Feature flags and toggles sometimes get used interchangeably, but they don't exactly mean the same thing. Yes, they solve similar problems (and often look identical in code), but they've evolved into meaningfully different approaches to controlling software behavior.

Understanding these differences is more than just semantics. It affects which tools you choose, how you architect your release process, and whether you're prepared for modern development practices like progressive delivery, continuous integration, and AI experimentation.

Below, we'll walk you through everything you need to know about feature flags vs. feature toggles, including where they overlap, how they've diverged, and why this matters for DevOps teams shipping everything from traditional features to AI-powered capabilities.

What is a feature toggle?

A feature toggle is the broader practice of controlling feature visibility and availability at runtime. It’s an approach that lets you turn parts of your software on and off without redeploying code.

Toggles originated to solve a fundamental problem: how do you ship code without immediately exposing every change to users? The answer was conditional logic that checks some external state before executing features.

Feature toggles work in three main scenarios:

Internal testing: Enable a beta feature for your development team, testers, or a specific group of users while keeping it hidden from production users.
Canary releases: Roll out changes to 5% of traffic to monitor performance and user feedback before full deployment.
Kill switches: Disable problematic new features instantly when issues arise in production.

Toggles enable the philosophy of decoupling deployment from release. With them, you can ship code today and decide tomorrow who sees it and when.

What is a feature flag?

A feature flag is the implementation mechanism. It’s the actual code-level construct that makes feature toggles possible. Flags are boolean or multivariate values that your application checks to determine behavior.

Here's what a basic flag looks like in practice with Javascript:

if (featureFlag.isEnabled('new-search-algorithm')) {
  return enhancedSearch(query);
} else {
  return legacySearch(query);
}

Flags can also return multiple values for A/B testing:

search_variant = feature_flag.get_value('search-test', default='control')
if search_variant == 'ml-powered':
    return mlSearch(query)
elif search_variant == 'elasticsearch':
    return elasticSearch(query)
else:
    return legacySearch(query)

Toggles describe the practice, but flags are the technical implementation that makes sophisticated release control possible.

How are feature flags different from feature toggles?

Feature flags are built upon the toggle foundation, but they add sophisticated functionality like targeting, experimentation, and governance that simple toggles cannot provide.

At LaunchDarkly, we see toggles as a subset of the broader feature flag ecosystem. A toggle implies binary states: on or off. Feature flag management includes choosing how your codebase behaves in real time, providing granular control over experimentation and rollouts.

When Martin Fowler published his influential work on feature branching in 2020, he used the term "feature flag" rather than "feature toggle" to describe hiding partially built features. This shift highlights the evolution from simple feature switches to advanced release mechanisms that could selectively reveal features to a subset of users.

The timeline shows this progression:

Basic toggles (2010s): Simple on/off switches in configuration files or environment variables. Teams could hide incomplete features or create basic kill switches.
Platform flags (2015+): Centralized systems with targeting rules, percentage rollouts, and user interfaces. Teams gained operational control through continuous deployment without code deploys.
AI-powered flags (2020s+): Intelligent experimentation platforms with advanced analytics, automated decision-making, and governance workflows for complex AI feature rollouts.

Modern feature flags enable progressive delivery, A/B testing, and operational backend control that simple toggles can’t. They've become the mature, enterprise-ready version of the original toggle concept—supporting everything from basic feature hiding to advanced AI model experiments across global user bases.

Feature toggles vs. feature flags (comparison table)

Aspect	Feature toggles	Feature flags
Complexity	Manual configuration files, environment variables, and simple database entries	Advanced workflows, dashboards, governance policies, automation, and decision-making
Control granularity	Binary on/off states	Percentage rollouts (5%, 25%, 50%), user cohort targeting, multivariate testing (A/B/C/D variants)
User interface	Code-based: JSON configs, .env files, database queries	GUI dashboards, REST APIs, Slack integrations, mobile apps for non-technical users
Lifespan	Temporary: cleaned up after feature release or rollback	Persistent: long-term experimentation, governance controls, operational safety nets
Use cases	Internal prototypes, basic kill switches, and simple feature hiding	Customer-facing rollouts, continuous delivery, revenue optimization, and compliance controls
Targeting capabilities	All users or none	Geographic regions, user attributes, device types, beta groups, and premium tiers
Analytics & monitoring	Basic logging, manual analysis	Real-time metrics, conversion tracking, statistical significance, automated alerts
Team collaboration	Developer-only changes	Product managers, marketers, and executives can control releases without engineering
Platform requirements	Minimal: any application framework	Enterprise-grade: high availability, global CDN, audit trails, RBAC
Team size suitability	Small teams (1-10 developers)	Growing to enterprise teams (10+ developers, multiple departments)
AI capabilities	Not applicable	Model A/B testing, prompt experimentation, AI safety controls, automated rollbacks
Rollback mechanism	Code deployment or manual config change	Instant toggle in production, no deployment required

Feature toggles and AI experimentation

Feature toggles and flags are non-negotiable for safely rolling out AI-powered features. AI features introduce non-deterministic behavior that you’ll need to carefully monitor (and sometimes rollback).

