We've thought quite a bit about how to make this work as a service. The key to our architecture is that evaluating a feature flag for a user does not involve a

We've thought quite a bit about how to make this work as a service. The key to our architecture is that evaluating a feature flag for a user does not involve a remote call. We make that work by embedding a rule evaluation engine in our SDKs. When you request a flag, the user is compared against these rules (in memory) and served the appropriate variation. We then use a streaming API to serve rule changes, so when you make a change to your dashboard, the new rules are streamed to all your backend servers within a few hundred milliseconds. If you need even more resiliency, you can deploy a small service in your own infrastructure (https://github.com/launchdarkly/ld-relay https://github.com/launchdarkly/ld-relay) that allows you to persist these flag configurations in Redis.