Products
Solutions
Resources
Top 7 Alternatives to Convert for Experimentation
Teams exploring alternatives to Convert typically face similar challenges: limited advanced statistical methods, pricing that scales poorly with traffic, and insufficient server-side testing capabilities for complex experimentation needs.
Convert serves its purpose for basic A/B testing, but modern product teams need more sophisticated experimentation infrastructure. The platform's focus on marketing optimization often leaves engineering teams without the technical depth they require, while its enterprise pricing can surprise growing companies. Strong alternatives address these gaps by offering advanced statistical engines, flexible deployment options, and transparent pricing that scales predictably with usage.
This guide examines seven alternatives that address these pain points while delivering the experimentation capabilities teams actually need.
Alternative #1: Statsig
Overview
Founded in 2020, Statsig delivers enterprise-grade experimentation capabilities that rival Convert's offerings. The platform processes over 1 trillion events daily, supporting companies like OpenAI, Figma, and Notion. Unlike Convert's marketing-focused approach, Statsig provides a comprehensive experimentation engine built for product teams.
Statsig matches Convert's core A/B testing features while adding advanced statistical methods rarely found elsewhere. The platform offers sequential testing, stratified sampling, and variance reduction through CUPED - capabilities that help teams run more sophisticated experiments with greater statistical power.
"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 experimentation platform delivers enterprise features that match or exceed Convert's capabilities.
Advanced statistical engine
Sequential testing allows you to peek at results without inflating false positive rates
CUPED variance reduction increases experiment sensitivity by 30-50%
Automated heterogeneous effect detection surfaces hidden user segments
Flexible deployment options
Warehouse-native deployment keeps your data in Snowflake, BigQuery, or Databricks
Hosted cloud option provides turnkey setup with unlimited scalability
Both models support the same advanced experimentation features
Comprehensive experiment management
Holdout groups measure long-term impact beyond individual tests
Mutually exclusive experiments prevent interference between concurrent tests
Days-since-exposure analysis detects novelty effects automatically
Developer-first infrastructure
30+ SDKs across every major programming language and framework
Less than 1ms evaluation latency after initialization
Transparent SQL queries visible with one click for complete auditability
"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
More sophisticated statistical methods
Statsig offers both Frequentist and Bayesian approaches, plus advanced techniques like Bonferroni correction. Convert focuses primarily on basic Frequentist testing. This flexibility helps teams choose the right statistical approach for their specific use case.
Unified platform beyond experimentation
While Convert specializes in A/B testing, Statsig includes feature flags, analytics, and session replay in one platform. Teams can turn any feature flag into an experiment instantly. This integration eliminates data silos and accelerates the entire product development cycle.
Proven enterprise scalability
Statsig handles experiments for billions of users without performance degradation. Brex reduced experimentation costs by 20% while running 100+ concurrent experiments. Convert's infrastructure hasn't been tested at this scale publicly.
Transparent, affordable pricing
Statsig's pricing scales only with analytics events, not seats or experiments. Feature flags remain free at any volume. Convert requires sales calls for enterprise pricing and charges based on monthly tested users.
"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 vs. Convert
Less marketing-specific functionality
Convert offers pre-built integrations with marketing platforms like HubSpot and Marketo. Statsig focuses on product experimentation rather than marketing optimization. Marketing teams might need additional setup to achieve similar workflows.
Newer platform with growing ecosystem
Founded four years after Convert, Statsig has fewer third-party integrations specifically for CRO tools. The platform compensates with robust APIs and webhooks. Most teams find the core functionality more important than niche integrations.
Different learning curve for marketers
Statsig's interface caters to product teams and engineers rather than marketers. Convert's WYSIWYG editor feels more familiar to non-technical users. However, Statsig's approach enables more precise experiment control and reduces implementation errors.
Alternative #2: Optimizely
Overview
Optimizely stands as one of the most established players in the digital experimentation space. The platform targets enterprise teams who need comprehensive optimization capabilities across web, mobile, and full-stack environments. Unlike Convert's focus on simplicity, Optimizely offers a feature-rich ecosystem designed for complex organizational needs.
