# Administrations --- ## Usage (Self-Managed) ## Compute In Materialize, [clusters](/concepts/clusters/) are pools of compute resources (CPU, memory, and scratch disk space) for running your workloads, such as maintaining up-to-date results while also providing strong [consistency guarantees](/get-started/isolation-level/). > **Note:** In Materialize,various [system clusters](/sql/system-clusters/) are > pre-installed to improve the user experience as well as support system > administration tasks. You must provision at least one cluster to power your workloads. You can then use the cluster to create the objects ([indexes](/concepts/indexes/) and [materialized views](/concepts/views/#materialized-views)) that provide always-fresh results. In Materialize, both indexes and materialized views are incrementally maintained when Materialize ingests new data. That is, Materialize performs work on writes such that no work is performed when reading from these objects. The cluster size for a workload will depend on the workload's compute and storage requirements. Clusters are always "on", and you can adjust the [replication factor](/sql/create-cluster/#replication-factor) for fault tolerance. See [Compute usage factors](#compute-usage-factors) for more information on increasing a cluster's replication factor. ## Compute usage factors Factors that contribute to compute usage include: | Cost factor | Details | |-------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | [Replication factor for a cluster](/sql/create-cluster/#replication-factor). | Each replica of the cluster provisions a new pool of compute resources to perform exactly the same computations on exactly the same data.| | [Indexes](/concepts/indexes/) and [materialized views](/concepts/views) | As data changes (insert/update/delete), [indexes](/concepts/indexes/) and [materialized views](/concepts/views) perform incremental updates to provide up-to-date results. | | [Sources](/concepts/sources/) |• Sources that use upsert logic (i.e., [`ENVELOPE UPSERT`](/sql/create-sink/kafka/) or [`ENVELOPE DEBEZIUM` Kafka sources](/sql/create-sink/kafka/)) can lead to high memory and disk utilization.
• Other sources consume a negligible amount of resources in steady state. | | [`SELECT`s](/sql/select/) and [`SUBSCRIBE`s](/sql/subscribe/) |• [`SELECT`s](/sql/select/) and [`SUBSCRIBE`s](/sql/subscribe/) that do not use indexes and materialized views perform work.
• [`SELECT`s](/sql/select/) and [`SUBSCRIBE`s](/sql/subscribe/) that use indexes and materialized views are computationally **free**.| | [Sinks](/concepts/sinks/) | Only small CPU/memory costs.| ## Storage In Materialize, storage is roughly proportional to the size of your source datasets plus the size of any materialized views, with some overhead from uncompacted data and system metrics. Most data in Materialize is continually compacted, with the exception of [append-only sources](/sql/create-source/kafka/#append-only-envelope). As such, the total state stored in Materialize tends to grow at a rate that is more similar to OLTP databases than cloud data warehouses. --- ## Usage & billing (Cloud) Materialize determines billing based on your compute and storage usage. Materialize bills per second based on the [cluster(s)](/concepts/clusters/) you provision for your workloads. Each cluster is a pool of resources (CPU, memory, and scratch disk space) that must stay up and running to continually provide you with always-fresh results. For pricing details, see [Pricing](https://materialize.com/pricing/). ## Compute In Materialize, [clusters](/concepts/clusters/) are pools of compute resources (CPU, memory, and scratch disk space) for running your workloads, such as maintaining up-to-date results while also providing strong [consistency guarantees](/get-started/isolation-level/). The credit usage for a cluster is measured at a one second granularity. > **Note:** When you enable a Materialize region, various [system > clusters](/sql/system-clusters/) are pre-installed to improve the user > experience as well as support system administration tasks. Except for the > default `quickstart` cluster, you are not billed for these system clusters. You must provision at least one cluster to power your workloads. You can then use the cluster to create the objects ([indexes](/concepts/indexes/) and [materialized views](/concepts/views/#materialized-views)) that provide always-fresh results. In