Total Cost of Ownership Comparison

Looking to optimize the cost of your big data platform and find the best price? Consider the following factors:

  • Cloud is almost always more cost efficient than on premise deployment
  • How you deploy in the cloud can make a big difference in your total cost of ownership
  • Cloud-native features can make a dramatic difference in TCO

Three reasons why big data in the cloud is more cost efficient than on-premise?

  • Pay for what you use vs Pay for peak workload
  • Opex vs Capex
  • Lower DevOps / DataOps Cost

Cloud vs On Premises TCO

On premise deployments typically have higher TCO because you have to provision your infrastructure to accommodate your peak workload. This can lead to significant waste due to unutilized infrastructure. The yellow area below represents idle infrastructure that you paid for but aren’t currently using.

On Premises (Cloudera, Hortonworks, MapR)


Cloud (Cloudera Altus, Amazon EMR, HDInsights, Databricks, Qubole)


Infrastructure (hardware/software)Upfront hardware/software CapEx plus ongoing operations OpExPay as you use OpEx
DevOps / DataOpsIn-house expertise or external consultants required for deployment and operationsDataOps expertise required for operations only
Upgrades / ExpansionSame as deploymentElastic
Deployment timeSlowInstant
Pricing ModelHigh CapEx plus high OpExPay as you use Opex

TCO for cloud based big data is typically at least 19% lower than on premise and can be as high 90%.

Consider your deployment model

There are a number of ways to deploy big data in the cloud: Static “always on” clusters, big-data-as-a-service and “serverless” systems are the most popular. The deployment model can have a big impact on TCO. Static clusters reduce the upfront CapEx costs and time-to-market but fail to take full advantage of the elasticity benefits of the cloud. Cluster lifecycle management is one way to leverage the cloud to reduce TCO. Serverless systems offer great flexibility and lower administrative overhead but they compromise on capacity and control. They’re best used for smaller ad hoc workloads.

There are two ways to benefit from the elasticity of the cloud. Both involve optimizing the use of compute because in the cloud compute and storage are de-coupled and compute is expensive while storage is cheap.

  • Cluster lifecycle management makes clusters ephemeral – they get created when needed and terminated when not in use, automatically. By eliminating compute costs when no workloads are running, you can reduce TCO substantially (see diagram, below)
  • Auto-Scaling can add nodes to a running cluster as they needed. Big data workloads can be “bursty” so a statically sized cluster can create lots of wasted compute time. With auto-scaling, you can dynamically add nodes to a running cluster to increase capacity as needed. Some solutions can auto-scale down as well – removing idle nodes when possible to reduce TCO even further. 
Static clusters in the cloud (Apache, Cloudera, Hortonworks)Big-data-as-a-service (Qubole, Databricks, EMR, HDInsights)Serverless (Athena, BigQuery)
Cluster lifecycle managementNoYesN/A

TCO for big data-as-a-service is typically at least 48% lower than other cloud deployment models and can be as high 90%.

Cloud-native Optimizations

Just because you deploy in the cloud doesn’t automatically mean you are taking maximum advantage of cloud and optimizing TCO.

Cluster lifecycle management and auto-scaling improve TCO by reducing the amount of idle (wasted) infrastructure.

Get started with Qubole today.