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Top 7 Alternatives to Convert for A/B Testing
Teams exploring alternatives to Convert typically cite similar concerns: limited statistical methods, lack of integrated analytics, complex pricing structures, and minimal support for modern experimentation workflows.
Convert's standalone approach to A/B testing creates workflow inefficiencies when teams need to connect experiment results with feature management or behavioral analytics. The platform's traditional testing methods struggle to deliver the speed and sophistication that data-driven organizations now expect. Modern alternatives address these gaps by combining experimentation with complementary capabilities like feature flags, session replay, and advanced statistical techniques. These integrated platforms enable teams to move faster while maintaining rigorous experimental standards.
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 exceed Convert's offerings. The platform processes over 1 trillion events daily with 99.99% uptime, serving billions of users across companies like OpenAI, Notion, and Atlassian.
Unlike Convert's standalone testing approach, Statsig combines experimentation, feature flags, analytics, and session replay in one platform. This integration eliminates data silos and enables teams to move from insight to action without switching tools.
"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 offers comprehensive A/B testing features that rival any enterprise experimentation platform.
Advanced statistical methods
CUPED variance reduction cuts experiment runtime by detecting smaller effects faster
Sequential testing and switchback testing for complex experimental designs
Bonferroni correction and Benjamini-Hochberg procedures prevent false positives in multi-variant tests
Enterprise experimentation infrastructure
Warehouse native deployment supports Snowflake, BigQuery, Redshift, and Databricks
Real-time health checks and guardrails automatically halt harmful experiments
Mutually exclusive experiments prevent interference between concurrent tests
Comprehensive metric support
Custom metrics with Winsorization, capping, and advanced filters
Growth accounting metrics including retention, stickiness, and churn
Percentile-based metrics for performance and latency tracking
Developer-friendly tools
30+ SDKs across every major programming language and edge computing platforms
One-click SQL transparency shows exact queries behind every result
Experiment templates and automated summaries speed up test creation
"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. Convert
Statistical sophistication beyond Convert
Statsig implements cutting-edge statistical methods unavailable in Convert. Features like CUPED variance reduction and heterogeneous effect detection help teams find meaningful results 30-50% faster.
Unified platform eliminates tool sprawl
While Convert requires separate tools for analytics and feature management, Statsig combines everything. Teams at Brex saved 50% of data scientists' time by consolidating their stack.
Dramatically lower total cost
Statsig's pricing analysis shows it costs 50-80% less than competitors at scale. The platform includes unlimited feature flags and 2M free analytics events monthly.
Proven scale for the largest companies
Processing trillions of events daily, Statsig handles experimentation programs far beyond Convert's capabilities. Companies like OpenAI and Notion run hundreds of concurrent experiments without performance issues.
"Having a culture of experimentation and good tools that can be used by cross-functional teams is business-critical now. Statsig was the only offering that we felt could meet our needs across both feature management and experimentation."
Sriram Thiagarajan, CTO and CIO, Ancestry
Cons vs. Convert
More features than basic testing needs
Teams only needing simple A/B tests might find Statsig's advanced capabilities overwhelming initially. The platform's depth requires some learning investment.
Newer company than Convert
Founded in 2020, Statsig has less market tenure than Convert. However, rapid adoption by leading tech companies validates the platform's maturity.
Limited agency-specific features
Convert offers specialized agency management tools. Statsig focuses more on direct enterprise customers, though agencies successfully use the platform.
Alternative #2: Optimizely
Overview
Optimizely stands as one of the most established players in the A/B testing space. The platform built its reputation on making complex testing accessible through intuitive interfaces and comprehensive feature sets.
While Convert emphasizes speed and simplicity, Optimizely takes a comprehensive approach to experimentation. The platform integrates A/B testing with advanced personalization features, making it particularly attractive for larger organizations with complex testing requirements.
Key features
Optimizely delivers a full-stack experimentation platform designed for enterprise teams running sophisticated testing programs.
Visual experiment builder
Drag-and-drop interface allows non-technical users to create tests without coding
Real-time preview shows exactly how variations will appear to users
WYSIWYG editor supports complex page modifications and element targeting
Advanced personalization engine
Machine learning algorithms automatically optimize content for different user segments
Behavioral targeting delivers personalized experiences based on user actions
Dynamic content delivery adapts in real-time based on user characteristics
Enterprise experimentation capabilities
Server-side testing supports backend changes and complex business logic experiments
Multivariate testing enables simultaneous testing of multiple page elements
Statistical significance calculations include sequential testing methods
Integration and deployment options
REST APIs connect with existing tech stacks and data warehouses
CDN-based delivery ensures fast loading times across global audiences
Mobile SDKs support native app experimentation on iOS and Android
Pros vs. Convert
Visual editor reduces technical barriers
Optimizely's visual editor makes test creation accessible to marketers without coding skills. Teams modify page elements and create variations through an interface that shows changes in real-time.