The platform has evolved from its origins as a simple A/B testing tool into a full digital experience platform. Today, Optimizely serves thousands of enterprise customers who run sophisticated experimentation programs. Their approach emphasizes both technical depth and business user accessibility through visual editing tools.
Key features
Optimizely provides enterprise-grade experimentation tools with extensive personalization and targeting capabilities.
Experimentation capabilities
Visual editor allows non-technical users to create tests without coding
Server-side testing supports backend and API experimentation
Multivariate testing enables complex statistical analysis across multiple variables
Targeting and personalization
Advanced audience segmentation based on behavioral and demographic data
Real-time personalization engines adapt content dynamically
Cross-channel targeting spans web, mobile, and email touchpoints
Analytics and reporting
Real-time results dashboard provides immediate experiment insights
Statistical significance calculations include confidence intervals and power analysis
Custom metrics tracking supports business-specific KPIs and conversion goals
Platform integrations
Native connections to major analytics platforms like Adobe and Google Analytics
CRM integrations sync experiment data with customer relationship tools
Marketing automation platforms receive targeting and personalization data
Pros vs. Convert
Advanced personalization engine
Optimizely's personalization capabilities extend far beyond basic A/B testing. The platform can deliver dynamic content based on user behavior, location, and historical interactions.
Visual experiment builder
Non-technical team members can create and modify experiments using drag-and-drop interfaces. This democratizes experimentation across marketing and product teams without requiring developer resources.
Enterprise-scale infrastructure
The platform handles high-traffic websites and complex organizational structures with ease. Multi-team workflows and approval processes support large-scale experimentation programs.
Comprehensive integration ecosystem
Optimizely connects with hundreds of third-party tools, enabling seamless data flow across your entire marketing and analytics stack.
Cons vs. Convert
Significantly higher costs
Optimizely's enterprise pricing can reach tens of thousands of dollars annually. Small and medium businesses often find the cost prohibitive compared to more affordable alternatives.
Complex setup and learning curve
The platform's extensive feature set requires substantial onboarding time. Teams often need dedicated training and technical resources to fully utilize the system's capabilities.
Over-engineering for simple needs
Organizations seeking straightforward A/B testing may find Optimizely's complexity overwhelming. The platform's enterprise focus can create unnecessary friction for basic experimentation workflows.
Alternative #3: VWO
Overview
VWO positions itself as a comprehensive conversion optimization platform that combines A/B testing with detailed user behavior analysis. The platform targets marketing teams and conversion rate optimization specialists who need both experimentation capabilities and behavioral insights in one solution.
Unlike Convert's focus on pure experimentation, VWO emphasizes the complete conversion funnel with heatmaps, session recordings, and personalization features. This approach makes VWO particularly appealing for teams that want to understand not just what works, but why it works through visual user behavior data.
Key features
VWO's feature set spans across experimentation, behavior analysis, and conversion optimization tools designed for comprehensive website optimization.
Testing capabilities
A/B testing with visual editor for quick test creation
Multivariate testing for complex variable combinations
Split URL testing for completely different page versions
Behavior analysis tools
Heatmaps showing click, scroll, and attention patterns
Session recordings capturing complete user journeys
Form analytics identifying drop-off points and friction
Analytics and reporting
Real-time experiment results and statistical significance
Revenue impact tracking for business metrics
Cohort analysis for long-term user behavior patterns
Personalization engine
Dynamic content delivery based on user segments
Behavioral targeting using on-site activity data
Geographic and device-based personalization rules
Pros vs. Convert
Comprehensive behavior insights
VWO's heatmaps and session recordings provide visual context that pure experimentation platforms like Convert can't match. Teams can see exactly where users click, scroll, and encounter friction points during their journey.
Visual test creation
The drag-and-drop visual editor allows marketers to create tests without developer involvement. This self-service approach can significantly reduce the time between hypothesis and live experiment compared to more technical platforms.
Integrated personalization
VWO combines experimentation with personalization in a single platform, allowing teams to test personalized experiences. This integration eliminates the need for separate tools and creates more cohesive user experiences.
