MongoDB Atlas

The Difference Between MongoDB and MongoDB Atlas

MongoDB is a popular document database with a wide range of features, including flexible schema and built-in replication. Its scalable architecture and high availability have made it a popular choice for various use cases.

MongoDB Atlas is a managed cloud database service that offers the flexibility and scalability of MongoDB. It supports a variety of use cases and provides automated backups and other security features.


MongoDB Atlas enables IT teams to deploy right-sized applications without sacrificing performance or availability. Using sharding, it eliminates hardware bottlenecks by distributing data across multiple servers. This feature reduces the need for a data center and allows IT teams to scale up or down in minutes, without application downtime.

It also automates key tasks during provisioning and configuration, maintenance, and disaster recovery processes to save time and effort. However, it lacks features that other services provide, such as search engines and caching. This could be a disadvantage for teams that require these features to build their applications.

Atlas has an operating system throttling limit that limits the number of operations per second for each connection to its cluster. If the rate of operations exceeds this limit, Atlas queues the transactions for a second before resuming the operation. This might cause some latency in your applications.


MongoDB offers robust security features to protect data in-transit, at rest and in-use. These include granular auditing, encryption at rest, and role-based access controls. It also provides a range of security-as-a-service capabilities, such as point-in-time backups and data masking.

Users can choose to store data on dedicated MongoDB Atlas systems, which are deployed in an Amazon Virtual Private Cloud and protected by a firewall. These dedicated systems are compliant with FedRAMP, a U.S. government program for standardized assessment, authorization and continuous monitoring of cloud products and services.

The Atlas platform is built to handle the complexities of multi-model data integration. It also supports aggregation frameworks and rich document types, which can reduce the need for third-party data integration tools like ETL and MDM. This can simplify application code and speed up deployments. However, it does come at a cost: additional infrastructure resources, operational complexity, and governance challenges. Customers can also opt to subscribe to the MongoDB Stitch service, which integrates data services and fine-grained data access policies with Atlas, but this adds an extra consumption-based cost resulting in higher TCO.


MongoDB offers a variety of scalability options to meet the performance requirements of most applications. For example, it can scale up to multiple instances based on throughput needs. It also supports the use of sharding, which distributes data across multiple nodes.

This means that your application will not have to wait for all the documents in a shard to be read or written before it can perform a query. Additionally, it can perform multiple transactions at once. However, this does come with a cost, and it is important to understand the trade-offs when using this feature.

In addition, Atlas supports a wide range of locales and collations. It also provides a feature called index suggestions that suggests which indexes to build for a collection. This feature helps to reduce the number of unnecessary indexes, which can improve performance by reducing the amount of data that has to be read or written. However, the index suggestion feature is only available on M10 cluster tiers and higher.


MongoDB Atlas is a fully-managed database-as-a-service (DBaaS). It takes care of deploying, managing and healing your deployment on the cloud service provider of your choice. It also provides advanced capabilities like sharding.

This allows you to focus on your application, without worrying about capacity planning, sharding strategy, multi-region expansion challenges and data model flexibility. However, a drawback is that this approach removes some flexibility from your deployment.

To use MongoDB Atlas, you need to create an organization and a project. Once you have these, you can deploy a database cluster.

After creating a cluster, you can connect to it from your applications using the MongoDB Atlas Management Console. You can find a list of your clusters and serverless instances through the Database Deployment Details view. Each cluster has a distinct configuration that defines how it is set up. Some configurations have to be planned ahead of time, while others can be changed on the fly.

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