Comprehensive personalization features
The platform goes beyond basic A/B testing to offer sophisticated personalization that Convert doesn't match. Machine learning algorithms automatically optimize content delivery based on user behavior.
Enterprise-grade infrastructure
Optimizely handles massive scale with proven reliability across Fortune 500 companies. The platform processes billions of decisions daily with sub-100ms response times.
Advanced statistical methods
The platform includes sophisticated approaches like sequential testing and Bayesian analysis. These methods help teams make decisions faster while maintaining statistical rigor.
Cons vs. Convert
Significantly higher pricing
Optimizely's enterprise focus means pricing starts much higher than Convert's accessible plans. Small businesses often find the cost prohibitive.
Complex setup and onboarding
The platform's extensive feature set creates a steeper learning curve. Teams often require dedicated training and longer implementation timelines.
Feature bloat for simple use cases
Organizations seeking straightforward A/B testing may find Optimizely overwhelming. The platform's complexity can slow down simple test creation workflows.
Alternative #3: VWO
Overview
VWO combines quantitative experiment data with qualitative user insights. The platform integrates heatmaps, session recordings, and user surveys directly into your testing workflow - helping teams understand not just what users do, but why they behave in specific ways.
Unlike Convert's focus purely on experimentation, VWO positions itself as a complete conversion optimization suite. The platform serves teams who want to dig deeper into user behavior patterns alongside their A/B testing efforts.
Key features
VWO delivers comprehensive A/B testing capabilities enhanced by behavioral analytics and user research tools.
Experimentation platform
Visual editor allows drag-and-drop test creation without coding requirements
Multivariate testing supports complex experiments with multiple variables
Statistical significance calculations include confidence intervals and sample size recommendations
Behavioral insights
Heatmaps show click patterns, scroll depth, and attention mapping across pages
Session recordings capture complete user journeys for qualitative analysis
Form analytics identify specific fields causing conversion drops
Personalization engine
Dynamic content delivery based on user segments and behavior patterns
Geo-targeting and device-based personalization rules
Integration with customer data platforms for advanced audience targeting
Analytics and reporting
Real-time experiment monitoring with automated alerts for significant changes
Revenue impact tracking connects tests directly to business metrics
Cohort analysis reveals how different user groups respond to variations
Pros vs. Convert
Qualitative data integration
VWO's biggest advantage lies in combining A/B testing with user behavior analysis. Session recordings and heatmaps provide context that pure statistical data can't deliver.
Visual test creation
The drag-and-drop editor makes VWO accessible to marketers without coding skills. You can create and launch tests faster than platforms requiring technical implementation.
Comprehensive conversion toolkit
VWO includes surveys, feedback widgets, and user research tools beyond basic A/B testing. This integrated approach eliminates the need for multiple point solutions.
Strong personalization features
Dynamic content delivery and advanced targeting options exceed Convert's capabilities. VWO's personalization engine adapts experiences based on real-time user behavior.
Cons vs. Convert
Higher complexity and cost
VWO's comprehensive feature set comes with steeper learning curves and higher pricing tiers. The platform can overwhelm teams seeking straightforward functionality.
Performance impact concerns
Loading heatmaps, session recordings, and tracking scripts can slow page performance. This overhead may affect user experience and test validity.
Limited statistical sophistication
VWO lacks advanced methods like CUPED variance reduction or sequential testing. Teams requiring rigorous statistical analysis may find the platform insufficient.
Alternative #4: AB Tasty
Overview
AB Tasty positions itself as an AI-powered experimentation platform that combines A/B testing with personalization capabilities. The platform targets enterprise teams seeking advanced targeting options and real-time campaign optimization.
Unlike Convert's focus on pure experimentation, AB Tasty emphasizes dynamic user experiences through predictive algorithms. Their visual editor allows non-technical users to create tests quickly, though this approach can limit testing complexity compared to Convert's more technical approach.
Key features
AB Tasty delivers experimentation tools with built-in personalization and AI-driven optimization capabilities.