Materialize, both indexes and materialized views are incrementally maintained when Materialize ingests new data. That is, Materialize performs work on writes such that no work is performed when reading from these objects. The cluster size for a workload will depend on the workload's compute and storage requirements. To help users select the correct cluster size for their workload, Materialize uses cluster size names that are based on the compute credit spend, specifically, "centicredits" or `cc` (1/100th of a compute credit). For example, the `25cc` cluster size is equivalent to 0.25 compute credits/hour; the `200cc` cluster size is equivalent to 2 compute credits/hour. Larger clusters can process data faster and handle larger data volumes. > **Note:** You can resize a cluster to respond to changes in your workload. See [Sizing > your clusters](/sql/alter-cluster/#resizing). Clusters are always "on", and you can adjust the [replication factor](/sql/create-cluster/#replication-factor) for fault tolerance. See [Compute cost factors](#compute-cost-factors) for more information on the cost of increasing a cluster's replication factor. ### Compute cost factors The credit usage for a cluster is measured at a one second granularity. Factors that contribute to compute usage include: | Cost factor | Details | |-------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | [Replication factor for a cluster](/sql/create-cluster/#replication-factor). | Cost is calculated (at one second granularity) as cluster [`SIZE`](/sql/create-cluster/#size) * [`REPLICATION FACTOR`](/sql/create-cluster/#replication-factor). | | [Indexes](/concepts/indexes/) and [materialized views](/concepts/views) | As data changes (insert/update/delete), [indexes](/concepts/indexes/) and [materialized views](/concepts/views) perform incremental updates to provide up-to-date results. | | [Sources](/concepts/sources/) |• Sources that use upsert logic (i.e., [`ENVELOPE UPSERT`](/sql/create-sink/kafka/#upsert) or [`ENVELOPE DEBEZIUM` Kafka sources](/sql/create-sink/kafka/#debezium)) can lead to high memory and disk utilization.
• Other sources consume a negligible amount of resources in steady state. | | [`SELECT`s](/sql/select/) and [`SUBSCRIBE`s](/sql/subscribe/) |• [`SELECT`s](/sql/select/) and [`SUBSCRIBE`s](/sql/subscribe/) that do not use indexes and materialized views perform work.
• [`SELECT`s](/sql/select/) and [`SUBSCRIBE`s](/sql/subscribe/) that use indexes and materialized views are **free**.| | [Sinks](/concepts/sinks/) | Only small CPU/memory costs.| ## Storage In Materialize, storage is roughly proportional to the size of your source datasets plus the size of any materialized views, with some overhead from uncompacted data and system metrics. Materialize uses cheap, scalable object storage for its storage layer (Amazon S3), and primarily passes the cost through to the customer. Most data in Materialize is continually compacted, with the exception of [append-only sources](/sql/create-source/kafka/). As such, the total state stored in Materialize tends to grow at a rate that is more similar to OLTP databases than cloud data warehouses. ## Invoices > **Note:** Accessing usage and billing information in Materialize > requires **administrator** privileges. From the [Materialize console](/console/) (`Admin` > `Usage & Billing`), administrators can access their invoice. The invoice provides Compute and Storage usage and cost information. ## On Demand Materialize Cloud administrators can sign up for an [On Demand plan](https://materialize.com/pdfs/on-demand-terms.pdf) from the billing section of the [Materialize console](/console/). Pricing is usage-based and is billed on a monthly basis. Invoices will be sent to the account email and paid via the card on file on the first of the month. If you have questions about billing or are interested in converting to an annual enterprise contract, please [contact us](https://materialize.com/docs/support/) to discuss further. If you'd like to cancel your On Demand account with Materialize, please email cancellations@materialize.com to terminate services per the On Demand [Terms and Conditions](https://materialize.com/pdfs/on-demand-terms.pdf). ## Additional references - [Pricing](https://materialize.com/pricing/) - [How Materialize can lower the cost of freshness for data teams](https://materialize.com/promotions/cost-of-freshness/?utm_campaign=General&utm_source=documentation)