5 Ways to Reduce Costs With MongoDB Atlas
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Now more than ever, businesses are looking for ways to reduce or eliminate costs wherever possible. As a cloud service, MongoDB Atlas is a platform that enables enhanced scalability and reduces dependence on the kind of fixed costs businesses experience when they deploy on premises instances of MongoDB. This article will help you understand ways you can reduce costs with your MongoDB Atlas deployment.
Pausing a cluster essentially brings the cluster down so if you still have active applications depending on this cluster, it's probably not a good idea. However, pausing the cluster leaves the infrastructure and data in place so that it's available when you're ready to return to business. You can pause a cluster for up to 30 days but if you do not resume the cluster within 30 days, Atlas automatically resumes the cluster. Clusters that have been paused are billed at a different, lower rate than active clusters. Read more about
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MongoDB Atlas was designed with scalability in mind and while scaling down is probably the last thing on our minds as we prepare for launching a Startup or a new application, it's a reality that we must all face.
Fortunately, the engineers at MongoDB that created MongoDB Atlas, our online database as a service, created the solution with bidirectional scalability in mind. The process of scaling a MongoDB Cluster will change the underlying infrastructure associated with the hosts on which your database resides. Scaling up to larger nodes in a cluster is the very same process as scaling down to smaller clusters.
Another great feature of MongoDB Atlas is the ability to programmatically control the size of your cluster based on its use. MongoDB Atlas offers scalability of various components of the platform including Disk, and Compute. With
, you have the ability to configure your cluster with a maximum and minimum cluster size. You can enable compute auto-scaling through either the UI or the
. Auto-scaling is available on all clusters M10 and higher on Azure and GCP, and on all "General" class clusters M10 and higher on AWS. To enable auto-scaling from the UI, select the Auto-scale "Cluster tier" option, and choose a maximum cluster size from the available options.
Atlas analyzes the following cluster metrics to determine when to scale a cluster, and whether to scale the cluster tier up or down:
- CPU Utilization
- Memory Utilization
Once you configure auto-scaling with both a minimum and a maximum cluster size, Atlas checks that the cluster would not be in a tier outside of your specified Cluster Size range. If the next lowest cluster tier is within your Minimum Cluster Size range, Atlas scales the cluster down to the next lowest tier if both of the following are true:
- The average CPU Utilization and Memory Utilization over the past 72 hours is below 50%, and
- The cluster has not been scaled down (manually or automatically) in the past 72 hours.
You may also be leveraging old datasets that you no longer need. Conduct a thorough analysis of your clusters, databases, and collections to remove any duplicates, and old, outdated data. Also, remove sample datasets if you're not using them. Many developers will load these to explore and then leave them.
As a last resort, you may want to remove your cluster by terminating it. Please be aware that terminating a cluster is a destructive operation -once you terminate a cluster, it is gone. If you want to get your data back online and available, you will need to restore it from a backup. You can restore backups from
Be sure you download and secure your backups before terminating as you will no longer have access to them once you terminate.
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