Visual experimentation
Drag-and-drop editor enables quick test creation without coding
Real-time preview shows changes before launching experiments
Template library provides pre-built test variations for common scenarios
AI-powered targeting
Predictive algorithms automatically segment users based on behavior patterns
Dynamic content delivery adjusts experiences based on user preferences
Machine learning optimizes traffic allocation during active experiments
Personalization engine
Real-time content customization adapts to individual user journeys
Behavioral triggers activate specific experiences based on user actions
Cross-channel personalization maintains consistency across touchpoints
Analytics and reporting
Live dashboards display experiment performance as data arrives
Statistical significance calculations help determine winning variations
Cohort analysis tracks long-term impact of successful tests
Pros vs. Convert
Intuitive visual interface
AB Tasty's drag-and-drop editor makes A/B testing accessible to marketers. Teams launch experiments quickly without waiting for developer resources.
AI-driven optimization
The platform's machine learning capabilities automatically optimize traffic allocation. This reduces manual monitoring compared to traditional approaches.
Real-time personalization
AB Tasty combines experimentation with dynamic content delivery for immediate improvements. This integrated approach eliminates the need for separate tools.
Enterprise-focused features
Advanced targeting options and cross-channel capabilities serve large organizations. The platform handles high-traffic scenarios with reliable performance.
Cons vs. Convert
Limited integration ecosystem
AB Tasty offers fewer third-party connections compared to Convert's extensive integration library. This creates workflow bottlenecks for teams using specialized tools.
Higher pricing structure
Enterprise-focused pricing makes AB Tasty expensive for smaller teams. The cost per experiment often exceeds Convert's flexible model.
Reduced statistical control
The visual editor approach limits advanced statistical configurations. Teams requiring custom metrics or complex designs may find the platform restrictive.
Alternative #5: LaunchDarkly
Overview
LaunchDarkly positions itself as the enterprise standard for feature management and controlled rollouts. The platform combines robust feature flagging with A/B testing capabilities, targeting development teams that need precise control over feature releases.
Unlike Convert's focus on conversion optimization, LaunchDarkly emphasizes developer-first workflows and infrastructure-level integrations. Teams use the platform to manage feature lifecycles from development through production, with experimentation built into the release process.
Key features
LaunchDarkly delivers enterprise-grade feature management with integrated A/B testing across multiple platforms and environments.
Feature flag management
Real-time flag updates propagate instantly across all environments
Percentage-based rollouts allow gradual feature releases to specific segments
Environment-specific targeting enables different configurations across stages
A/B testing integration
Native experimentation capabilities turn any feature flag into a controlled test
Statistical analysis provides confidence intervals and significance testing
Multi-variate testing supports complex experiments with multiple treatments
Developer tools and SDKs
25+ SDKs cover major programming languages and frameworks
Edge computing support reduces latency through distributed evaluation
Webhook integrations connect releases to monitoring and deployment tools
Enterprise controls
Role-based permissions control who can modify flags across teams
Audit logs track all changes with detailed attribution
Approval workflows require review before sensitive changes reach production
Pros vs. Convert
Developer-centric approach
LaunchDarkly excels at integrating experimentation directly into development workflows. Engineers create and manage tests without leaving their existing tools.
Infrastructure reliability
The platform handles massive scale with 99.999% uptime guarantees. Feature flag costs vary significantly across providers, but LaunchDarkly's infrastructure justifies premium pricing.
Advanced targeting capabilities
Sophisticated user segmentation allows precise experiment targeting. Teams run multiple concurrent experiments without interference through built-in traffic allocation.
Comprehensive integrations
Native connections to monitoring tools and CI/CD pipelines create seamless workflows. Development teams trigger flag changes from deployment scripts automatically.
Cons vs. Convert
Higher cost structure
LaunchDarkly's pricing scales with monthly active users and becomes expensive quickly. Small teams find the cost prohibitive compared to Convert's model.
Steeper learning curve
The platform requires significant technical knowledge to implement effectively. Non-technical team members struggle with the developer-oriented interface.
Limited conversion optimization focus
While LaunchDarkly supports A/B testing, it lacks Convert's specialized tools. Marketing teams miss features like visual editors and conversion funnel analysis.
Alternative #6: Split.io
Overview
Split.io positions itself as an enterprise-grade feature management platform with strong A/B testing capabilities. The platform emphasizes performance optimization and real-time data processing for high-traffic applications.
Unlike Convert's focus on conversion optimization, Split.io builds around feature delivery and progressive rollouts. The platform integrates feature flagging with experimentation to support modern development practices - teams manage both controlled releases and statistical testing from a unified interface.
Key features
Split.io combines feature management with A/B testing through an engineering-focused platform designed for scale.
