7 Best Mobile A/B Testing Tools in 2025

Mobile apps operate on fundamentally different release cycles than web applications - you can't just push a fix when something breaks. Apple's App Store review can take days, Google Play updates roll out gradually, and users control when they update. This reality makes A/B testing essential for mobile teams who need to validate features before committing to a release that might live on devices for weeks or months.

Traditional experimentation platforms weren't built for these constraints. They drain battery life with constant network requests, bloat app size with heavy SDKs, and fail to handle offline scenarios gracefully. A proper mobile A/B testing tool needs sub-millisecond evaluation speeds, minimal SDK footprint, and robust offline support - plus the statistical rigor to make confident decisions from smaller mobile user bases.

This guide examines seven options for mobile A/B testing that address delivering the experimentation capabilities teams actually need.

Statsig

Overview

Statsig combines experimentation, feature flags, analytics, and session replay in one platform designed specifically for modern product teams. The platform handles over 1 trillion events daily while maintaining sub-millisecond SDK performance that mobile apps require. Unlike legacy tools that bolt on mobile support, Statsig built its infrastructure from day one to handle the unique constraints of mobile development.

The platform offers both warehouse-native and hosted deployment options - teams can keep sensitive data in their own Snowflake or BigQuery instances, or use Statsig's managed infrastructure. This flexibility has attracted companies like OpenAI and Notion who need enterprise-grade experimentation without the typical enterprise complexity. The unified approach means metrics stay consistent whether you're testing a new onboarding flow or analyzing session replays.

"With mobile development, our release schedule is driven by the App Store review cycle, which can sometimes take days. Using Statsig's feature flags, we're able to move faster by putting new features behind delayed and staged rollouts, and progressively testing the new features." — Paul Frazee, CTO, Bluesky

Key features

Statsig delivers enterprise-grade mobile experimentation with advanced statistics and performance optimization built for scale.

Advanced experimentation capabilities

• CUPED variance reduction cuts experiment runtime by 30-50% through pre-experiment data

• Sequential testing enables early stopping when results reach statistical significance

• Switchback tests measure network effects in marketplace and social apps

• Stratified sampling ensures balanced user allocation across segments

Mobile-optimized infrastructure

• <1ms SDK evaluation after initialization keeps apps responsive

• 30+ native SDKs including Swift, Kotlin, React Native, and Flutter

• Edge computing support reduces latency for global mobile apps

• Offline mode caches assignments when devices lose connectivity

Automated safeguards

• Metric guardrails automatically pause experiments harming key metrics

• Real-time health checks monitor SDK performance and data quality

• Automatic rollbacks trigger when metrics move beyond thresholds

• Exposure event validation ensures accurate user assignment tracking

Unified platform benefits

• Single metrics catalog eliminates discrepancies between tools

• Feature flag integration turns any release into an A/B test

• Session replay linking shows actual user behavior in test variants

• Warehouse native option keeps sensitive data in your infrastructure

"Implementing on our CDN edge and in our nextjs app was straight-forward and seamless. We use Trunk Based Development and without Statsig we would not be able to do it." — G2 Review

Pros

Most affordable enterprise solution

Statsig offers unlimited feature flags at all usage levels, making it the most cost-effective option for growing mobile teams. The free tier includes 2M events monthly plus 50K session replays - enough for serious experimentation programs to get started.

Developer-first experience

Engineers praise the transparent SQL queries and open-source SDKs that show exactly how metrics are calculated. The platform integrates cleanly with existing CI/CD pipelines, and documentation actually covers the edge cases you'll hit in production.

Proven scale and reliability

Processing 6 trillion events monthly across 2.5 billion users isn't just a vanity metric - it means the infrastructure handles traffic spikes without breaking a sweat. Microsoft and Atlassian trust Statsig for mission-critical experiments where downtime isn't an option.

Responsive support team

The shared Slack channel connects you directly with Statsig engineers who understand your technical challenges. Customer success teams include actual data scientists who can help design complex experimental frameworks, not just troubleshoot basic issues.

"Our engineers are significantly happier using Statsig. They no longer deal with uncertainty and debugging frustrations. There's a noticeable shift in sentiment—experimentation has become something the team is genuinely excited about." — Sumeet Marwaha, Head of Data, Brex

Cons

Initial event instrumentation required

You'll need to implement tracking events before running experiments - typically a 1-2 sprint investment depending on app complexity. Warehouse-native deployments also require existing data pipelines to be in place.

