Releasing new features creates stress for application development teams. Even with thorough testing and careful planning, an application release to production can yield unexpected results, could unintentionally expose features intended only for certain users, or might degrade overall application performance. Feature flags are a strategy used by software engineering and DevOps teams to enable or disable functionality at runtime. This article explores feature flags as a development approach, highlighting their benefits, their importance in deployments, and the challenges they pose for development and other teams.
What are feature flags?
Feature flags are a software development technique that enables teams to toggle functionality on or off at runtime without redeploying code. By separating deployment from release, feature flags enable engineers to target new features to specific user groups rather than the entire user base. Targeted releases reduce risks and enable observation of real-world effects in production. By implementing feature flags, releases to production can become more controlled, reversible, and transparent, transforming a single risky event into a manageable process.
Targeted releases are also called “canary releases.” Canary releases are a deployment method in which a new software version is gradually introduced to a small group of users, called the “canaries,” before a complete rollout. This serves as a live test to identify bugs and performance problems early. Canary releases reduce risk for all users. The name comes from the practice of using canaries in coal mines to warn miners of dangerous gases. This approach enables teams to observe real-world performance, collect feedback, and roll back updates if issues arise, ensuring stability and supporting gradual enhancements.
Why are feature flags important?
Minor discrepancies between development, quality assurance (QA), staging, and production environments can lead to unforeseen issues during the deployment of new features to a user base. Feature flags help engineering teams reduce risk and increase delivery velocity by giving teams precise control over how changes are exposed in production.
Teams can use feature flags to release incremental updates to a specific feature rather than releasing it in full. Limiting the release to a specific feature and a narrow set of users enables end-to-end and A/B testing in a production environment without disrupting the main user base.
Integrating feature flags with observability metrics enables engineering teams to safely run tests in production, reduce mean time to repair (MTTR), and evaluate the business impact of new releases. By incorporating feature flag data into telemetry such as metrics, logs, and traces, teams can immediately observe how a particular code variant influences system performance.
Feature flags can accelerate operational release cycles by enabling rapid feature deployment, testing, and rollback. This approach encourages teams to quickly implement new ideas without the complications of complex code integrations or “heavy” deployments. When several teams work on different parts of complex applications, feature flags help by minimizing dependencies and conflicts.
The benefits of using feature flags with a production release process include:
Controlled releases to specific cohorts: Teams can restrict features to specific groups such as regions, device types, customer tiers, or internal users. This restriction allows teams to review, test, and confirm behavior before wider deployment.
Reduced danger zone: Limiting exposure to a subset of traffic minimizes the impact of bugs or performance regressions on the entire user base.
Decoupled deployment and release: Engineers can continuously deploy code while controlling when and where features are activated.
Faster incident response: Features can be disabled instantly for affected cohorts without rolling back entire deployments.
Improved confidence and release velocity: By releasing changes incrementally and observing outcomes, teams gain confidence to ship more frequently.
How do feature flags work within the application development lifecycle?
Feature flags operate through conditional logic embedded in application code that is evaluated at runtime. The application checks a configuration file or setting to determine whether a flag is enabled for a user or segment. If the flag is enabled, the new code runs. If not, the old code or a fallback path is used. Rather than relying solely on deployments to enable features, engineers can use feature flags to specify which users, requests, or services are affected by a change.
The key architectural components and processes for feature flags within the development lifecycle include:
Feature flag configuration: Feature flags are managed in a control plane, where teams define states such as enabled, disabled, or multiple variants. These configurations are version-controlled and can be updated separately from application deployments.
Application SDK and local evaluation: Applications include a feature-flag SDK that evaluates flags directly on the device at runtime using a standardized method. This ensures fast decision-making and reliable performance, even if the control plane is temporarily unavailable. For an introduction to this feature, see “Getting Started with Feature Flags” in Datadog Docs.
Context and cohort definition: Each flag evaluation considers contextual attributes like user ID, account tier, region, device type, environment, and request metadata. These details enable teams to create cohorts and accurately target specific users or systems for a feature.
Targeting rules and traffic allocation: Targeting logic determines how flags are assigned to cohorts, whether via percentage rollouts, rule-based targeting, or environment-specific behaviors. This allows teams to introduce changes gradually and assess their impact before wider deployment.
Progressive delivery workflows: Teams typically implement staged rollouts that gradually increase exposure, starting with small groups and expanding to larger audiences. Each phase allows for evaluation of system performance and user experience before advancing.
Metrics and monitoring signals: Engineers monitor both technical and business metrics, such as error rates, latency, throughput, and conversion rates, to assess how a feature impacts different user groups. These indicators guide decisions on whether to proceed, pause, or revert a release.
Feature lifecycle management: Once a feature is fully deployed or no longer necessary, its associated flag and conditional logic should be removed. Regular cleanup helps avoid the buildup of outdated flags, which can complicate the system over time.
What use cases apply regarding feature flags in application development?
Releases using feature flags play a crucial role in application development for software engineers, platform teams, and site reliability engineers (SREs) managing modern, always-on systems. Feature flags are valuable in environments where teams must balance delivery speed with system reliability and user experience.