Conversion-focused analytics
The platform's analytics specifically target conversion metrics and revenue impact rather than general product analytics. This focus aligns well with marketing teams' primary objectives and KPIs.
Cons vs. Convert
Higher cost complexity
VWO's pricing can become expensive when you need the full feature set including heatmaps, recordings, and personalization. The comprehensive feature comparison shows VWO often costs more for equivalent testing capabilities.
Client-side performance impact
VWO's visual editor and behavior tracking tools run primarily client-side, which can impact page load times. This approach contrasts with server-side focused platforms that minimize frontend performance effects.
Limited server-side capabilities
While VWO offers server-side testing, it's not their primary strength compared to platforms built specifically for backend experimentation. Teams with complex server-side requirements might find the capabilities insufficient.
Alternative #4: AB Tasty
Overview
AB Tasty positions itself as an experimentation and personalization platform designed for teams who want to move fast without technical barriers. The platform emphasizes visual editing capabilities and AI-driven personalization, making it accessible to both marketing and product teams. Unlike Convert's focus on statistical rigor, AB Tasty prioritizes ease of deployment and quick time-to-value for experimentation programs.
The platform serves companies looking to combine A/B testing with personalization features in a single solution. AB Tasty's approach centers on reducing the technical overhead typically associated with experimentation platforms.
Key features
AB Tasty combines client-side and server-side testing with personalization tools designed for rapid deployment.
Visual experimentation
Drag-and-drop editor requires no coding knowledge for test creation
Real-time preview shows changes before tests go live
Template library accelerates common test scenarios
AI-powered personalization
Machine learning algorithms recommend content variations automatically
Dynamic content delivery based on user behavior patterns
Predictive targeting identifies high-value user segments
Cross-platform testing
Web, mobile app, and server-side testing from unified dashboard
SDK support for iOS, Android, and major web frameworks
API-first architecture enables custom integrations
Analytics and reporting
Real-time results tracking with statistical significance indicators
Heatmaps and user session recordings provide qualitative insights
Integration with Google Analytics, Adobe Analytics, and other platforms
Pros vs. Convert
Faster setup and deployment
AB Tasty's visual editor lets non-technical team members create and launch tests within minutes. This speed advantage becomes significant for marketing teams who need to iterate quickly on campaigns and landing pages.
Built-in personalization capabilities
The platform combines experimentation with AI-driven personalization features that Convert doesn't offer natively. Teams can move beyond simple A/B tests to deliver dynamic, personalized experiences based on user behavior.
Mobile app testing support
AB Tasty provides dedicated mobile SDKs and testing capabilities that make it easier to run experiments across iOS and Android applications. This cross-platform approach suits teams managing both web and mobile experiences.
Marketing team accessibility
The no-code interface and pre-built templates make experimentation accessible to marketers without requiring developer involvement. This democratization can accelerate testing velocity for marketing-focused use cases.
Cons vs. Convert
Higher pricing for advanced features
AB Tasty's pricing structure can become expensive as you scale, particularly when accessing advanced statistical features or higher traffic volumes. Teams evaluating experimentation platform costs should factor in long-term scaling expenses.
Limited statistical sophistication
The platform lacks some of the advanced statistical methods that Convert offers, such as sequential testing or sophisticated variance reduction techniques. Teams with rigorous statistical requirements may find these limitations restrictive.
AI features may add unnecessary complexity
The built-in personalization and AI recommendations can introduce complexity that not all teams need. Organizations focused purely on A/B testing might prefer Convert's more straightforward approach to experimentation.
Alternative #5: Kameleoon
Overview
Kameleoon delivers AI-powered experimentation and personalization for enterprises seeking data-driven optimization. The platform combines machine learning capabilities with traditional A/B testing to create predictive targeting experiences. Unlike simpler alternatives, Kameleoon focuses heavily on personalization through artificial intelligence algorithms.
Enterprise teams often choose Kameleoon when they need sophisticated segmentation beyond basic demographic splits. The platform processes real-time data to deliver immediate insights and automated decision-making. This approach appeals to organizations with complex user bases requiring nuanced experimentation strategies.
