> For the complete MongoDB documentation index, see www.mongodb.com/docs/llms.txt

# mongot Sizing Quickstart

To quickly provide your `mongot` deployment with a healthy balance of resources for most general use cases, a small or medium High-CPU node is often an effective starting point. This configuration provides a solid foundation for common search workloads.

For more precise resource provisioning tailored to a specific workload, review the following pages:

- [Deployment Architecture Patterns](https://www.mongodb.com/docs/search/self-managed/current/resource-planning-sizing/architecture.md#std-label-mongot-sizing-architecture-patterns)

- [Resource Allocation Considerations](https://www.mongodb.com/docs/search/self-managed/current/resource-planning-sizing/resource-allocation.md#std-label-mongot-sizing-resource-allocation)

- [Hardware Considerations](https://www.mongodb.com/docs/search/self-managed/current/resource-planning-sizing/hardware.md#std-label-mongot-sizing-hardware)

These pages offer guidance on mission-critical applications, or when higher-fidelity optimization is required.

**Note:**

Sizing resources for search and vector search workloads is an iterative process. These examples represent a starting point, but advanced considerations and measurements may be required for sizing a specific workload.

## Workload Classes

`mongot` deployments fall into two classes:

- Low-CPU (suitable for lower data volumes and vector search)

- High-CPU (suitable for higher data volumes and full-text search)

Use the following guidance to select a starting configuration that matches your application's needs.

### Low-CPU Workloads

The low-CPU archetype is ideal for vector search applications or low data volumes where memory is prioritized over raw CPU power. These nodes typically have an **8:1 RAM-to-CPU ratio**. A key factor in determining the appropriate size category is an estimate of your **expected total
vector size**. To see reference vector size ranges, refer to the table in the **Select a starting size** step of the [Introduction.](https://www.mongodb.com/docs/search/self-managed/current/resource-planning-sizing/introduction.md#std-label-mongot-sizing-intro)

The following table shows recommendations for memory, storage, and CPU cores based on your expected workload in Low-CPU deployments:

| Workload Size Category | Default Memory (GB) | Default Storage (GB) | CPU Cores |
| --- | --- | --- | --- |
| Small | 8 - 16 | 50 - 100 | 1 - 2 |
| Medium | 32 - 64 | 200 - 400 | 4 - 8 |
| Large | 128 - 256 | 800 - 1600 | 16 - 32 |

Additional considerations:

- **Small**: Suitable for initial testing or very small vector search applications.

- **Medium**: Suitable for growing vector search use cases or moderate data volumes.

- **Large**: Suitable for substantial vector search applications or larger low-CPU-intensive workloads.

### High-CPU Workloads

The High-CPU archetype is designed for general-purpose full-text search workloads where queries are more CPU-intensive. These nodes typically have a **2:1 RAM-to-CPU ratio**. Key factors in determining the appropriate size category include the required throughput (QPS (queries per second)) and the expected indexing load. The volume of inserts can serve as a proxy for indexing load. More inserts generally indicate a higher level of indexing activity. To see reference QPS ranges, refer to the table in the **Select a starting size** step of the [Introduction.](https://www.mongodb.com/docs/search/self-managed/current/resource-planning-sizing/introduction.md#std-label-mongot-sizing-intro)

The following table shows recommendations for memory, storage, and CPU cores based on your expected workload in High-CPU deployments:

| Workload Size Category | Default Memory (GB) | Default Storage (GB) | CPU Cores |
| --- | --- | --- | --- |
| Small | 4 - 8 | 100 - 200 | 2 - 4 |
| Medium | 16 - 32 | 400 - 800 | 8 - 16 |
| Large | 64 - 96 | 1600 - 2400 | 32 - 48 |

Additional considerations:

- **Small**: A starting point for full-text search with moderate query rates. A minimum setup of two small nodes (4 CPUs total) supports roughly 40 QPS.

- **Medium**: Suitable for more active full-text search applications with higher query throughput.

- **Large**: Suitable for demanding full-text search, heavy indexing, or substantial query workloads.

## Considerations for Large Vector Search Workloads

Vector search is a key focus area for AI applications. Modern techniques like automatic binary quantization are shifting the primary resource constraint from RAM to storage. Binary quantization makes indexes more storage-constrained.

In these cases, consider a Low-CPU class node with a large amount of storage available. The large storage supports full-fidelity vector embeddings and the quantized version of the source vector embeddings. This alignment of resources to workload ensures you can build and scale modern AI applications efficiently and economically.