*[Installation](open-source/pgcat/installation.md)

*[Configuration](open-source/pgcat/configuration.md)

*[PostgresML Cloud](product/postgresml-cloud/README.md)

*[Serverless](product/postgresml-cloud/serverless.md)

*[Dedicated](product/postgresml-cloud/dedicated.md)

*[Enterprise](product/postgresml-cloud/enterprise.md)

*[Embeddings](guides/embeddings/README.md)

*[Unified RAG](guides/unified-rag.md)

*[OpenSourceAI](guides/opensourceai.md)

*[Natural Language Processing](guides/natural-language-processing.md)

*[Vector database](product/vector-database.md)

*[Vector database](guides/vector-database.md)

*[Overview](cloud/overview.md)

*[Serverless](cloud/serverless.md)

*[Dedicated](cloud/dedicated.md)

*[Enterprise](cloud/enterprise/README.md)

*[Teams](cloud/enterprise/teams.md)

*[VPC](cloud/enterprise/vpc.md)

*[Architecture](resources/architecture/README.md)

Once logged in, select "New Database" from the left menu and choose the Dedicated Plan.

<figure><imgsrc="../../.gitbook/assets/spaces_B7HH1yMjCs0skMpuwNIR_uploads_S9xbhlwvqnnFUYSJLJug_image.webp"alt=""><figcaption><p>Create new database</p></figcaption></figure>

<figure><imgsrc="../.gitbook/assets/spaces_B7HH1yMjCs0skMpuwNIR_uploads_S9xbhlwvqnnFUYSJLJug_image.webp"alt=""><figcaption><p>Create new database</p></figcaption></figure>

<figure><imgsrc="../../.gitbook/assets/image(4).png"alt=""><figcaption><p>Choose the Dedicated plan</p></figcaption></figure>

<figure><imgsrc="../.gitbook/assets/image%20(4).png"alt=""><figcaption><p>Choose the Dedicated plan</p></figcaption></figure>

Enterprise plans are ideal large companies that have special compliance needs and deployment configurations; with options for cloud-prem (VPC), on-prem, ACL’s and more.

Invite additional team members to manage your databases.

PostgresML can be launched in your Virtual Private Cloud (VPC) account on AWS, Azure or GCP.

<figure><imgsrc="../../.gitbook/assets/vpc.png"alt=""><figcaption><p>Deploy in your cloud</p></figcaption></figure>

The PostgresML control plane provides a complete management solution to control the resources in your cloud account:

- Responsible for PostgresML instance launches, backups, monitoring and failover operations. This requires permission to create and destroy AWS EC2, EBS and AMI resources inside the designated VPC.

- Does not read/write any data inside PostgresML databases other than status metadata inside system tables or the pgml schema necessary to perform the previously mentioned operations.

Quickly and easily create a PostgresML engine that can scale from very little capacity to gigabytes of GPU cache and terabytes of disk storage. Ideal for teams that want to start small and grow as their usage of PostgresML increases.

[Learn more about serverless](serverless)

[Learn more about serverless](serverless.md)

Dedicated plans provide a large assortment of hardware, including CPU and GPU configurations, near-bottomless storage capacity and horizontal scaling into millions of queries per second. Ideal for larger startups and enterprises that have established PostgresML as their AI database of choice.

[Learn more about dedicated](dedicated)

[Learn more about dedicated](dedicated.md)

Enterprise plans are ideal large companies that have special compliance needs and deployment configurations; with options for cloud-prem (VPC), on-prem, ACL’s and more.

[Learn more about enterprise](enterprise)

[Learn more about enterprise](enterprise/)

Once logged in, select "New Database" from the left menu and choose the Serverless Plan.

<figure><imgsrc="../../.gitbook/assets/image(1).png"alt=""><figcaption><p>Create new database</p></figcaption></figure>

<figure><imgsrc="../.gitbook/assets/image%20(1).png"alt=""><figcaption><p>Create new database</p></figcaption></figure>

<figure><imgsrc="../../.gitbook/assets/image(2).png"alt=""><figcaption><p>Choose the Serverless plan</p></figcaption></figure>

<figure><imgsrc="../.gitbook/assets/image%20(2).png"alt=""><figcaption><p>Choose the Serverless plan</p></figcaption></figure>

By building PostgresML on top of a mature database, we get reliable backups for model inputs and proven scalability without reinventing the wheel, so that we can focus on providing access to the latest developments in open source machine learning and artificial intelligence.

This guide will help you get started witha generous[free account](create-your-database), which includes access to GPU accelerated models and 5 GB of storage, or you can skip to our[Developer Docs](/docs/resources/developer-docs/quick-start-with-docker) to see how to run PostgresML locally with our Docker image.

This guide will help you get started with[$100 credits](create-your-database), which includes access to GPU accelerated models and 5 GB of storage, or you can skip to our[Developer Docs](/docs/resources/developer-docs/quick-start-with-docker) to see how to run PostgresML locally with our Docker image.

Partitioning by hash, unlike by range, can be applied to any data type, including text. A hash function is executed on the partition key to create a reasonably unique number, and that number is then divided by the number of partitions to find the right child table for the row.

To create a table partitioned by hash, the syntax is similar to partition by range. Let's use the USA House Prices dataset we used in[Vectors](../../product/vector-database.md) and[Tabular data](README.md), and split that table into two (2) roughly equal parts. Since we already have the`usa_house_prices` table, let's create a new one with the same columns, except this one will be partitioned:

To create a table partitioned by hash, the syntax is similar to partition by range. Let's use the USA House Prices dataset we used in[Vectors](../../cloud/vector-database.md) and[Tabular data](README.md), and split that table into two (2) roughly equal parts. Since we already have the`usa_house_prices` table, let's create a new one with the same columns, except this one will be partitioned: