Review Deployment Options

Deployment Type

For testing Atlas Vector Search queries, you can deploy a shared or dedicated cluster or a local Atlas deployments.

Node Architecture

In this deployment model, the search mongot process runs alongside mongod on each node in the Atlas cluster. The mongod process routes queries to the mongot on the same node and they share the same resources.

By default, Atlas enables the search mongot process on the same node that runs the mongod process when you create your first Atlas Vector Search index. The mongot process performs the actions described in About the mongot Process.

Limitations

You might experience resource contention between the database mongod and the search mongot processes. This could negatively impact the performance of your index and latency of your queries. We recommend this deployment model for only testing and prototyping environments. For production-ready applications and associated search workloads, we recommend migrating to dedicated Search Nodes.

Deployment Type

For production-ready applications, you need a dedicated cluster.

Cluster Tiers

Dedicated clusters include M10 and higher tiers. The M10 and M20 tiers are suitable for development and for production environments. However, the higher tiers can handle large datasets and production workloads. We recommend that you also deploy dedicated Search Nodes for your search workload. This allows you to scale your search deployment independently and appropriately.

Cloud Provider and Region

The cloud provider and region that you choose affects the configuration options and search tiers available for the cluster and the cost of running the cluster.

All the cluster tiers are available in all the supported cloud provider regions with the exception of certain AWS regions. For clusters hosted on AWS, you must select a cloud provider available for your deployment.

Node Architecture

In this deployment model, the mongot process runs on Search Nodes, which are separate from the cluster nodes on which the mongod process runs. Atlas deploys Search Nodes with each cluster or with each shard on the cluster.

For example, if you deploy two Search Nodes for a cluster with three shards, Atlas deploys six Search Nodes, two per shard. You can also configure the number of Search Nodes and the amount of resources provisioned for each search node.

When you deploy separate Search Nodes, Atlas automatically assigns a mongod for each mongot for indexing. The mongot communicates with the mongod to listen for and sync index changes for the indexes that it stores. Atlas Vector Search indexes and processes your queries similar to when both the mongod and mongot processes run on the same node. To learn more, see How to Index Fields for Vector Search and Run Vector Search Queries. To learn more about deploying Search Nodes separately, see Search Nodes for Workload Isolation.

When you migrate to Search Nodes, Atlas deploys the Search Nodes, but doesn't serve queries on the nodes until it successfully builds all the indexes on the cluster on the Search Nodes. While Atlas builds the indexes on the new nodes, it continues to serve queries using the indexes on the cluster nodes. Atlas starts serving queries from the Search Nodes only after it successfully builds the indexes on the Search Nodes and removes the indexes on the cluster nodes.

If you delete all the Search Nodes on your cluster, there will be an interruption in processing your search query results. To learn more, see Modify a Cluster. If you delete your Atlas cluster, Atlas pauses and then deletes all associated Atlas Vector Search deployments (mongot processes).

Benefits

This deployment model provides the following benefits:

• Efficiently utilize your resources while ensuring high availability of your resources for search workloads.

• Size and scale your search deployment independently from your database deployment.

• Automatically process Atlas Vector Search queries concurrently, improving the response time especially on large datasets. To learn more, see Parallel Query Execution Across Segments.

Size and Scale Your Cluster

Atlas Vector Search holds the entire index in memory, so you need to ensure there's enough memory for the Atlas Vector Search index and JVM. Each index is a combination of the vectors being indexed and additional metadata. The index size is primarily determined by the size of the vectors that you are indexing, with metadata space typically being relatively nominal.

Consider the following requirements for a single vector:

BinData vector subtype int8 quantized vectors. To learn more, see Ingest Quantized Vectors.

The required space scales linearly with the number of vectors that you are indexing and with the vector dimensionality. You can also use the Search Index Size metric to determine the amount of space and memory you need on your Search Nodes.

When you deploy dedicated Search Nodes, you can choose from different search tiers. Each search tier has a default RAM capacity, storage capacity, and CPU. This allows you to size and scale your cluster independently from your database deployment. To scale your search deployment separately, you can make the following changes to your cluster configuration at any time:

• Adjust the number of Search Nodes on your cluster.

• Adjust the CPU, RAM, and storage of the node by changing search tiers.

We recommend that your node has RAM that is at least 10% larger than the total size of your Atlas Vector Search indexes. We also recommend that you ensure you have enough available CPUs. Query latency depends on the number of available CPUs, which can significantly impact the level of internal concurrency that accelerates query performance.

Migrate to Dedicated Search Nodes

Dedicated Search Nodes allow you to both size and scale your search deployment separately from your cluster. It also eliminates any resource contention that you might experience on a cluster that runs both the database and search processes on the same node.

To migrate to dedicated Search Nodes, make the following changes to your deployment:

1. If your deployment is currently using a shared tier, upgrade your cluster to a higher tier. Dedicated Search Nodes are supported only for M10 and higher cluster tiers. To learn more about migrating to a different cluster tier, see Modify the Cluster Tier.

2. Dedicated Search Nodes are available on a subset of the AWS regions and in all supported Google Cloud and Azure regions. Make sure to deploy your cluster in regions where Search Nodes are also available. If your existing cluster is in regions where Search Nodes are not available, migrate your cluster to regions where Search Nodes are available. To learn more, see Cloud Provider Regions.

3. Enable Search Nodes for workload isolation and configure Search Nodes. To learn more, see Add Search Nodes.