Here's how teams use feature controls for AI rollouts:

Testing ML models in production: Deploy a new recommendation algorithm to 5% of users while the existing model serves the remaining 95%. Monitor conversion rates, latency, and user satisfaction before expanding.
A/B testing generative AI outputs: Compare GPT-5 against Claude or Llama for customer support responses. Route different user segments to different models while measuring resolution rates and customer satisfaction.
AI kill switches: Instantly disable an AI feature if it produces biased outputs, generates unsafe content, or exceeds cost thresholds. No code deployment required. Just flip the switch.

LaunchDarkly provides experimentation infrastructure that’s specifically designed for AI teams. You can ship models faster while reducing risk through progressive delivery—release models incrementally, monitor outcomes in real time, then expand safely based on data.

The platform includes AI governance features accessible through APIs and SDKs. These include role-based access controls for sensitive model experiments, comprehensive audit trails for compliance requirements, and lifecycle management for AI feature flags across software development, staging, and production environments.

LaunchDarkly is uniquely positioned to help companies de-risk AI rollouts by combining experimentation, safety controls, and governance in one platform. Basic toggles might work for simple feature hiding, but AI experimentation needs the targeting, real time monitoring, and instant rollback features that only advanced feature flags provide.

When to use feature toggles vs. feature features

Feature toggles: when simple works

Feature toggles are the best starting point when you need basic on/off control. It doesn’t provide complex targeting or experimentation requirements, though. Just simple and basic.

Ideal use cases include small teams (under 10 developers), temporary feature hiding during the development process, internal tools with limited user bases, and proof-of-concepts where you're validating ideas quickly.

Implementation tends to involve configuration files, environment variables, or simple database flags:

{
  "newDashboard": true,
  "betaFeatures": false,
  "maintenanceMode": false
}

You can implement basic toggles in an afternoon without external dependencies or complex infrastructure. Plus, it delivers low overhead, quick setup, minimal learning curve, and low cost.

There are limitations, though, especially as you scale. These include manual management across environments, no user targeting capabilities, basic reporting, difficulty coordinating new code changes across team members, and potential technical debt from unused toggles.

Most teams eventually need flag-level sophistication as they grow beyond single-feature releases and homogeneous user bases.

Here’s when you know it’s time to upgrade to feature flags:

Releasing more than weekly
Teams exceeding 10 developers
Serving diverse user segments
Needing to coordinate releases across multiple services

Feature flags: when you need more power

Feature flags are the next-step approach for serious feature management.

Some specific features include:

Percentage rollouts
User targeting based on attributes
A/B testing with statistical analysis
Real-time control without code deployments

Enterprise requirements often demand governance workflows, comprehensive audit trails for compliance, and multi-environment support. Regulated industries need to track exactly who changed what, when, and why (capabilities that basic release toggles can't provide).

Beyond the features, your business gets faster iteration cycles, reduced deployment risk, data-driven decisions backed by real user behavior, and operational control that lets product teams move independently of engineering schedules.

Teams with 10+ developers, daily deployments, multiple environments, or regulatory requirements need the robustness that only dedicated feature flag services provide. This includes high availability, global distribution, extensive integrations with analytics tools, and AI capabilities for intelligent model rollouts.

Flags represent where feature management is heading, and LaunchDarkly leads that evolution with capabilities ranging from basic Boolean flags to sophisticated AI experimentation infrastructure.

The hybrid reality: most teams need both

There’s usually not a one-size-fits-all solution here. Many mature organizations use both approaches for different contexts. Simple toggles work perfectly for basic internal tools, but sophisticated flags handle customer-facing features that require careful rollouts and experimentation.

Think about context-driven decisions: use environment variables for developer productivity tools, but leverage advanced targeting for revenue-generating features where user experience directly impacts business metrics.

Unified platforms like LaunchDarkly support both simple and complex real-world use cases, so you're not forced to choose between approaches or manage multiple systems.

Migration guidance typically follows this pattern: teams start with toggle thinking for immediate needs, then adopt flag sophistication as requirements grow. The transition feels natural when your platform supports both paradigms.

And this is where terminology flexibility really becomes apparent, because what you call them matters less than having the right capabilities available when you need them. Some teams call everything "flags," others prefer "toggles" for simple cases, and both approaches work fine.

LaunchDarkly's advantage is providing one platform that grows with your needs, from simple toggles for internal tools to AI-powered flags for cutting-edge customer experiences.

Why LaunchDarkly makes the toggle vs flag debate irrelevant

Yes, the terminology matters, but when you have an all-in-one platform that provides both feature toggles and feature flags, it’s more about accessing the right capabilities for the right situation.

LaunchDarkly embodies the feature flag philosophy. We provide advanced targeting that lets you release features to specific user segments, enterprise governance with audit trails and role-based access, and AI-powered capabilities for experimenting with machine learning models in production.

These aren't just incremental improvements over basic toggles, either. They're fundamentally different approaches to controlling software behavior.

Basic toggles might work for simple scenarios, but growing teams inevitably need the advanced capabilities that define true feature flags. The question isn't whether you'll eventually need sophisticated feature management—it's whether you'll build those capabilities in-house or leverage a platform designed specifically for this purpose.

LaunchDarkly makes the toggle vs flag debate irrelevant by supporting both simple and complex use cases in one platform. Start with basic feature control and evolve toward advanced experimentation without switching tools or rebuilding infrastructure.

Start your free LaunchDarkly trial and see the difference that true feature flags make for your development workflow.

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Jesse Sumrak

LaunchDarkly

Feature Toggle vs Feature Flag: Is There a Difference?

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