Feature depth for simple use cases

Marketing teams running basic landing page tests might find the statistical features overwhelming. The platform clearly targets product and engineering teams who need rigorous experimentation, not simple A/B testing.

Limited visual experiment builder

Creating experiments requires understanding metrics and segments at a technical level. Non-technical users will need training on statistical concepts since visual WYSIWYG editors aren't the primary interface.

Firebase A/B Testing

Overview

Firebase A/B Testing integrates directly with Google's Remote Config and Analytics to enable mobile experimentation without code deployments. Built specifically for developers already using Firebase services, it offers a zero-friction path to testing app parameters and user engagement metrics. The tight integration means you can modify experiment variables instantly across your entire user base without waiting for app store approvals.

Google's machine learning algorithms automatically optimize experiments by identifying winning variants based on your chosen success metrics. This automation reduces the manual monitoring burden, though it comes at the cost of statistical transparency. Firebase targets mobile developers who prioritize speed and simplicity over advanced experimental design capabilities.

Key features

Firebase delivers mobile-first experimentation through deep integration with Google's ecosystem and automated optimization.

Mobile-first experimentation

• Native Android and iOS SDK integration with Firebase suite

• Remote Config parameter testing without app store submissions

• Real-time experiment parameter updates across user sessions

• Automatic variant selection based on performance data

Automated optimization

• Machine learning algorithms select winning variants automatically

• Predictive analytics identify likely experiment outcomes early

• Smart traffic allocation adjusts user distribution during tests

• Goal-based optimization for conversion metrics

Google ecosystem integration

• Seamless Analytics 4 event tracking and conversion measurement

• BigQuery data export for advanced analysis workflows

• Cloud Messaging integration for targeted user communications

• Authentication service connection for user segmentation

Audience management

• Automatic user segmentation based on app behavior patterns

• Custom audience creation using Analytics demographic data

• Geographic and device-based targeting without manual setup

• Cohort analysis for understanding user retention

Pros

Zero-cost entry point

Firebase's generous free tier covers most startups through their growth phase. You can run multiple concurrent experiments without hitting billing thresholds until you reach significant scale.

Instant deployment capability

Remote Config changes propagate immediately without app updates. This speed advantage is crucial when you need to react quickly to user feedback or market changes.

Effortless Firebase integration

If you're already using Firebase services, A/B testing requires minimal additional setup. The unified SDK and consistent user identification across services eliminate integration headaches.

Machine learning automation

Google's algorithms handle the heavy lifting of traffic allocation and winner selection. For teams without dedicated data scientists, this automation provides sophisticated optimization out of the box.

Cons

Limited statistical rigor

Firebase lacks advanced methods like CUPED variance reduction or sequential testing that can significantly reduce experiment runtime. You're stuck with basic t-tests that might miss subtle but important effects.

Opaque calculation methods

The platform doesn't show how it calculates significance or makes decisions. This black-box approach makes it impossible to verify results or adjust confidence levels for different risk tolerances.

Google ecosystem lock-in

Extracting data requires BigQuery setup, and advanced features depend heavily on other Google Cloud services. Teams using mixed analytics stacks face real integration challenges.

Mobile-only focus

Firebase can't handle web or server-side experiments effectively. Companies with multi-platform products need separate tools, increasing complexity and costs across their experimentation stack.

Optimizely

Overview

Optimizely entered the market as a visual website optimizer for marketers but has since expanded into full-stack experimentation. The platform now supports mobile apps through SDKs, though its architecture still reflects those web-first origins. Enterprise customers with deep pockets form their core market - the pricing model requires significant budget commitments before accessing features that come standard elsewhere.

The legacy architecture creates complexity that newer platforms avoid. Mobile teams often struggle with heavy SDKs that impact app performance, while the visual editor that made Optimizely famous provides little value for native app development. Still, their decade-plus experience means they've encountered nearly every edge case in experimentation.

Key features

Optimizely provides comprehensive experimentation capabilities wrapped in enterprise governance and compliance features.

Visual editor and web optimization

• Drag-and-drop interface lets marketers create tests without developer involvement

• A/B testing capabilities for web pages and content personalization

• Real-time preview shows changes before experiments go live

• Multi-page funnel experiments track conversion paths

Full-stack experimentation

• SDKs support mobile and server-side testing across programming languages

• Feature flagging enables controlled rollouts and instant rollbacks

• Holdout groups measure long-term impact of experiment programs

• Multi-armed bandit algorithms for dynamic traffic allocation

Statistical engine

• False discovery rate control prevents inflated significance

• Sequential testing allows early stopping at statistical significance

• Mutual exclusivity ensures experiments don't interfere

• Custom primary metrics and guardrail monitoring

Enterprise governance

• Approval workflows require stakeholder sign-off before launch

• Audit trails track every change for compliance requirements

• Role-based permissions control experiment access

• Program management dashboards for executive reporting

Pros

Mature platform with proven track record

A decade of running enterprise experiments means Optimizely has seen it all. Their platform handles complex scenarios that might trip up newer competitors.

