Commonly used terms and abbreviations within Grafana Enterprise Metrics documentation are as follows:
|Access policy||An access policy is a resource that contains a granular set of permissions which specify what actions a request to GEM is allowed to do. In GEM, access policies are created with the desired set of permissions. Then API tokens can be generated that are associated with a particular access policy. You can also configure GEM to integrate with an OAuth backend to externally generate tokens associated with a particular access policy.|
|Cluster||A cluster is a licensed deployment of Grafana Metric Enterprise. Clusters are uniquely named and must have a corresponding license.|
|Tenant||A tenant is scoped to a particular cluster. New samples can be written to a tenant and queries can be issued to a particular tenant. Each tenant will store its metrics in a separate set of TSDB blocks that are stored in the configured storage bucket. The blocks themselves will be stored in the bucket with the name of the tenant as a prefix. This means once a tenant is created, its name cannot be changed. However, the display_name for a tenant can be changed.|
|Token||A token is a randomly generated string that can be used as an API key when making requests to GEM.|
Related Enterprise Metrics resources
Running Prometheus-as-a-service with Grafana Enterprise Metrics
Introducing Grafana Enterprise Metrics (GEM), a simple and scalable Prometheus service that is seamless to use, simple to maintain, and supported by Grafana Labs.
How Robinhood scaled from 100M to 700M time series with Grafana Enterprise Metrics
In this GrafanaCONline session, the Robinhood team tells how GME (GameStop) led to GEM (Grafana Enterprise Metrics).
Benchmarking Grafana Enterprise Metrics for horizontally scaling Prometheus up to 500 million active series
We stress-tested GEM to show how it horizontally scaled. One takeaway: Hardware usage scales linearly up to 500 million active series.