Review Deployment Options
On this page
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
For testing your vector search queries and prototyping your application, we recommend the following configuration for your cluster.
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.
Production Environment
For your production-ready application, we recommend the following cluster configuration.
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. 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.
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:
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.
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:
If your deployment is currently using a shared tier, upgrade your cluster to a higher tier. Dedicated search nodes are supported only for
M10and higher cluster tiers. To learn more about migrating to a different cluster tier, see Modify the Cluster Tier.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.
Enable Search Nodes for workload isolation and configure search nodes. To learn more, see Add Search Nodes.