Strong security and compliance features

ISO certifications and SOC 2 compliance satisfy regulated industries. Financial services and healthcare companies appreciate the comprehensive audit trails and approval workflows.

Professional services and support

Dedicated teams help design experiment programs and train internal staff. This hand-holding appeals to organizations without existing experimentation expertise.

Visual editor reduces technical barriers

Marketing teams can launch web experiments without developer resources. This self-service capability works well for content and design testing on websites.

Cons

Expensive pricing that scales poorly

Experimentation platform costs analysis shows Optimizely among the priciest options. Monthly active user pricing can reach tens of thousands per month for growing apps.

Heavy SDK impact on performance

Mobile SDKs add over 100KB to app bundles - a significant weight that impacts download rates and app store rankings. Performance-conscious teams often look elsewhere.

Limited analytics require third-party integrations

Basic reporting covers experiment results, but deeper product analytics need separate BI tools. You'll end up paying for multiple platforms to get complete insights.

Complex setup and maintenance overhead

The platform's flexibility comes with configuration complexity requiring dedicated resources. Many teams need months of setup time before running their first meaningful experiment.

LaunchDarkly

Overview

LaunchDarkly built its reputation as the enterprise standard for feature flag management, with experimentation capabilities added later as an optional module. The platform excels at sophisticated targeting rules and instant feature toggles, making it the go-to choice for risk-averse enterprises that prioritize control over statistical rigor. Their global relay proxy network ensures flags evaluate quickly worldwide, though this infrastructure focus comes at a steep price.

The experimentation add-on feels secondary to their core feature management strengths. While you can run A/B tests on features controlled by flags, the statistical analysis lacks the depth of dedicated experimentation platforms. Teams often find themselves paying premium prices for basic testing functionality that requires significant additional tooling to match competitors' capabilities.

Key features

LaunchDarkly delivers enterprise-grade feature management with experimentation as an additional layer.

Feature flag management

• Advanced targeting rules with percentage rollouts and custom attributes

• Approval workflows and change management for enterprise compliance

• Real-time flag updates with instant kill-switches for rollbacks

• Flag retirement workflows to prevent technical debt accumulation

Experimentation add-on

• Basic A/B testing functionality integrated with existing flags

• Limited metric support compared to dedicated platforms

• Statistical analysis requires manual interpretation

• Custom event tracking through SDK instrumentation

Enterprise infrastructure

• Global relay proxy network reduces latency worldwide

• SOC 2 Type II compliance and security certifications

• Extensive SDK support covering all major platforms

• Edge computing capabilities for flag evaluation

Integration capabilities

• API-first architecture enables custom workflows

• Data export options for analytics connections

• Webhook support for triggering external systems

• Audit log streaming to SIEM platforms

Pros

Enterprise-grade security and compliance

SOC 2 Type II certification and comprehensive audit trails reassure large organizations. The security posture meets requirements that keep procurement teams happy.

Powerful targeting and segmentation

Targeting rules go far beyond simple percentage rollouts. Complex boolean logic, custom attributes, and user cohorts enable precise feature control.

Global infrastructure and performance

The relay proxy network ensures low latency worldwide while handling billions of flag evaluations. This scale matters for global mobile apps with distributed user bases.

Comprehensive SDK ecosystem

SDK coverage spans virtually every language and framework including specialized support for React Native, Flutter, and Unity. Integration is straightforward regardless of tech stack.

Cons

Expensive pricing model

LaunchDarkly's pricing becomes prohibitive at scale, charging per flag evaluation rather than offering unlimited usage. High-traffic apps face budget-breaking cost increases.

Limited experimentation capabilities

The experimentation module lacks CUPED, sequential testing, or other variance reduction methods. You'll need separate tools for sophisticated A/B testing, defeating the purpose of an integrated platform.

Data synchronization challenges

API polling for data warehouse integration leads to metric drift and delayed insights. Real-time analytics alignment remains elusive without significant custom engineering.

Complex setup and maintenance

Enterprise focus means extensive configuration that overwhelms smaller teams. You'll need dedicated DevOps resources just to keep the platform running smoothly.

