The best feature flag providers for apps in 2025

What are feature flags?

Feature flags (also known as feature toggles) enable software teams to control the visibility of new features, decouple deployment from release, and facilitate safer, more gradual rollouts.

They typically involve adding a small SDK or code snippet into your application, plus a dashboard or CLI that toggles features on and off at runtime.

Feature flags allow developers to manage when and how new features appear in an application. By toggling features on or off at runtime, teams can experiment safely in production, perform A/B testing, and instantly roll back changes without a full redeploy.

As continuous delivery, agile methodologies, and rapid iteration become the standard, the demand for sophisticated feature flag solutions has skyrocketed.

Beyond enabling smoother releases, feature flags also support progressive delivery strategies, allowing companies to target specific user segments for controlled exposure to new features.

Organizations leveraging feature flags gain increased agility, reduced downtime, and improved user experiences.

LaunchDarkly

LaunchDarkly remains an industry leader, providing a comprehensive platform for managing feature flags at scale. Enterprise teams trust it for its reliability, real-time flag updates, and deep integration with popular frameworks.

LaunchDarkly excels in supporting complex use cases, including feature experimentation, phased rollouts, and personalized user targeting.

Key features

• Real-time flag updates ensure immediate changes without redeployment.
• Granular user targeting is based on custom attributes.
• Comprehensive analytics and experimentation tools to measure feature impact.
• Enterprise-grade security with role-based access control (RBAC) and audit logging.

Implementation example

Install the SDK:

‍npm install launchdarkly-react-client-sdk

‍Initialize the client:

import { asyncWithLDProvider } from 'launchdarkly-react-client-sdk';
(async () => {
  const LDProvider = await asyncWithLDProvider({
    clientSideID: 'client-side-id-123abc',
    context: {
      "kind": "user",
      "key": "user-key-123abc",
      "name": "Sandy Smith",
      "email": "sandy@example.com"
    },
    options: { /* ... */ }
  });
  render(
    <LDProvider>
      <YourApp />
    </LDProvider>,
    document.getElementById('reactDiv'),
  );
})();

Evaluate a feature flag:

import { withLDConsumer } from 'launchdarkly-react-client-sdk';
const Home = ({ flags, ldClient /*, ...otherProps */ }) => {
  // You can call any of the methods from the JavaScript SDK
  // ldClient.identify({...})
  return flags.devTestFlag ? <div>Flag on</div> : <div>Flag off</div>;
};
export default withLDConsumer()(Home);

Common pitfalls

• Network latency: High-traffic environments should leverage LaunchDarkly’s relay proxy to reduce latency.
• Misconfigured user targeting: Ensure attributes used for segmentation are properly set to avoid unexpected rollouts.

Optimizely

Optimizely, originally known for its A/B testing capabilities, has expanded into feature flagging. This allows teams to combine controlled experiments with gradual feature rollouts, making it an excellent choice for teams that prioritize data-driven development.

Key features

• A/B testing and experimentation built directly into the feature flagging system.
• Dynamic rollouts based on user behaviors and segmentation.
• Server-side and client-side SDKs for flexibility in implementation.
• Robust analytics to track feature performance in real-time.

Implementation example

Install the SDK:

npm install @optimizely/optimizely-sdk

Initialize the client:

const optimizelyClient = window.optimizelySdk.createInstance({
  sdkKey: '<YOUR_SDK_KEY>',
  // datafile: window.optimizelyDatafile,
  // etc.
});

optimizelyClient.onReady().then(({ success, reason }) => {
  if (success) {
      // Create the Optimizely user context, make decisions, and more here!
  }
});

Check feature flag status:

optimizelyClient.onReady().then(({ success, reason }) => {
  if (success) {
      // Create the Optimizely user context, make decisions, and more here!
  }
});

Common pitfalls

• Datafile management: Keep the datafile updated to ensure feature configurations remain accurate.
• User context: Define user attributes properly to take full advantage of Optimizely’s targeting capabilities.
• Limited offline support: Optimizely’s evaluation process depends on frequent data file updates, which may cause issues in offline environments.

Unleash

Unleash is a leading open-source feature flag solution tailored for teams that require full control over their flagging infrastructure. It is highly customizable and is the go-to choice for organizations with strict compliance requirements or security concerns.

Key features

• Self-hosted option for maximum control over data.
• Flexible activation strategies for various rollout conditions.
• Multi-environment support for managing different feature states across staging, QA, and production.
• Lightweight SDKs available for multiple languages.

Implementation example

Install the SDK:npm install unleash-client

‍Initialize the client:

import { startUnleash } from 'unleash-client';

const unleash = await startUnleash({
  url: 'https://YOUR-API-URL',
  appName: 'my-node-name',
  customHeaders: { Authorization: '<YOUR_API_TOKEN>' },
});

Check feature flag status:

// The Unleash SDK now has a fresh state from the Unleash API
const isEnabled = unleash.isEnabled('Demo');

Common pitfalls

• Self-Hosting complexity: While powerful, maintaining a self-hosted Unleash instance requires infrastructure and monitoring.
• Consistent user identifiers: Use stable user identifiers for accurate targeting to avoid inconsistent feature experiences.

Bucket

Bucket is a feature flag provider for B2B SaaS companies. It's built to work at the company/organization level right out of the box, is quickly set up, and gives you feature adoption metrics and customer feedback in a beautifully crafted UI.