Feature flagging and experimentation
Real-time feature toggles with instant rollback capabilities
Built-in A/B testing framework integrated with flag management
Progressive rollout controls with automated traffic allocation
Performance and scalability
Sub-millisecond flag evaluation with edge computing support
High-throughput SDKs optimized for enterprise-scale applications
Real-time metrics processing with minimal latency impact
Advanced targeting
Sophisticated user segmentation with custom attribute support
Multi-variate testing capabilities for complex experimental designs
Dynamic configuration management for personalized experiences
Analytics and insights
Real-time experiment monitoring with statistical significance tracking
Custom metric definition with business KPI integration
Detailed performance analytics across all feature releases
Pros vs. Convert
Enterprise-grade performance
Split.io delivers sub-millisecond response times and handles massive traffic volumes. The platform's edge computing capabilities ensure consistent speed globally.
Integrated feature management
Teams manage feature releases and A/B tests through a single platform. This integration enables progressive experimentation where features roll out gradually while collecting data.
Advanced targeting capabilities
Split.io offers sophisticated user segmentation beyond basic demographics. Custom attributes and behavioral triggers provide precise experiment control.
Real-time analytics
The platform processes experiment data in real-time for faster decision-making. Teams monitor statistical significance and business metrics without batch processing delays.
Cons vs. Convert
Complex interface for beginners
Split.io's feature-rich interface can overwhelm teams new to A/B testing. The learning curve is steeper compared to Convert's conversion-focused design.
Higher implementation overhead
Setting up Split.io requires more technical configuration than Convert. Engineering teams need to integrate multiple SDKs and configure targeting rules.
Cost considerations at scale
Split.io's pricing model becomes expensive as traffic grows. Teams should evaluate feature flag platform costs before committing to enterprise plans.
Alternative #7: Adobe Target
Overview
Adobe Target represents the enterprise tier of A/B testing platforms, designed for large organizations with complex personalization needs. The platform integrates deeply with Adobe's marketing ecosystem, offering AI-powered automation and sophisticated testing capabilities.
While Convert focuses on straightforward experimentation, Adobe Target emphasizes comprehensive customer journey optimization across multiple touchpoints. Enterprise teams often choose Adobe Target for its advanced personalization engine and seamless data flow with other Adobe products.
Key features
Adobe Target delivers enterprise-grade A/B testing with AI-driven personalization and comprehensive audience management capabilities.
Advanced testing capabilities
Multivariate testing supports complex variable combinations across elements
Auto-Target uses machine learning to deliver personalized experiences
Automated Personalization creates individualized content for each segment
AI-powered optimization
Sensei AI analyzes visitor patterns to predict optimal experiences
Real-time decisioning delivers personalized content within milliseconds
Predictive audiences identify high-value visitor segments automatically
Enterprise integration
Native connectivity with Adobe Analytics provides comprehensive insights
Real-time Customer Data Platform enables cross-channel personalization
Adobe Experience Manager integration streamlines content workflows
Advanced audience management
Profile scripts create custom visitor attributes for precise targeting
Geo-targeting delivers location-specific experiences globally
Device and browser targeting optimizes for specific environments
Pros vs. Convert
Enterprise-grade personalization
Adobe Target's AI engine delivers individualized experiences at scale. The platform's machine learning capabilities continuously optimize without manual intervention.
Comprehensive Adobe ecosystem integration
Seamless data flow between Adobe products creates a unified customer view. This integration eliminates data silos that plague multi-tool setups.
Advanced audience segmentation
Sophisticated targeting allows precise visitor categorization. Profile scripts enable custom attribute creation for highly specific definitions.
Real-time optimization capabilities
Auto-Target and Automated Personalization deliver optimized experiences instantly. Machine learning algorithms continuously improve based on interactions.
Cons vs. Convert
Significant cost barrier
Adobe Target's enterprise pricing exceeds smaller teams' budgets substantially. Experimentation platform costs vary widely, with Adobe Target at the premium end.
Implementation complexity
The platform requires extensive technical expertise and dedicated resources. Many organizations need Adobe-certified consultants to maximize capabilities.
Steep learning curve
Adobe Target's advanced features demand significant training time. The interface complexity can overwhelm users accustomed to simpler tools.
Vendor lock-in concerns
Deep integration with Adobe's ecosystem makes switching alternatives challenging. Organizations become dependent on Adobe's roadmap and pricing decisions.
Closing thoughts
Choosing the right Convert alternative depends on your team's specific needs and technical maturity. Statsig offers the most comprehensive solution for teams wanting integrated experimentation with feature management. Optimizely and Adobe Target serve enterprise organizations with complex personalization requirements. VWO and AB Tasty excel at combining qualitative insights with quantitative testing.
For engineering teams, LaunchDarkly and Split.io provide developer-first approaches that integrate experimentation into deployment workflows. Each platform brings unique strengths - the key is matching those capabilities to your organization's experimentation goals and technical constraints.
Additional resources for evaluating experimentation platforms:
Hope you find this useful!