PostHog

Overview

PostHog positions itself as an open-source alternative to commercial product analytics platforms, bundling feature flags and A/B testing alongside its core analytics offering. The platform appeals to privacy-conscious startups who want to self-host their data rather than send it to third-party services. However, this bundled approach creates a pricing model that charges separately for each capability - making it surprisingly expensive when you need multiple features.

According to pricing analysis, PostHog consistently costs 2-3x more than alternatives across typical usage scenarios. The self-hosting option requires significant infrastructure investment and ongoing maintenance that many teams underestimate. While the open-source model provides transparency, it also means you're responsible for scaling, security, and updates.

Key features

PostHog combines product analytics, feature management, and experimentation in an integrated open-source platform.

Analytics and tracking

• Autocapture automatically tracks user interactions without manual setup

• Session replay records user sessions for qualitative analysis

• JavaScript SDK enables quick web implementation

• Custom event properties for detailed behavioral tracking

Feature management

• Feature flags support percentage rollouts and targeting

• Boolean flags provide simple on/off functionality

• Release management includes scheduling and rollouts

• Multi-variate flags for complex feature variations

Experimentation

• A/B testing uses sequential testing methodology

• Experiment setup integrates with flag infrastructure

• Basic statistical analysis with confidence intervals

• Conversion funnel analysis for experiment variants

Infrastructure options

• Self-hosted deployment gives full data control

• Cloud hosting available for managed infrastructure

• Plugin marketplace extends core functionality

• Data export APIs for warehouse integration

Pros

Open-source transparency

The transparent codebase lets teams customize functionality for unique requirements. You can modify the platform to fit specific mobile stacks or data processing needs.

Self-hosting capabilities

Self-hosted deployment satisfies strict privacy requirements and data governance policies. Your user data never leaves your infrastructure, which matters for regulated industries.

Integrated toolset

All tools share the same data foundation, reducing setup complexity. The unified interface means less context switching between analytics and experimentation.

Plugin ecosystem

Community-built extensions add specialized functionality through the marketplace. Popular plugins include data warehouse connectors and custom visualization tools.

Cons

Expensive pricing structure

Separate charges for flags, experiments, and replays accumulate quickly. Feature flag pricing analysis shows PostHog as the second most expensive option after LaunchDarkly.

High maintenance overhead

Self-hosted deployments require significant server management and scaling work. You'll need dedicated infrastructure resources that smaller teams rarely have available.

Limited mobile SDK capabilities

Mobile SDKs lag behind web counterparts in features and performance. Android and iOS implementations miss functionality that JavaScript users take for granted.

Basic experimentation features

A/B testing lacks variance reduction, automated guardrails, or other advanced methods. The sequential testing provides basic analysis but misses sophisticated techniques that reduce experiment runtime.

Split

Overview

Split takes a unique approach as a data-first feature delivery platform, automatically deriving experiment analytics from feature flag impression streams. Their architecture processes every flag interaction to generate insights without manual instrumentation - an approach that appeals to engineering teams tired of maintaining separate analytics events. The platform emphasizes real-time data processing and enterprise compliance features that larger organizations require.

The automated impact measurement sounds compelling, but it assumes your feature flags map cleanly to business metrics. Mobile teams often find this model limiting when experiments span multiple features or require custom success metrics. Split targets engineering-heavy organizations that value automation over flexibility in their experimentation programs.

Key features

Split centers on automated experimentation through feature flag impressions and enterprise delivery infrastructure.

Real-time analytics and alerting

• Automatic exposure logging captures every flag interaction

• Real-time alerting triggers when metrics exceed thresholds

• Impact dashboards visualize feature performance automatically

• Anomaly detection identifies unexpected metric movements

Enterprise compliance and workflow integration

• Change audit logs track every flag modification with attribution

• Git workflow integrations enable flag management through PRs

• SLA guarantees provide uptime commitments for critical features

• RBAC controls limit access to sensitive experiments

Advanced targeting and delivery

• Percentage rollouts enable gradual feature releases

• User segmentation supports complex targeting rules

• Multi-environment support separates dev, staging, and production

• Traffic allocation algorithms ensure statistical validity

SDK and integration ecosystem

• Client and server SDKs cover major languages and frameworks

• API-first architecture enables custom integrations

• Streaming clients provide low-latency flag evaluations

• Data pipeline connectors for major warehouses

Pros

Automatic exposure tracking

Split eliminates manual event logging by capturing flag impressions automatically. This reduces implementation overhead and ensures consistent data collection across experiments.

