7 Best A/B Testing Platforms for Mobile Apps 2025

1. Amplitude Feature Experimentation

Amplitude Feature Experimentation combines product analytics and A/B testing in one platform. Teams can run experiments and measure results using the same events and metrics across web and mobile apps.

The platform connects directly to data warehouses, so experiment results align with other business metrics. enable controlled rollouts without waiting for app store approval.

• Key strengths: Unified analytics and experimentation, real-time segmentation, consistent cross-platform metrics for small startups to large enterprises.
• Best for: Marketing and product teams that want integrated measurement and testing capabilities, all in one easy-to-use platform.

2. Firebase A/B Testing

Firebase A/B Testing is Google's mobile development platform with built-in experimentation. It works well within the Google ecosystem but has limited integrations with other analytics tools.

The platform offers a free tier and connects to Google Analytics for basic reporting. Remote Config enables feature toggles without app updates.

• Key strengths: Free tier, Google ecosystem integration, remote configuration
• Limitations: Limited advanced segmentation, point solution approach requiring separate analytics

3. Optimizely Feature Experimentation

focuses on enterprise experimentation with full-stack testing capabilities. The platform supports both mobile but requires a separate analytics tool for detailed measurement.

Teams can run feature flags and gradual rollouts across multiple platforms from a single dashboard.

• Key strengths: Enterprise features, full-stack testing, comprehensive feature flag management
• Limitations: Requires separate analytics platform, complex pricing structure

4. Apptimize

Apptimize specializes in mobile-first A/B testing with visual editors for UI changes. Now owned by Airship, it focuses primarily on marketing and engagement use cases.

The platform offers native mobile SDKs and drag-and-drop experiment creation for non-technical users.

• Key strengths: Mobile-native SDKs, visual experiment editor, quick UI testing
• Limitations: Limited analytics depth, primarily marketing-focused features

5. AB Tasty

AB Tasty combines mobile A/B testing with personalization features. The platform emphasizes e-commerce and marketing experimentation with AI-driven content optimization.

Teams can create experiments using drag-and-drop interfaces and target specific user segments.

• Key strengths: AI-powered personalization, user-friendly interface, e-commerce focus
• Limitations: Less suitable for product teams, requires separate analytics for deep analysis

6. Statsig

Statsig is a modern experimentation platform built for engineering teams working at scale. It emphasizes SDK performance and data warehouse integrations for mobile testing.

The platform offers high-performance SDKs and connects directly to cloud for consistent metrics.

• Key strengths: Lightweight SDKs, warehouse-native architecture, developer-focused tools
• Limitations: Newer platform with fewer ecosystem integrations

7. LaunchDarkly

LaunchDarkly primarily focuses on feature flag management with basic mobile A/B testing capabilities. The platform emphasizes deployment control and developer workflows rather than experiment analytics.

Teams use it mainly for controlled feature rollouts and risk mitigation during releases.

• Key strengths: Robust feature flag management, developer tools, deployment controls
• Limitations: Limited experimentation analytics, requires separate measurement tools

Lightweight SDK and offline support

An SDK (Software Development Kit) adds experimentation code to your mobile app. Mobile devices have limited memory and battery life, so lightweight SDKs prevent performance issues.

Offline evaluation assigns experiment variants on the device without contacting servers. This ensures consistent user experiences even when network connections are poor.

Key considerations include:

• Battery impact: Minimize background processing and network requests
• App size: Keep SDK footprint small to avoid bloating app downloads
• Network reliability: Handle slow connections and offline scenarios

Feature flags and controlled rollouts

Feature flags act as on/off switches for app features that you can control remotely. gradually expose new features to increasing percentages of users.

This approach reduces risk by limiting the impact of potential issues and enables instant rollbacks without app store resubmission.

Benefits include:

• Risk reduction: Limit exposure if problems occur
• Instant rollback: Disable features immediately when issues arise
• Staged releases: Progress from small tests to full launches

Real-time analytics integration

Unified measurement means using the same events and metrics across iOS, Android, and web platforms. This consistency prevents metric drift where the same KPI shows different values on different platforms.

