Architecture, Hosting, and Infrastructure

Where are GraphCMS projects being hosted?

GraphCMS projects are physically hosted on state-of-the-art Amazon Web Services (AWS) infrastructure.

Projects on Dedicated Infrastructure, an Enterprise service, can be hosted in every available region in the AWS global network. Contact us for more information.

Projects on Shared Infrastructure are deployed on three continents for best performance and reliability (Europe/EU (Germany), US East (Virginia), US West (Oregon), Asia (Japan)). When creating a new project, you can select which of these locations best suits your project.

Is the GraphCMS architecture horizontally scalable?

Yes, GraphCMS is hosted on a scalable state-of-the-art server architecture hosted by Amazon Web Services and based on PostgresQL.

Is GraphCMS multi-tenant (shared between several countries)?

Yes, this is possible with project cloning. We support hosting in four regions out of the box (Europe/EU (Ireland), US East (Virginia), US West (Oregon), Asia (Japan)). More regions are available with dedicated infrastructure upon request.

Is the storage size or the number of entries and records in a GraphCMS project limited?

Depending on your plan, there are no limits on storage or project entries and records.

What are the GraphCMS latency metrics?

This depends on your location, the location of your users, and the complexity of your GraphQL queries. We aim for a latency of a maximum of 70-100 ms for requests.

How does GraphCMS host assets?

The assets are delivered via our provider Filestack who use Fastly as their content distribution network (CDN).

Where are my application files (e.g. html and javascript) hosted?

Your application files are usually in your control. We provide the API for the content but not a full-stack solution to manage your frontend deployments. A lot of our users use a myriad of services like Netlify, Vercel, Azure, AWS, or Heroku to host and deploy.

What does GraphCMS use for monitoring & regression testing its GraphQL servers?

We have different testing strategies to avoid regressions. It is a mix of unit testing and integration tests. We also periodically run automated tests against canary deployments before releasing new features, to ensure we don’t introduce regressions.

Regarding monitoring, we use LSTIO (Prometheus) for monitoring low-level metrics (like resources as CPU, memory, networks throughput, etc), as well as apollo engine for our management server, which is used by the web app (e.g. to create a project, create a model, etc).

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