Strong enterprise SLAs

Uptime guarantees and performance commitments satisfy large organizations. Compliance features and audit trails help teams meet regulatory requirements while maintaining velocity.

Git-based workflow integration

Managing flags through pull requests maintains existing development practices. This integration helps teams adopt feature flags without changing their workflow.

Real-time impact measurement

Built-in analytics measure feature impact immediately without separate tools. Teams get instant feedback on how changes affect key metrics.

Cons

Limited statistical methods

Analysis relies primarily on t-tests without advanced techniques. Teams requiring CUPED or sequential testing need additional statistical tools.

Opaque enterprise pricing

Pricing becomes unclear above 200K monthly active users. The lack of transparency complicates budget planning for growing organizations.

SDK performance impact

iOS and Android SDKs add noticeable weight due to streaming requirements. Mobile teams must balance feature capabilities against app size concerns.

Rigid measurement model

The flag-to-metric mapping doesn't handle complex experiments well. Custom success metrics or multi-feature tests require workarounds that complicate analysis.

Eppo

Overview

Eppo takes a fundamentally different approach as a warehouse-native experimentation platform built for analytics teams who refuse to compromise on data control. Rather than requiring you to send events to external systems, Eppo operates directly within your Snowflake, BigQuery, or Databricks environment. All statistical calculations happen through transparent SQL queries you can inspect and modify - a level of transparency that appeals to data teams burned by black-box platforms.

This architecture makes sense for organizations with mature data infrastructure and dedicated analytics resources. However, mobile teams often struggle with Eppo's limited feature management capabilities. You'll need to build custom gating logic around exposure events rather than using built-in feature flags, adding complexity to what should be straightforward mobile experiments.

Key features

Eppo delivers warehouse-native architecture with statistical flexibility for data-driven experimentation teams.

Warehouse-native architecture

• Direct connection to existing data warehouses without data movement

• Support for Snowflake, BigQuery, Redshift, and Databricks

• Column-level security maintains warehouse permissions

• Zero data replication reduces compliance concerns

Statistical analysis

• Both Bayesian and frequentist inference methods available

• CUPED variance reduction increases experiment sensitivity

• Customizable statistics framework for advanced use cases

• Transparent SQL queries show exact calculations

Metrics and dashboards

• Pre-computed metrics registry ensures consistent definitions

• Looker-style dashboards for data exploration

• Custom metric creation with SQL-based definitions

• Automated metric health monitoring and alerts

SDK and implementation

• Lightweight SDK focuses on assignment logging only

• Minimal client-side performance impact

• Server-side SDKs for backend experimentation

• Exposure event validation prevents data quality issues

Pros

Data security and governance

Sensitive data never leaves your warehouse environment. Existing security protocols and access controls remain intact without additional compliance overhead.

Statistical transparency

Open SQL queries let you verify every calculation. Data teams can understand and trust results rather than accepting black-box outputs.

Warehouse optimization

Leverage existing infrastructure investments and compute resources. Your data engineering workflows continue unchanged without additional ETL processes.

Flexible statistics

Supporting both Bayesian and frequentist approaches gives teams options. Advanced users can customize the framework to match specific analytical needs.

Cons

Limited feature management

Eppo lacks feature flag rollout controls and progressive deployment. You'll need separate tools for feature management beyond basic assignment.

Engineering overhead

Data engineers must maintain ETL pipelines to populate experiment data. This requirement creates bottlenecks without dedicated data engineering resources.

Missing product features

No session replay, autocapture, or comprehensive product analytics. Teams need additional tools for complete user behavior visibility.

Mobile implementation complexity

Building custom gating logic around exposure events requires more development work than full-featured experimentation platforms. Mobile teams often find this approach cumbersome.

Closing thoughts

Choosing the right mobile A/B testing platform depends on your team's specific constraints and priorities. If you need a unified solution that handles the complete experimentation lifecycle with minimal overhead, Statsig offers the best balance of features, performance, and cost. For teams already embedded in Google's ecosystem, Firebase provides a frictionless starting point. Organizations with mature data infrastructure might prefer Eppo's warehouse-native approach.

The key is matching platform capabilities to your mobile development reality. Consider SDK performance impact, offline support, and release cycle integration before committing. Remember that the most sophisticated statistics won't help if your SDK slows down the app or your team can't implement experiments efficiently.

For more insights on experimentation platforms and pricing comparisons, check out the detailed cost analysis and feature flag platform comparison. Your mobile users deserve experiments that enhance rather than degrade their experience.

Hope you find this useful!