Product teams choose GrowthBook over Optimizely when they want to reduce complexity and cost with a warehouse-native experimentation, feature flags, and product analytics platform.
Modern product teams choose GrowthBook over Split when they want robust feature flags, advanced experimentation, and powerful product analytics at scale.
- Developer-friendly platform, integrates into your stack, to launch experiments in hours.
- Warehouse-native architecture to analyze experiment and product data together.
- GrowthBook is 1/5th the cost to run experiments.
- Complex, engineering-focused tool for server-side experimentation. Recently acquired.
- Proprietary platform requires data to flow through Split's infrastructure for analysis.
- Free tier available, but experimentation speed is limited by engineering resources.
GrowthBook’s warehouse-native architecture lowers the cost and complexity of experimentation while giving product teams smarter feature flags and a single source of truth for all their product and experiment data.
Developer-friendly, full-stack experimentation platform
Engineering-first experimentation platform
Designed For
Developers, product teams, analysts
Engineering-led product teams
Primary Use
Test any new feature you build in any platform
Server-side experimentation and feature flagging
Statistical Methods
Bayesian, frequentist, sequential (with CUPED, post-stratification)
Frequentist, sequential, multi-armed bandits
Deployment Options
Cloud or fully self-hosted
Cloud
Pricing & Plans
Per-seat pricing with unlimited tests, unlimited traffic
Free tier, paid support, and enterprise plans
Setup Time
Hours
Days
When it makes sense to have experimentation, feature flags, and product analytics in a warehouse-native platform, it’s time to switch to GrowthBook.
Customers choose GrowthBook when they want to run experiments across teams with speed, transparency, and data control.
Developer Experience
GrowthBook lets teams launch faster and QA without waiting on engineering for every change.
GrowthBook
- Developer-friendly, intuitive environment for fast iteration
- Chrome debugger + visual editor
- Clear documentation, AI chatbot access, and responsive support
- Flexible for both technical and non-technical stakeholders
- 24+ SDKs: JavaScript, React, Node.js, Python, Ruby, Go, PHP, Java, Swift, Kotlin, etc.
Split (now Harness)
- Built for engineers with code workflows
- No visual editor
- Client-side tests require code changes for setup and updates
- CMS and content workflows require custom engineering
- QA and forced-variant workflows rely on manual targeting and whitelisting
Performance & Scale
GrowthBook is designed for high-velocity experimentation on full traffic, with low-latency decisioning that scales without complexity.
GrowthBook
- Run any number of experiments on any amount of traffic
- Low-latency SDKs with rapid rule processing
- Scales from small teams to large enterprises on the same core platform
- 99.999% uptime for high traffic websites and apps
Split (now Harness)
- Strong for server-side execution in engineering-led environments
- Experiment velocity depends on engineering bandwidth and release cycles
Experimentation Depth & Flexibility
GrowthBook supports more experiment types and execution models, so teams can answer more complex product questions without switching tools.
GrowthBook
- Testing types: Supports A/B tests, multivariate tests, redirects, visual editor, and holdouts
- One platform for server-side, client-side, mobile, and edge experiments
- Full-stack coverage: server-side, client-side, mobile, and edge experiments
- Flexible targeting and randomization units user, location, postal code, URL path, etc.
- Statistical frameworks: Bayesian, frequentist, sequential (with CUPED, post-stratification)
Split (now Harness)
- Strong for server-side A/B testing
- No full multivariate testing support beyond basic A/B/n
- Analysis is proprietary and managed on Split infrastructure
- Advanced experimentation programs often require more complexity
Feature Flagging & Flag-Based Experiments
GrowthBook unifies flags and experiments so teams can ship safer and smarter, without stitching systems together.
GrowthBook
- World-class feature flags at the core
- Controlled rollouts, gradual exposure, and instant kill switches
- Zero network call SDKs for low latency and high reliability
- Unified system for flags and experiments
Split (now Harness)
- Feature flagging and server-side decisioning for engineering teams
- Code-first workflows
- No built-in bandit optimization for shifting traffic automatically
- Advanced optimization requires additional tooling or custom work
Transparency & Warehouse-Native Architecture
GrowthBook keeps analysis in your warehouse so results are easier to trust, audit, and share with customizable dashboards and reports.
GrowthBook
- Bring your data architecture Snowflake, BigQuery, Redshift, Postgres, etc.
- Analyze all your product and experimentation data in one place
- Customize metrics using SQL, use metric libraries, and even add metrics retroactively
- Reproduce and confirm any GrowthBook calculation
Split (now Harness)
- Platform-managed analysis and reporting
- Not warehouse-native
- Harder to audit calculations and extend analysis outside the platform
- Troubleshooting often requires more vendor involvement
Privacy, Security & Deployment
GrowthBook works well for any industry with strict privacy and compliance requirements, including AI, financial services, healthcare, education, and gaming.
GrowthBook
- Fully self-hosted, air-gapped option for data residency requirements
- Meets strict privacy compliance requirements GDPR, HIPAA, CCPA, SOC 2 Type II certified
- No end-user PII required. Your data stays in your data warehouse
- Open-source code is publicly available for security review on GitHub
Split (now Harness)
- No self-hosted or private cloud dedicated deployment option
- Privacy adjustments often require additional process
AI-Ready & Future-Proof
GrowthBook gives teams building AI products the control they need to test and optimize features for better engagement and outcomes.
GrowthBook
- 3 of the 5 leading AI companies use GrowthBook to optimize their chatbots and APIs
- Design highly customized metrics to measure engagement, usage and outcomes
- Tune AI responses per use case with very targeted experiments
- MCP integration for natural language access to GrowthBook directly in IDE Claude Code, Cursor, VS Code
Split (now Harness)
- Support for modifying AI parameters
- Limited control, uses OpenAI for experimentation data summaries
- MCP integration for feature flag data access
Cost of Ownership
GrowthBook scales predictably with per-seat pricing and unlimited traffic, while Split costs rise with paid support and complexity.
GrowthBook
- Predictable per-seat pricing with unlimited experiments and unlimited traffic
- Free tier and open source options
- Enterprise self-hosting gives customers flexibility and control
- Warehouse-native architecture means you do not pay twice to capture the same data
Split (now Harness)
- Total cost and complexity increase as usage grows
- Paid support not included in core pricing
- Free tier available
What do GrowthBook users say?
“We don’t need any code changes, we don’t need an app release. We just configure the new tests and launch right away.”
Filipa Batista, Lingokids
"With GrowthBook, you can toggle experiments on and off without reloading the page. It's a lot faster for front-end developers."
Alex Kalish, Engineering Manager, Dropbox
“Talk about bang-for-buck.”
FAQs
Can Split do everything GrowthBook does statistically?
No. GrowthBook supports more experiment types and statistical approaches, including multivariate tests and holdouts, across server-side, client-side, mobile and edge use cases.
How easy is it to move from Split to GrowthBook?
Moving is straightforward. GrowthBook supports the same server-side experimentation patterns you're already using with Split. When you migrate to GrowthBook, you add client-side, mobile, and edge testing options. Warehouse-native analysis can be used on existing experiments as you migrate to GrowtBook.
Why do companies choose GrowthBook over Split for experimentation and A/B testing?
Companies choose GrowthBook over Split to run experiments faster across teams, keep experiment data in their warehouse, and support self-hosted deployment for privacy and compliance needs.
What is the main difference between GrowthBook and Split?
GrowthBook analyzes experiments directly in your data warehouse and works for product teams across the organization with no-code options. Split is an engineering-first platform focused on server-side experimentation with code-driven workflows.
