GrowthBook vs Split (by Harness)

Modern product teams choose GrowthBook over Split when they want robust feature flags, advanced experimentation, and powerful product analytics at scale.

Comparison

At a glance: GrowthBook vs Split

Developer-friendly platform for engineering, product, and data teams to launch experiments in hours
Warehouse-native architecture to analyze experiments and product data together
GrowthBook is 1/5th the cost to run experiments
Engineering-heavy experimentation tool, part of the Harness DevOps suite
Lacks visual editor, requires code to run experiments, and support is not included
Cloud analysis by default, warehouse native for Snowflake and Redshift only.

Why choose GrowthBook over Split

Designed for
Primary Use
Statistical Methods
Deployment Options
Pricing & Plans
Setup Time
growthbook logo

Developer-friendly, full-stack
experimentation platform

Designed for
Developers, product teams, analysts
Primary Use
Test any new feature you build in any platform
Statistical Methods
Bayesian, frequentist, sequential (with CUPED, post-stratification)
Deployment Options
Cloud or fully self-hosted
Pricing & Plans
Per-seat pricing with unlimited tests, unlimited traffic
Setup Time
Hours
Split company logo

Engineering-first experimentation platform

Designed for
Engineering-led product teams
Primary Use
Feature flagging and experimentation for engineers
Statistical Methods
Frequentist, sequential, multi-armed bandits
Deployment Options
Cloud
Pricing & Plans
Free tier, paid support, and enterprise plans
Setup Time
Days

Ready to migrate from Split to GrowthBook?

When it makes sense to have experimentation, feature flags, and product analytics in a warehouse-native platform, it’s time to switch to GrowthBook.

How GrowthBook compares to Split?

Customers choose GrowthBook when they want to run experiments across teams with speed, transparency, and data control.

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
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
Run any number of experiments on any amount of traffic
Low-latency SDKs with rapid rule processing
Scales from startups to large enterprise on the same core platform
99.999% uptime for high traffic websites and apps
Strong for server-side execution in engineering-led environments
Experiment velocity depends on engineering bandwidth and release cycles
Testing types: Supports A/B tests, multivariate tests, redirects, visual editor, and holdouts
Full-stack coverage: server-side, client-side, mobile, and edge experiments
Works across apps, APIs, CDNs, and microservices
Flexible targeting and randomization units: user, location, postal code, URL path, etc.
Statistical frameworks: Bayesian, frequentist, sequential (CUPED and post-stratification for variance reduction)
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
Lightest weight SDKs in the industry by design
Zero network calls for low latency and high reliability
Includes Boolean, number, string, and JSON flag types
Controlled rollouts, gradual exposure, and instant kill switches
Add experiment anytime, no re-instrumentation needed
Feature flagging for engineering teams
Code-first workflows
No built-in bandit optimization for shifting traffic automatically
Advanced optimization requires additional tooling or custom work
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, add metrics retroactively
Reproduce and confirm any GrowthBook calculation
Cloud analysis by default, warehouse native for Snowflake and Redshift only
Harder to audit calculations and extend analysis outside the platform
Troubleshooting often requires more vendor involvement
Fully self-hosted, air-gapped option for data residency requirements (HIPAA)
SOC 2 Type II certified, GDPR, CCPA, and COPPA compliant
No end-user PII required. Your data stays in your data warehouse
Open-source code is publicly available for security review on GitHub
No self-hosted or private cloud dedicated deployment option
Privacy adjustments often require additional process
Use natural language and AI inside GrowthBook for hypotheses, descriptions, and SQL queries
MCP server integration to create flags, run experiments, and query results without leaving your editor
A/B test models and prompts against latency, cost, satisfaction, any custom metric in your warehouse
Trusted by 3 of the 5 largest LLM companies in the world
Support for modifying AI parameters
Limited control, uses OpenAI for experimentation data summaries
MCP integration for feature flag data access
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
Total cost and complexity increase as usage grows
Paid support not included in core pricing
Free tier available

“GrowthBook gave us a modern experimentation and release platform that actually fits how Dropbox works. We can run analytics directly on our data lake, roll features out safely in stages, and support teams across different stacks without duplicating data or tooling.”

Alex Kalish
Engineering Manager, Dropbox

“Being able to turn a feature on and off with a flip of a switch 
is fantastic... That’s so much easier than having to do a deploy or a roll-back.”

John Resig
Chief Software Architect, Khan Academy

“People only see the wins, but there’s actually greater value in avoiding losses. We’ve stopped changes that could have cost millions.”

Merritt Aho
Digital Analytics Lead at Breeze Airways

“GrowthBook has changed the way we think about experiments... It allowed us to uplevel our code, speed up decision-making, and focus on what we do best.”

Diego Accame
Director of Engineering, Upstart

“Experimentation showed what customers actually do rather than what we assume they’ll do.”

Marek Maciusowicz
Head of Engineering, Treatwell

“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
Product Manager, Lingokids

"We are always experimenting now. It’s a natural part of product development. This is due to GrowthBook and the ease of usage both in the UX and in the seamless integration with Snowflake/DWH."

Fredrik Jørgensen
Head of Insight, Retail Platform, Oda

"GrowthBook's results speak for themselves. Every time we do a test, we see benefits for our audiences and our partners. These posters are our one shot, and we wouldn't want to fly blind."

Senior Director
Head of Insight, TodayTix

“GrowthBook lets us build experiments exactly how we want. The ability to target based on culture and geography, as granular as needed, is a major win for us.”

Eslam Samy
Data Scientist, Floward

"GrowthBook's results speak for themselves. Every time we do a test, we see benefits for our audiences and our partners. These posters are our one shot, and we wouldn't want to fly blind."

Senior Director
Head of Insight, TodayTix

"We are always experimenting now. It’s a natural part of product development. This is due to GrowthBook and the ease of usage both in the UX and in the seamless integration with Snowflake/DWH."

Fredrik Jørgensen
Head of Insight, Retail Platform, Oda

“GrowthBook gave us a modern experimentation and release platform that actually fits how Dropbox works. We can run analytics directly on our data lake, roll features out safely in stages, and support teams across different stacks without duplicating data or tooling.”

Alex Kalish
Engineering Manager, Dropbox

More comparisons

FAQs

No. GrowthBook supports more experiment types and statistical approaches, including multivariate tests and holdouts, across server-side, client-side, mobile and edge use cases.

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.

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.

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.

Split is no longer a standalone product. Harness acquired Split in 2024 and rebranded it as Harness Feature Management & Experimentation (FME). When people search for Split, they're now looking at a module within the broader Harness DevOps platform. If you're evaluating Split, you're evaluating Harness FME.

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