Key features
Kameleoon's feature set centers on AI-driven personalization and enterprise-grade experimentation capabilities.
AI-powered targeting
Machine learning algorithms predict user behavior and segment audiences automatically
Predictive models identify high-value visitors before they convert
Dynamic content optimization adjusts experiences based on real-time user signals
Experimentation infrastructure
Server-side testing supports complex backend experiments without performance impact
Client-side testing handles frontend changes with visual editor capabilities
Hybrid testing combines both approaches for comprehensive optimization programs
Data integration
Native CRM connections sync customer data for personalized experiences
Data platform integrations pull behavioral signals from multiple sources
Custom APIs enable flexible data flows between systems
Real-time processing
Live data streams update experiment results as traffic flows through tests
Instant segmentation adjustments respond to changing user patterns
Automated alerts notify teams when significant changes occur
Pros vs. Convert
Advanced AI capabilities
Kameleoon's machine learning features go beyond traditional A/B testing to predict user behavior. The platform automatically creates segments based on behavioral patterns rather than manual rules.
Enterprise scalability
Server-side testing infrastructure handles high-traffic scenarios without affecting site performance. Complex experiments run simultaneously across multiple touchpoints and user journeys.
Comprehensive data integration
Deep CRM and data platform connections create unified customer profiles for personalization. Real-time data processing enables immediate optimization based on fresh behavioral signals.
Predictive personalization
AI algorithms identify conversion likelihood and adjust experiences accordingly. Dynamic content optimization happens automatically without manual intervention from marketing teams.
Cons vs. Convert
Implementation complexity
Advanced AI features require significant technical resources and longer setup times. Teams need data science expertise to fully leverage machine learning capabilities effectively.
Higher resource requirements
Enterprise-focused features demand more infrastructure and maintenance than simpler alternatives. Organizations must invest in training and ongoing platform management for optimal results.
Cost considerations
AI-powered personalization typically comes with premium pricing compared to basic experimentation tools. Experimentation platform costs vary significantly based on feature complexity and usage volume.
Alternative #6: LaunchDarkly
Overview
LaunchDarkly specializes in feature management and feature flagging for continuous delivery workflows. The platform enables engineering teams to control feature rollouts and mitigate deployment risks through sophisticated flag management. LaunchDarkly focuses primarily on DevOps methodologies rather than marketing-driven experimentation.
While Convert targets conversion optimization and marketing teams, LaunchDarkly serves engineering organizations practicing continuous deployment. The platform emphasizes speed and reliability in production environments over comprehensive A/B testing capabilities.
Key features
LaunchDarkly provides enterprise-grade feature management with extensive developer integrations and deployment controls.
Feature flag management
Granular targeting rules with user segmentation capabilities
Percentage-based rollouts for gradual feature releases
Kill switches for immediate feature deactivation during incidents
Developer workflow integration
Native CI/CD pipeline integrations with popular tools
Broad SDK support across 25+ programming languages
Real-time flag updates without application restarts
Production controls
Environment-specific flag configurations for dev, staging, and production
Audit trails and approval workflows for change management
Performance monitoring and flag usage analytics
Enterprise features
Role-based access controls and team permissions
Custom attributes for advanced user targeting
Webhook integrations for external system notifications
Pros vs. Convert
Superior feature flag capabilities
LaunchDarkly offers more sophisticated feature flagging than Convert's basic toggle functionality. The platform provides advanced targeting rules and user segmentation options that exceed Convert's capabilities.
Developer-first approach
The platform integrates seamlessly with existing development workflows and CI/CD pipelines. Engineers can manage flags directly from their development environment without switching contexts.
Production reliability
LaunchDarkly's infrastructure handles high-scale deployments with minimal latency impact. The platform provides robust monitoring and alerting for production flag management.
Risk mitigation tools
Kill switches and gradual rollouts reduce deployment risks compared to Convert's all-or-nothing approach. Teams can quickly revert problematic features without full application deployments.
