Review Deployment Options

Deployment Type

For testing Atlas Vector Search queries, you can deploy shared or dedicated clusters.

Cluster Tiers

Shared clusters include the M0, M2, and M5 tiers. These low-cost cluster types are available for testing your Atlas Vector Search queries. However, you might experience resource contention and query latency on shared clusters. If you begin your project with a shared cluster, we recommend upgrading to a higher tier when your application is ready for production.

Dedicated clusters include M10 and higher tiers. The M10 and M20 tiers are suitable for prototyping your application. You can upgrade to higher tiers to handle large datasets or deploy dedicated Search Nodes for workload isolation when your application is ready for production.

Cloud Provider and Region

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

All the cluster tiers are available in all the supported cloud provider regions.

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. To learn more about the behavior of mongod and mongot processes running on the same node, see Atlas Search Architecture.

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. To learn more about the behavior of mongod and mongot processes running on different nodes, see Search Nodes Architecture.

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. We recommend that your node has RAM that is at least 10% larger than the total size of your Atlas Vector Search indexes. 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. While the index size can vary based on the amount of metadata, the memory required for additional metadata is typically minimal.

Consider the following requirements for a single vector:

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.

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.