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Azure Cosmos DB for MongoDB offers RU-based and vCore-based models for building scalable, cloud-native applications. This article compares these models and helps you select the best option for your workload and business needs.
Here are a few key factors to help you decide which is the right option for you.
- You're migrating (lift & shift) an existing MongoDB workload or building a new MongoDB application.
- Your workload has more long-running queries, complex aggregation pipelines, distributed transactions, joins, etc.
- You prefer high-capacity vertical and horizontal scaling with familiar vCore-based cluster tiers such as M30, M40, M50, and more.
- You're running applications requiring 99.995% availability.
- You need native support for storing and searching vector embeddings.
- You're building new cloud-native MongoDB apps or refactoring existing apps for cloud-native benefits.
- Your workload has more point reads (fetching a single item by its _id and shard key value) and few long-running queries and complex aggregation pipeline operations.
- You want limitless horizontal scalability, instantaneous scale up, and granular throughput control.
- You're running mission-critical applications requiring industry-leading 99.999% availability.
The vCore and RU services have different architectures with important billing differences.
- You'd like dedicated instances that utilize preset CPU, memory, and storage resources, which can dynamically scale to suit your needs.
- You prefer to pay a consistent flat fee based on compute (CPU, memory, and the number of nodes) and storage.
You'd like a multitenant service that instantly allocates resources to your workload, aligning with storage and throughput requirements. In this option, throughput is based on request units (RUs).
You prefer to pay fixed (standard provisioned throughput) or variable (autoscale) fees corresponding to Request Units (RUs) and consumed storage.
Note
RU charges depend on the selected model: provisioned throughput (standard or autoscale) or serverless.