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Fastly Compute@Edge

Starter Kit hosted on GitHub for running Optimizely Feature Experimentation feature flags and experiments on Fastly's Compute@Edge offering.

Fastly Compute@Edge

Fastly’s Compute@Edge offering allows you to build high scale, globally distributed applications and execute code at the edge.

Advantages of Compute@Edge

  • Execute code quickly – No cold start on servers running around the world.

  • Exceptional user experiences – Local development and debugging environment help increase end-user experience.

  • Enhanced security – Isolation technology helps keep requests secure.

  • Familiar languages – Developers can create code in familiar languages such as JavaScript and Rust.



Although you can write Fastly Compute@Edge code in various languages, Optimizely Feature Experimentation currently only provides a starter kit for JavaScript.

Compute@Edge use cases

Compute@Edge provides a fast, secure and instant global compute platform that can be used for various use cases, such as ad targeting, content stitching and authentication. For a complete list of use cases, refer to the Compute@edge documentation.

Restrictions and Limitations

There are various restrictions and limitations to Compute@Edge to note when running Full Stack feature flags and experiments. Refer to the official Fastly Compute@Edge documentation for the most recent resource limitations. Also, review the known limitations and constraints for Compute@Edge before getting started.

Optimizely + Fastly Compute@Edge Starter Kit

The Optimizely Feature Experimentation starter kit for Fastly's Compute@Edge embeds and extends our Javascript SDK for you to start implementing experimentation and feature flagging for your experiences at the edge.

For a guide to getting started with our platform more generally, this can be combined with the steps outlined in our Javascript Quickstart here.

External Calls via Fastly Backends

This starter kit overrides the standard Optimizely Feature Experimentation Javascript SDK's external calls to use Compute@Edge's fetch against registered backend endpoints. This backend setup also provides performant caching for the Optimizely Datafile.

Identity Management

Out of the box, Optimizely's Feature Experimentation SDKs require a user-provided identifier to be passed in at runtime to drive experiment and feature flag decisions. This example generates a unique ID, stores it in a cookie and reuses it to make the decisions sticky. Alternatively, you can use an existing unique identifier available within your application and pass it in as the value for the OPTIMIZELY_USER_ID cookie.


For more information on how Optimizely Feature Experimentation SDKs assign users to feature flags and experiments, see the documentation on how bucketing works.

How to use


You must have these prerequisites to use this start kit:

Get started

  1. Create a new folder on your local machine and initialize a Fastly Compute@Edge service using the Fastly CLI from the starter kit hosted on GitHub.

    fastly compute init --from
  2. Follow the wizard and provide the service name, description and any other information.

    1. a) Add your service_id to fastly.toml if you want to use an existing Fastly servce.
  3. Update your Optimizely sdkKey and flagKey in src/index.js. Your SDK keys can be found in the Optimizely application under Settings.

  4. Build and publish:

fastly compute publish
  1. Monitor logs:
fastly log-tail

Additional resources