Feature Flag
An engineering practice that decouples code deployment from feature release. A flag controls whether a new feature is visible to users, supporting gradual rollout and rapid rollback.
Timeline (last 90 days)
功能开关若在初始化阶段发起网络请求，会把网络往返时间计入冷启动，从而放大尾延迟。
“随部署打包数据文件 + 调用携带修订版本号”属于混合方案，既保留本地读取的低延迟，又通过版本号在调用链路上校验数据新鲜度。
A feature flag is an engineering practice that decouples feature release from code deployment: switch checks are embedded in the code in advance, and the configuration center dynamically determines which users and which environments a feature takes effect for.
Using feature flags' percentage rollout directly to run experiments makes it easy to encounter problems such as non-random traffic splitting, imbalanced sample proportions, and the same user being assigned to different groups on different devices.
Feature flags and A/B testing share the "grouping + traffic splitting" capability in engineering implementation, but their goals differ: flags focus on release control, while experiments focus on causal inference.
The value of Flagsmith Experiments lies in reusing the existing grouping identifiers of flags as experiment units and supplementing them with a randomization and statistics layer.
On LaunchDarkly, anonymous visitors push up client-side monthly active context costs, reaching about $1,020/month at 100k MAU.
Local Evaluation mode uses a server-side SDK to periodically pull the full rule set and evaluate it in memory, communicating with the platform only when rules change, thereby shifting the billing unit from "per evaluation" to "per rule sync".
Feature flag platforms typically bill by "context", i.e. each evaluated user or device identifier counts as one metering unit. Because anonymous visitors have no stable ID, platforms often generate a new context for each session or even each page load, causing MAU counts to be inflated.
All Facts (9)
功能开关若在初始化阶段发起网络请求，会把网络往返时间计入冷启动，从而放大尾延迟。
50%Unverified“随部署打包数据文件 + 调用携带修订版本号”属于混合方案，既保留本地读取的低延迟，又通过版本号在调用链路上校验数据新鲜度。
50%UnverifiedA feature flag is an engineering practice that decouples feature release from code deployment: switch checks are embedded in the code in advance, and the configuration center dynamically determines which users and which environments a feature takes effect for.
50%UnverifiedUsing feature flags' percentage rollout directly to run experiments makes it easy to encounter problems such as non-random traffic splitting, imbalanced sample proportions, and the same user being assigned to different groups on different devices.
50%UnverifiedFeature flags and A/B testing share the "grouping + traffic splitting" capability in engineering implementation, but their goals differ: flags focus on release control, while experiments focus on causal inference.
50%UnverifiedThe value of Flagsmith Experiments lies in reusing the existing grouping identifiers of flags as experiment units and supplementing them with a randomization and statistics layer.
50%UnverifiedOn LaunchDarkly, anonymous visitors push up client-side monthly active context costs, reaching about $1,020/month at 100k MAU.
50%UnverifiedLocal Evaluation mode uses a server-side SDK to periodically pull the full rule set and evaluate it in memory, communicating with the platform only when rules change, thereby shifting the billing unit from "per evaluation" to "per rule sync".
50%UnverifiedFeature flag platforms typically bill by "context", i.e. each evaluated user or device identifier counts as one metering unit. Because anonymous visitors have no stable ID, platforms often generate a new context for each session or even each page load, causing MAU counts to be inflated.
50%