Summary: Scaling options for AI applications that use pgvector on Lakebase Postgres, covering vertical scaling from 0.25 CU (1 GB RAM) to 56 CU (224 GB RAM) with autoscaling up to 16 CU, and horizontal scaling via read replicas for offloading vector similarity search workloads at no extra storage cost.

Scale your AI application with Neon's Autoscaling and Read Replica features

You can scale your AI application built on Postgres with pgvector in the same way you would any Postgres app: Vertically with added CPU, RAM, and storage, or horizontally with read replicas.

In Neon, scaling vertically is a matter of selecting the desired compute size. Neon supports compute sizes ranging from .25 CU (1 GB RAM) up to 56 CU (224 GB RAM). Autoscaling is supported up to 16 CU. Larger computes are fixed size computes (no autoscaling). The maintenance_work_mem values shown below are approximate.

See Edit a compute to configure your compute size. Available compute sizes differ according to your Neon plan.

To optimize pgvector index build time, you can increase the maintenance_work_mem setting for the current session beyond the preconfigured default shown in the table above with a command similar to this:

SET maintenance_work_mem='10 GB';

The recommended maintenance_work_mem setting is your working set size (the size of your tuples for vector index creation). However, your maintenance_work_mem setting should not exceed 50 to 60 percent of your compute's available RAM (see the table above). For example, the maintenance_work_mem='10 GB' setting shown above has been successfully tested on a 7 CU compute, which has 28 GB of RAM, as 10 GB is less than 50% of the RAM available for that compute size.

You can also enable Neon's autoscaling feature for automatic scaling of compute resources. Neon's Autoscaling feature automatically scales up compute on demand in response to application workload and down to zero on inactivity.

For example, if your AI application experiences heavy load during certain hours of the day or at different times throughout the week, month, or calendar year, Neon automatically scales compute resources without manual intervention according to the compute size boundaries that you configure. This enables you to handle peak demand while avoiding consuming compute resources during periods of low activity.

Enabling autoscaling is also recommended for initial data loads and memory-intensive index builds to ensure sufficient compute resources for this phase of your AI application setup.

To learn more about Neon's autoscaling feature and how to enable it, refer to our Autoscaling guide.

On the Free plan, you get 0.5 GB of storage per project. Storage on paid plans is usage based. See Neon plans for details.

Neon supports read replicas, which are independent read-only computes designed to perform read operations on the same data as your primary read-write compute. Read replicas do not replicate data across database instances. Instead, read requests are directed to the same data source. This architecture enables read replicas to be created instantly, enabling you to scale out CPU and RAM, but because data is read from a single source, there are no additional storage costs.

Since vector similarity search is a read-only workload, you can use read replicas to offload reads from your primary compute to a dedicated compute. After you create a read replica, swap your Neon connection string for the read replica connection string.

Neon's read replicas support the same compute sizes outlined above. Read replicas also support autoscaling.

To learn more about the Neon read replicas, see read replicas and refer to our Working with Neon read replicas guide.

• Optimize vector search

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