Cons vs. Convert
Limited experimentation analysis
LaunchDarkly lacks Convert's comprehensive statistical analysis and reporting capabilities. Teams need additional tools for proper experimentation measurement and significance testing.
Marketing team limitations
The platform doesn't provide Convert's marketing-focused features like visual editors or conversion tracking. Marketing teams may find LaunchDarkly's technical interface challenging for campaign optimization.
Higher complexity for simple tests
LaunchDarkly's enterprise focus adds unnecessary complexity for basic A/B testing scenarios. Simple conversion experiments require more setup than Convert's streamlined approach.
Alternative #7: PostHog
Overview
PostHog takes a different approach than traditional experimentation platforms by combining open-source flexibility with comprehensive product analytics. The platform was built for teams who want complete control over their data while running experiments alongside detailed user behavior tracking. Unlike Convert's focused testing approach, PostHog integrates experimentation directly into a broader product intelligence suite.
This all-in-one philosophy means you're not just getting A/B testing capabilities: you're getting the full context of how experiments fit into your product's overall performance. PostHog's open-source foundation allows teams to customize their experimentation workflows in ways that proprietary platforms simply can't match.
Key features
PostHog delivers experimentation capabilities within a comprehensive product analytics framework designed for technical teams.
Experimentation and testing
Feature flags with percentage rollouts and user targeting
A/B testing with statistical significance calculations
Multivariate testing for complex experiment designs
Product analytics integration
Event tracking with custom properties and user identification
Funnel analysis to understand conversion paths
Cohort analysis for user segmentation and retention studies
User behavior insights
Session recordings to watch actual user interactions
Heatmaps showing click patterns and user engagement
User paths to track navigation flows
Technical flexibility
Self-hosted deployment options for complete data control
Cloud hosting available for easier setup and maintenance
Extensive API access for custom integrations and workflows
Pros vs. Convert
Complete data ownership
Self-hosting means your experimentation data never leaves your infrastructure. This approach gives you full control over data privacy, compliance, and security requirements that many enterprises demand.
Unified product intelligence
You can analyze experiment results alongside comprehensive user behavior data in one platform. This integration eliminates the need to correlate results across multiple tools, giving you deeper insights into why experiments succeed or fail.
Open-source customization
The codebase is fully accessible, allowing you to modify experimentation logic to fit specific business needs. Teams can contribute features, fix bugs, or integrate custom statistical methods that proprietary platforms don't support.
Cost-effective scaling
Self-hosting eliminates per-user or per-event pricing that can become expensive at scale. According to PostHog's pricing analysis, their hosted version can be 2-3x more expensive than alternatives, but self-hosting changes this equation significantly.
Cons vs. Convert
Technical complexity
Setting up and maintaining a self-hosted experimentation platform requires significant engineering resources. You'll need dedicated DevOps expertise to handle updates, scaling, and troubleshooting that managed platforms handle automatically.
Limited experimentation focus
PostHog's experimentation features are part of a broader analytics suite rather than a specialized testing platform. Advanced statistical methods and experiment-specific workflows may be less mature compared to dedicated experimentation tools.
Support and documentation gaps
Open-source projects often have less comprehensive documentation and support compared to commercial experimentation platforms. You'll rely more heavily on community forums and internal expertise to resolve issues.
Closing thoughts
Choosing the right experimentation platform depends on your team's specific needs, technical capabilities, and growth trajectory. Convert works well for straightforward marketing optimization, but modern product teams often need more: advanced statistics, flexible deployment options, and transparent pricing that doesn't punish success.
The alternatives we've explored each excel in different areas. Statsig and Optimizely lead in enterprise capabilities; VWO and AB Tasty focus on marketing teams; Kameleoon brings AI-powered personalization; LaunchDarkly dominates feature management; PostHog offers open-source flexibility. Your choice should align with both current requirements and future ambitions.
For teams ready to explore these options, consider starting with free trials or proof-of-concept implementations. Most platforms offer generous trial periods that let you test real experiments with your actual traffic. Pay special attention to how each platform handles your specific use cases - the best experimentation platform is the one your team will actually use.
Additional resources for diving deeper:
Hope you find this useful!