Key features

• ‍Feature flags for B2B: company-level targeting rules, user aggregation, filtering, segments, and reporting‍
• Feature entitlements: grant companies access by flipping a switch ‍
• Feature remote configs: manage feature configuration in one place‍
• Feature adoption metrics: using a proven and B2B-optimized framework‍
• Feature feedback: using surveys or feedback buttons‍
• Feature data export: sync with your CRM and/or data warehouse

Implementation example

Install the SDK:

npm i @bucketco/react-sdk

Initialize the client:

import { useFeature } from "@bucketco/react-sdk";

const App = () => (
  <BucketProvider
    publishableKey="pub_prod_GgaS4JqPUQMy4OzzP5eCbZ"
    user={{ id: "john_doe", name: "John Doe" }}
    company={{ id: "acme_inc", name: "Acme Inc." }}
  >
    <MyFeature />
  </BucketProvider>
);

Check feature flag status:

const MyFeature = () => {
  const { isEnabled } = useFeature("my-new-feature");

  return isEnabled ? "You have access!" : null;
};

Common pitfalls

• ‍Focused on B2B: Bucket doesn't handle complex A/B testing scenarios across multiple B2C and ecommerce applications.‍
• Programming language support: Bucket currently supports React, Node.js, Next.js and OpenFeature. Although it provides a Browser SDK and HTTP API, it could benefit from support for more languages and frameworks like Vercel, Laravel and PHP.

Split.io

Split.io is a feature delivery platform that combines feature flag management with robust experimentation capabilities.

It enables engineering and product teams to release features confidently, perform controlled rollouts, and assess the impact of new functionalities in real time.

With its data-driven approach, Split.io is ideal for organizations aiming to integrate feature management with comprehensive performance monitoring and experimentation.

Key features

• Feature flags: Decouple deployment from release, allowing for controlled rollouts and the ability to toggle features on or off without redeploying code.
• Targeting rules: Define precise audience segments based on user attributes, enabling personalized experiences and gradual feature exposure.
• Dynamic configuration: This allows you to modify application behavior and parameters in real-time without code changes, facilitating rapid iterations and A/B testing.
• Automated rollout monitoring: Continuously monitor feature performance and receive alerts on anomalies to ensure stable and reliable releases.
• Feature experimentation: Conduct rigorous A/B tests and experiments to measure features' impact on user engagement and other key metrics.

Implementation example

Install the SDK:npm install @splitsoftware/splitio

Initialize the client:

// Import the SDK
var SplitFactory = require('@splitsoftware/splitio').SplitFactory;

// Instantiate the SDK
var factory = SplitFactory({
  core: {
    authorizationKey: 'YOUR_SDK_KEY'
  }
});

// Get the client instance you'll use
var client = factory.client();

Evaluate a feature flag:

// Set a callback to listen for the SDK_READY event, to make sure the SDK is properly loaded before asking for a treatment
client.on(client.Event.SDK_READY, function() {
  var treatment = client.getTreatment('CUSTOMER_ID', 'FEATURE_FLAG_NAME');
  if (treatment == 'on') {
    // insert code here for on treatment
  } else if (treatment == 'off') {
    // insert code here for off treatment
  } else {
    // insert your control treatment code here
  }
});

Common pitfalls

• Data privacy: When targeting based on user attributes, handle sensitive data cautiously to comply with privacy regulations.
• Flag management: Regularly audit and clean up outdated or unused feature flags to maintain codebase clarity and reduce technical debt.

Eppo

Eppo is a next-generation experimentation platform that seamlessly integrates feature flagging with advanced A/B testing capabilities. Designed for modern data teams, Eppo enables organizations to deploy features confidently, conduct rigorous experiments, and personalize user experiences, all while leveraging a warehouse-native architecture for trustworthy data analysis.Key features

• High-Performance feature flags: Manage feature toggles with minimal latency, supporting use cases from simple feature gates to complex experiments.
• Integrated experimentation: Conduct A/B/n tests and multivariate experiments with robust statistical analysis to measure feature impact accurately.
• Dynamic configuration: Adjust application behavior in real-time without code deployments, enabling rapid iteration and optimization.
• Comprehensive SDK support: Access a wide range of client-side and server-side SDKs, including JavaScript, Python, Java, Go, and more, ensuring seamless integration across diverse tech stacks.

Implementation example

Install the SDK:

‍npm install @eppo/js-client-sdk

Initialize the client:

import { init } from "@eppo/js-client-sdk";

await init({
  apiKey: 'YOUR_SDK_KEY',
  assignmentLogger: { logAssignment: (assignmentEvent) => console.log('Send to warehouse: ', assignmentEvent) },
});

Create an assignment:

import * as EppoSdk from "@eppo/js-client-sdk";

// Get SDK instance
const eppoClient = EppoSdk.getInstance();

// Define assignment parameters
const featureKey = "my-neat-feature";
const subjectKey = getCurrentUser() || "anonymous";
const subjectAttributes = {
    'country': user.country,
    'device': user.device,
};
const defaultValue = "default-value";

// Make an assignment
const variation = eppoClient.getStringAssignment(
  featureKey,
  subjectKey,
  subjectAttributes,
  defaultValue
);

Use an assignment:

// Example using a boolean assignment
const variation = eppoClient.getStringAssignment(
  featureKey,
  subjectKey,
  subjectAttributes,
  defaultValue
);

// Render different components based on assignment
if (variation === "landing-page-a") {
    return <LandingPageA />
} else if (variation === "landing-page-b") {
    return <LandingPageB />
} else {
    return <LandingPageC />
}

Common pitfalls

• Attribute consistency: When using targeting rules based on user attributes, consistently pass the correct attributes during flag evaluation to achieve the desired segmentation.
• Flag lifecycle management: Regularly review and clean up obsolete feature flags to maintain a clear and maintainable codebase.