Azure HDInsight pricing

HDInsight deploys a different number of nodes for each cluster type. Within a given cluster type there are different roles for the various nodes, which allows a customer to size those nodes in a given role appropriate to the details of their workload. For example, a Hadoop cluster can have its worker nodes provisioned with a large amount of memory if the analytics being performed are memory intensive.

Hadoop clusters for HDInsight are deployed with two roles:

• Head node (2 nodes)
• Data node (at least 1 node)

HBase clusters for HDInsight are deployed with three roles:

• Head servers (2 nodes)
• Region servers (at least 1 node)
• Master/Zookeeper nodes (3 nodes)

Storm clusters for HDInsight are deployed with three roles:

• Nimbus nodes (2 nodes)
• Supervisor servers (at least 1 node)
• Zookeeper nodes (3 nodes)

Spark clusters for HDInsight are deployed with three roles:

• Head node (2 nodes)
• Worker node (at least 1 node)
• Zookeeper nodes (3 nodes) (free for A1 zookeepers)

The use of R-Server will incur one edge node in addition to the cluster deployment architecture.

We charge for the number of minutes your cluster is running, rounded to the nearest minute, not hour.

If you run a cluster for 100 hours in US East with two D13 v2 head nodes and three D12 v2 data nodes, the billing would be the following in the two scenarios:

• On a Standard HDInsight cluster - 100 hours x (2 x $-/hour + 3 x $-/hour) = $-
• On a Standard HDInsight cluster with Enterprise Security Package - 100 hours x (2 x $-/hour + 3 x $-/hour) + 100 hours x (2 x 8 + 3 x 4 + 3 x 2) x $-/core-hour = $-

In order to stop an HDInsight cluster, you must delete the cluster. By default, permanent data generated from HDInsight cluster should be written to Azure Blob Storage, Azure Data Lake Storage Gen1 or Azure Data Lake Storage Gen2 so the data is not affected by the deletion. If you want to preserve your Hive metadata (tables and schemas), you should provision a cluster with an external metadata store. You can find more details in this documentation.

There are a number of options to reduce the costs:

• Drive higher utilisation of your existing clusters.
  1. Delete clusters while not in use. For more information about deleting a cluster, see Delete an HDInsight cluster using your browser, PowerShell or the Azure CLI
  2. Scale down. For more information about manually scaling clusters, see Scale HDInsight clusters
  3. Enable Autoscale. For information about auto-scaling clusters, see Automatically scale Azure HDInsight clusters (preview)
• Deploy the clusters with lower cost. This includes proper planning on how many nodes to use, which type of node to use for head nodes and worker nodes, and which region to launch the cluster as HDInsight offers many different node types to deploy to, with a range of pricing options. Review the Base price/node-hour section of this article for pricing, and for more information, see Capacity planning for HDInsight clusters

The number of data nodes will vary depending on your needs. With the elasticity available in Azure cloud services, you can try a variety of cluster sizes to determine your own optimal mix of performance and cost, and only pay for what you use at any given time. Clusters can also be scaled on demand to grow and shrink to match the requirements of your workload.

Each subscription has a default limit on how many HDInsight data nodes can be created. If you need to create a larger HDInsight cluster or multiple HDInsight clusters that together exceed your current subscription maximum, you can request for your subscription’s billing limits to be increased. Please open a support ticket with Support Type = Billing. Depending on the maximum nodes per subscription that you request, you may be asked for additional information that will allow us to optimise your deployment(s).

To estimate the cost of clusters of various sizes, try the Azure Calculator.