Some common use case scenarios for integrating feature flags include:
Progressive and canary releases: Gradually introduce new features to small traffic segments or isolated services to ensure stability before a complete rollout. Examples include percentage-based traffic deployment, which routes 1%, 5%, 25%, and then 100% of live traffic to the new version, monitoring metrics at each step, and user segment deployment, which begins by deploying the release to internal developers, moves to beta testers, then to a small percentage of real users, and then to the entire user base.
Cohort-based feature exposure: Target features to specific user groups, such as regions, device types, customer tiers, or internal testers, to manage risk and verify functionality behaviors. Examples include using an acquisition cohort, which includes a new onboarding flow for users who signed up during two consecutive months to determine if the new flow improves retention metrics, and A/B feature testing, which might include displaying a button in one specific location for one cohort (group A) and in another location for a second cohort (group B).
Incident mitigation and kill switches: Quickly disable or revert problematic features for affected groups without needing redeployment. Examples include traffic mirroring/shadowing, which duplicates production traffic to a test environment to validate changes without impacting live users before a migration, and ransomware kill switches, which lock down protocols and quarantine endpoints to stop ransomware spread during an attack.
Experimentation and validation: Test feature variants with specific groups to assess performance, reliability, and user impact before a wider rollout. Examples include algorithm optimization, which tests a new search algorithm by directing a small percentage of user searches (for example, 5%) to the new algorithm and then comparing metrics to the previous search algorithm—such as click-through rates and purchase conversions—and early adopter programs, which provide advanced features to “VIP” or “early adopter” users first for experience testing before deploying to the general user base.
What industry shifts affect implementing feature flags in application development?
Industry changes in software development have shifted the implementation of feature flags from “if/else” toggles to advanced, AI-powered platforms essential to business, enabling progressive delivery and dynamic testing. As demand for faster, safer, and more tailored software grows, these tools are evolving from engineering-only utilities into centralized systems used by marketing, product, and data teams.
Additional examples of industry changes that affect feature flags include:
The increasing complexity of modern software systems, driven by trends such as microservices, distributed architectures, and shared platforms, complicates releases and increases the risk that minor changes might cause widespread issues.
AI-powered development speeds up change volume by generating more code and configuration updates. This raises the risk of unintended behaviors, underscoring the need for strategies that address potential issues and enable quick rollbacks in production environments.
What are the challenges associated with feature flags?
Feature flags can cause issues such as code bloat, unmanaged flags that add to technical debt, increased testing complexity, performance overhead, potential system instability, and difficulty tracking complex flag interactions. Managing these challenges requires disciplined lifecycle oversight, clear ownership, and automation to prevent the system from becoming overwhelmed with conditional logic.
Other common challenges associated with implementing feature flags include:
Feature flag sprawl and technical debt arise when the number of feature flags increases for releases and experiments. Over time, unused or permanent flags can accumulate, increasing code complexity and maintenance risks.
Inconsistent use of feature flags by teams can complicate governance, safety assurance, and comprehension of behavior across different services and environments.
Adding feature flags to runtime dependencies introduces additional logic that executes on each request. If flag evaluation depends on a remote service without safeguards, network delays or outages can directly impact the application’s availability.
What features should users look for when considering feature flags in application development?
When assessing a feature flag solution, teams should focus on a solution’s capabilities that address challenges arising from increased system complexity, operational risk, and the number and diversity of users. Feature flags can reduce risk, cut back on the amount of manual work required to oversee production releases, and demonstrate the real-world effects of flag modifications, particularly at scale.
Datadog provides feature flags embedded within its observability platform. Engineers can design feature flags, oversee rollouts, link health metrics to these rollouts, automate canary releases and rollbacks, and conduct advanced experiments, all within a unified workflow.
Additional features to consider include:
Reliable local evaluation with fail-safe defaults: Seek SDKs that assess flags locally without needing a network round-trip for each request and offer fallback options if the control plane is unreachable. This approach minimizes the impact of network problems or outages on application availability.
Cohort targeting and flexible rule evaluation: A solution should enable accurate targeting of specific cohorts, such as by region, device type, customer tier, or environment.
Progressive rollouts with automation hooks: Look for integrated support for staged rollouts that can progress, pause, or revert according to specified conditions. For teams, this feature reduces the risk and complexity of single, large, and complex production releases.
Observability and impact correlation: Select a solution that simplifies linking flag changes to service health indicators (e.g., latency, error rate, and throughput) and user-experience metrics. This is essential for rapidly addressing the question “what changed?” during investigations.
Dependency visibility and blast-radius awareness: Try to identify which services, endpoints, or code paths rely on a specific flag. Mapping these dependencies reduces uncertainty during rollouts and makes cleanup more secure.
Governance, access controls, and audit history: A robust solution provides role-based access, change approvals as needed, and audit logs that record who made changes and when. This can help minimize accidental modifications and ensure compliance with standards.
Lifecycle management and cleanup support: Because outdated flags can introduce ongoing complexity, teams should use tools that detect whether flags are fully implemented or obsolete to enable safe, gradual removal.
Learn more
Review the following links for deploying feature flags through Datadog’s observability platform: