SQL knowledge.

Enterprise-grade user journey analysis

Amplitude's behavioral analytics go deeper than most experimentation platforms. You can understand not just what users do, but why they behave certain ways.

Strong integration ecosystem

The platform connects with popular marketing tools, CDPs, and data sources. This makes it easier to create a complete view of your customer data.

Cons vs. Eppo

Not warehouse-native

Amplitude requires data ingestion rather than connecting directly to your warehouse. This creates data silos and potential governance issues for data-mature organizations.

Limited advanced experimentation features

The platform lacks sophisticated A/B testing capabilities like multi-armed bandit tests. Sequential testing and advanced statistical methods aren't available.

Higher pricing at scale

Amplitude's pricing model can become expensive as your user base grows. The cost per monthly tracked user adds up quickly for high-traffic applications.

Complexity for smaller teams

The platform's extensive feature set can overwhelm teams that need simple A/B testing. Setup and configuration require significant time investment upfront.

Alternative #7: Mixpanel

Overview

Mixpanel started as a product analytics platform and recently expanded into A/B testing capabilities. The platform focuses on event tracking and user behavior analysis for product teams. Unlike warehouse-native solutions, Mixpanel operates as a hosted analytics service with experimentation features built on top.

The platform appeals to product teams who want analytics and A/B testing in one place. Mixpanel's strength lies in its deep event tracking and user segmentation capabilities. Its experimentation features remain secondary to its core analytics offering.

Key features

Mixpanel combines product analytics with basic A/B testing functionality across multiple feature areas.

Event tracking and analytics

• Real-time event ingestion with flexible property tracking

• Advanced segmentation based on user properties and behaviors

• Cohort analysis for understanding user retention patterns

A/B testing capabilities

• Basic experiment setup within the analytics interface

• Statistical significance testing for conversion metrics

• Integration with existing event data for experiment analysis

Reporting and visualization

• Funnel analysis to identify conversion bottlenecks

• Retention reports showing user engagement over time

• Custom dashboards for tracking key product metrics

User management

• User profiles with complete event histories

• Behavioral cohorts for targeted analysis

• Cross-platform user identification and tracking

Pros vs. Eppo

Unified analytics and experimentation

Mixpanel combines deep product analytics with A/B testing in a single platform. This integration eliminates the need to switch between tools for experiment analysis.

Strong event tracking foundation

The platform excels at capturing and analyzing user behavior data. Teams can leverage existing event streams for both analytics and experimentation without additional setup.

User-friendly interface

Mixpanel's interface makes it accessible to non-technical team members. Product managers can set up experiments and analyze results without SQL knowledge.

Comprehensive user insights

The platform provides detailed user profiles and behavioral analysis. Teams gain context about experiment participants beyond just conversion metrics.

Cons vs. Eppo

Limited statistical rigor

Mixpanel's A/B testing features lack advanced statistical methods like CUPED or sequential testing. The platform focuses more on basic significance testing than sophisticated experiment design.

No warehouse-native deployment

Teams must send data to Mixpanel's servers rather than analyzing within their own infrastructure. This approach limits data control and may raise privacy concerns for some organizations.

Manual event implementation required

Setting up proper event tracking requires significant engineering effort upfront. Teams must instrument events manually rather than leveraging existing warehouse data.

Experimentation as secondary feature

A/B testing capabilities feel like an add-on rather than a core platform strength. The experimentation features may not match dedicated platforms in terms of depth and functionality.

Closing thoughts

Finding the right Eppo alternative depends on your team's specific needs and constraints. Statsig stands out for teams wanting comprehensive experimentation with flexible deployment options. PostHog and GrowthBook appeal to organizations prioritizing open-source solutions and data control. Traditional platforms like LaunchDarkly and Optimizely serve enterprises with established workflows, while Amplitude and Mixpanel work best for teams already invested in their analytics ecosystems.

The key is matching platform capabilities to your actual requirements: statistical rigor, deployment flexibility, team accessibility, and total cost of ownership. Start with a clear understanding of your experimentation maturity and growth trajectory. Most platforms offer trials or free tiers—take advantage of these to test integration complexity and team adoption before committing.

For teams ready to explore these alternatives, check out detailed comparisons, case studies, and implementation guides on each platform's documentation site. The experimentation community also maintains active forums where practitioners share real-world experiences and best practices.

Hope you find this useful!