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All Content APIs are served via our globally distributed edge cache. Whenever a query is sent to the content API, its response is cached in multiple POP (data centers) around the globe.

Hygraph CDN MapHygraph CDN Map

Hygraph comes with two different content API endpoints, served by 2 different CDN providers. To understand which endpoint you should use, look at the following table.

Endpoint nameEndpointConsistencyAccess
Regular read & write endpointhttps://${region}
Eventual (Read-after-write within POP)Read & Write
High performance endpointhttps://${region}
EventualRead & Write

#Regular read & write endpoint

The Regular read & write endpoint allows for reads and writes. All your requests are cached, and any mutation will invalidate the cache for your project.

Even though this endpoint is eventually consistent, within a single region you will get read-after-write consistency.

This endpoint is ideal for delivering content around the globe with one endpoint, but it only has basic caching logic.

#High performance endpoint

We use a state-of-the-art caching approach, and we have developed a high-performance edge service on the Fastly Compute Edge platform. The service is deployed close to your users around the world.

This endpoint is ideal for delivering content around the globe with low latency and high read-throughput.


To understand both endpoints better, you should have a basic knowledge of cache consistency modes. You can be sure that any changes have persisted at Hygraph if your response was successful.

#Eventual consistency

Some changes are not visible immediately after the update. For example, if you create, delete or update content the update is distributed around the globe with a short delay. Theoretically, if you read content right after a mutation, you may receive stale content.

#Read-after-write consistency

In contrast, read-after-write consistency guarantees that a subsequent read after an update, delete, or create can see those changes immediately. This is only valid for operations hitting the same POP (point-of-presence).

#Model + stage based invalidation

Model + stage based invalidation, which is only available for our High performance endpoint, allows invalidating only the models that were changed based on the mutations used for content and schema changes, rather than invalidate the complete cache.

Regarding queries that fetch related content that needs to be invalidated: We analyze query responses and invalidate only the cached queries that contain the changed model.

For example:

Considering invalidation after a schema change, if a user were to update the Author model shown in the example above, this cached query would be invalidated, as it also returned the Author model.

For content changes, we also take the stage into account, meaning that updating an Author entry, and not publishing it, would invalidate all cached queries that returned the DRAFT stage and the Author model. Queries that returned the PUBLISHED stage will remain cached.

#Smart cache invalidation

Our System understands if mutations are flowing through the cache and invalidates the affected resources with an eventual consistency guarantee.


In case of an outage of our APIs - this includes remote field origin errors as well- we will fall back for at least 24h to the latest cached content on our edge servers. This adds an additional reliability layer.

You can use a header for the High performance endpoint that lets you set stale-if-error on a per query basis.

"hyg-stale-if-error": "21600"

The values are in seconds.


With the High performance endpoint you will get cached responses directly, while we update the content in the background. This means your content is always served on the edge, with low latency for your users.

Staleness refers to how long we deliver stale data while we revalidate the cache in the background if the cache was invalidated.

You can use a header for the High performance endpoint that lets you set stale-while-revalidate on a per query basis.

"hyg-stale-while-revalidate": "27"

The values are in seconds.

#Remote fields

GraphQL queries that contain remote fields are cached differently. By default, a response is marked as cacheable when all remote field responses are cacheable according to rfc7234. You can control the TTL (Time-to-Live) cache by returning the Cache-Control response header.

By default, we will set a TTL of 900s, you can set a minimum TTL of 60s. While it is also possible to respond with a no-store cache directive to disable the cache, this is not recommended, as it marks the entire response as not cacheable and will increase the load on your origin.