dif.sh Review: Feature Flags, A/B Testing and Experiment Management in Git

Modern software teams constantly test new ideas. A company might want to know whether a different checkout button increases conversions, whether a new pricing headline performs better, or whether a particular feature should be released to only a percentage of users.

dif.sh takes a different approach to feature flags and experimentation by putting experiments directly inside the Git repository alongside the application code.

What Is dif.sh?

dif.sh is a developer tool for managing feature flags and experiments directly in a code repository.

It supports feature flags, A/B tests, holdouts, staged rollouts, audience targeting, analytics, conflict detection and experiment history.

Why Would Developers Need dif.sh?

Instead of managing an experiment completely outside the development workflow, dif.sh allows developers to keep the experiment definition close to the application code.

The dif/ Folder

A project can contain a dedicated dif/ directory containing experiments, surface information, configuration and generated files.

Feature Flags and A/B Tests

dif.sh uses a consistent file-based approach for feature flags, A/B tests, holdouts and staged rollouts.

Audience Targeting

Developers can define audiences using attributes such as country, plan and returning visitor.

Experiment Variants

An experiment can contain a control version and one or more variants. This makes the experiment definition easy to understand directly from the project files.

Metrics and Guardrails

Experiments can define primary metrics and guardrail metrics so teams can measure improvements while also monitoring possible negative effects.

Exclusion Groups

Exclusion groups help prevent conflicting experiments from running against the same users at the same time.

dif CLI

dif.sh provides a command-line workflow with commands such as dif init, dif new, dif validate, dif qa, dif build and dif conclude.

How the Workflow Works

1. Initialize the project.
2. Create an experiment.
3. Validate the configuration.
4. Run QA and preview variants.
5. Build the client.
6. Run and measure the experiment.
7. Conclude the experiment and preserve its learning.

Working With Coding Agents

dif.sh generates a dif/context.json file containing active experiments, variants and recent learnings. Coding agents can read this context when working on the repository.

Experiment Knowledge Becomes Institutional Memory

Completed experiments can be archived and their learnings can be added to surface logs. This means future experiments can start with knowledge from previous tests.

Git Becomes the Audit Trail

Because experiment definitions live in the repository, Git can provide version history and pull requests can be used as part of the review process.

Analytics With dif.track()

dif.sh provides dif.track() for sending experiment results. Dif Cloud can calculate lift, while custom event workflows can connect results to analytics systems.

Dif Cloud

Dif Cloud is an optional hosted layer that provides a broader view of experiments across repositories, including lift measurements and confidence intervals.

Confidence Intervals

Dif Cloud provides experiment results with confidence intervals so teams can evaluate the uncertainty around observed differences.

Supported Development Environments

The dif.sh website provides examples for Web, Server, React and Svelte applications.

Simple Example

Imagine an online store testing two checkout buttons: Buy Now and Get It Today. The developer can create an experiment, define its variants and connect the experiment to the application.

Main Advantages of dif.sh

• Git-based workflow
• Simple file structure
• Developer-friendly CLI
• Experiment history
• Conflict detection
• Audience targeting
• Coding-agent context
• Optional cloud dashboard

Things Developers Should Consider

dif.sh is focused on developers and teams that are comfortable working with Git and command-line tools. Teams should consider their existing development workflow, analytics setup, repository structure and need for a hosted dashboard.

Who Is dif.sh Best For?

• Software developers
• Product teams
• Startups
• Growth teams
• AI-assisted development teams

dif.sh vs Traditional Feature Flag Platforms

Traditional feature flag systems often rely heavily on external dashboards. dif.sh takes a Git-native approach where experiment definitions remain inside the repository.

Final Verdict

dif.sh is an interesting developer-focused approach to feature flags and experimentation.

Its combination of feature flags, A/B tests, holdouts, staged rollouts, audience targeting, conflict detection, experiment history, CLI commands and coding-agent context creates a workflow designed around developers and version control.

For teams that already work heavily with Git, dif.sh is worth exploring as an alternative approach to managing software experimentation.

Frequently Asked Questions

What is dif.sh?

dif.sh is a developer tool for managing feature flags and experiments directly in a Git repository.

What can dif.sh manage?

It can manage feature flags, A/B tests, holdouts and staged rollouts.

Does dif.sh use Git?

Yes. Its core approach stores experiment definitions in the repository.

Does dif.sh have a CLI?

Yes. It provides commands including dif init, dif new, dif validate, dif qa, dif build and dif conclude.

Can dif.sh target specific audiences?

Yes. Developers can target audiences using attributes such as country, plan and returning visitor.

What is Dif Cloud?

Dif Cloud is the optional hosted layer for viewing experiment results, lift measurements and confidence intervals.