Real-time processing updates experiment results continuously rather than in daily batches. Teams can make decisions faster when they see results update immediately.

Key capabilities:

• Consistent definitions: Same event names and calculations across platforms
• Cross-platform attribution: Track user behavior across devices
• Statistical significance: Valid inference using proper statistical methods

App store approval delays

Traditional A/B tests that require new app versions face delays from app store review processes. Apple and Google can take days or weeks to approve updates, slowing experiment cycles.

Feature flags and remote configuration move experiment logic to servers rather than app code. This enables testing without waiting for store approval.

Solutions include:

• Server-side experiments: Run tests without app updates
• Remote configuration: Change app behavior through API calls
• Feature toggles: Turn features on/off instantly

Slow result analysis

Analysis paralysis happens in mobile testing when teams collect data but delay decisions because results arrive slowly or are hard to interpret. Batch processing systems update metrics once daily, creating long feedback loops.

Real-time analytics stream mobile experiment data continuously and update dashboards immediately. Teams can spot trends and make decisions as data arrives.

Improvements include:

• Real-time dashboards: See results update continuously
• Automated alerts: Get notified when significance is reached
• Integrated analysis: View experiments alongside user behavior data

Data fragmentation across tools

When mobile experimentation and analytics live in separate systems, user identities and metric definitions often diverge. This fragmentation makes it difficult to trust results or reproduce findings.

Unified platforms keep analytics and experimentation data in sync. Data warehouse connections align schemas and definitions across different tools.

Solutions include:

• Integrated platforms: Single source of truth for users and metrics
• Consistent user tracking: Same identifiers across all tools
• Unified event schemas: Standardized data collection across platforms

Map your use cases and traffic

Start by identifying your primary testing goals. Common mobile app use cases include onboarding flow optimization, feature adoption experiments, and monetization tests.

Traffic volume affects how long experiments take to reach . Higher-traffic apps can detect smaller changes, while lower-traffic apps may only catch major differences.

Consider these factors:

• Experiment complexity: Simple UI changes vs. algorithm modifications
• Expected effect size: Large changes are easier to detect than small ones
• Statistical power: How confident you want to be in results

Check integration capabilities

Customer Data Platforms (CDPs) collect user data from multiple sources and create unified customer profiles. Data warehouses store analytics data for SQL-based analysis and reporting.

Strong integrations reduce data silos and enable consistent metrics across tools. Popular connections include Snowflake, BigQuery, and major customer data platforms.

Integration types to evaluate:

• Real-time data sync: Immediate experiment result updates
• User identity matching: Consistent tracking across platforms
• Metric alignment: Same calculations in different tools

Compare pricing models

A/B testing platforms use different pricing approaches. Event-based pricing charges per user action tracked. User-based pricing charges per monthly active user. Flat-rate pricing offers unlimited usage for a fixed fee.

Consider your experiment volume, user base size, and required features when evaluating costs.

Pricing factors include:

• Monthly active users: How many people use your app
• Experiment volume: Number of concurrent tests
• Advanced features: Statistical methods, integrations, support level

Start with a simple pilot

Many teams begin with a low-risk feature flag to validate their setup. A simple on/off test for a single feature helps you learn the platform without major consequences.

Choose a feature that affects a small user segment and has clear success metrics. This approach reduces risk while you build confidence with the new tool.

Pilot strategy elements:

• Single feature focus: Don't test multiple things at once
• Small user segment: Limit initial exposure
• Clear success metrics: Define what improvement looks like

Amplitude brings analytics and experimentation together in a single platform. Teams run using the same events and metrics that power their product analytics, eliminating data silos between tools.

The warehouse-native architecture ensures experiment results align with business metrics stored in your data warehouse. Cross-platform measurement uses consistent user identities and event definitions across iOS, Android, and web.

Unlike point solutions that require multiple tool integrations, Amplitude connects experiment readouts directly to , retention cohorts, and behavioral segmentation. Teams see how variants affect real product outcomes, not just .

Key advantages include unified analytics and experimentation, consistent cross-platform metrics, real-time behavioral segmentation, and integrated user journey analysis.