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Review Deployment Options

On this page

  • Testing and Prototyping Environments
  • Deployment Type
  • Cluster Tiers
  • Cloud Provider and Region
  • Node Architecture
  • Limitations
  • Production Environment
  • Deployment Type
  • Cluster Tiers
  • Cloud Provider and Region
  • Node Architecture
  • Benefits
  • Size and Scale Your Cluster
  • Migrate to Dedicated Search Nodes

You can structure your Atlas cluster with different deployment types, cloud providers, and cluster tiers to meet the needs of a pre-production or production environment. Use these recommendations to select the deployment type, cloud provider and region, and cluster and search tiers for performing vector search.

Environment
Deployment Type
Cluster Tier
Cloud Provider Region
Node Architecture
Testing Queries
Shared or dedicated cluster
M0, M2, M5, or higher tier
All
MongoDB and Search processes run on the same node
Prototyping Applications
Dedicated cluster
M10, M20, or higher tier
All
MongoDB and Search processes run on the same node
Production
Dedicated cluster with separate search nodes
M10 or higher cluster tier and S30 or higher search tier
AWS in some regions or Google Cloud in all regions
MongoDB and Search processes run on different nodes

To learn more about these deployment models, review the following sections:

  • Testing and Prototyping Environments

  • Production Environment

For testing your vector search queries and prototyping your application, we recommend the following configuration for your cluster.

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

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.

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.

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.

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.

For your production-ready application, we recommend the following cluster configuration.

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

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.

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. However, search nodes are only available for AWS and Google Cloud in a subset of the cloud provider regions. So, you must select a cloud provider and region where search nodes are available for your deployment.

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.

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.

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:

Embedding Model
Vector Dimension
Space Requirement
OpenAI text-embedding-ada-002
1536
6kb
Google text-embedding-gecko
768
3kb

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.

Note

To learn more about the cost of search nodes and search tiers, expand View all plan features and click Atlas Vector Search in the the MongoDB Pricing page.

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. If you're running a multi-region cluster or if your cluster is currently deployed on Azure, migrate your cluster to AWS or Google Cloud. Dedicated search nodes are only available for single-region AWS and Google Cloud clusters. To learn more, see Modify the Cloud Provider & Region.

    Dedicated search nodes are only available on a subset of the AWS regions and in all supported Google Cloud regions. Make sure to deploy your cluster in a region where search nodes are also available. If your existing cluster is in a region where search nodes are not available, migrate your cluster to a region 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.

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