Databricks costs consist of two main parts: the Databricks Units (DBUs) charge and the cloud infrastructure costs (such as compute instances, storage, and networking) from the cloud provider like AWS, Azure, or GCP. In general, the total Databricks cost is higher than just the pure infrastructure cost because it includes the platform, orchestration, collaborative features, and added functionality like notebooks and ML tools.

Key points regarding cost comparison:

Databricks DBU charges are a premium on top of cloud infrastructure hourly costs. For example, an AWS m5.xlarge instance might cost around $0.3795/hr in infrastructure plus a DBU cost of 0.69 DBUs per hour, totaling roughly $1.07/hr.
Higher-end instance types with more memory or GPUs see higher DBU and infra costs, e.g., an r5.4xlarge instance costs about $5.58/hr combining DBU and infra.
Infrastructure costs alone include VM costs, storage, and data transfer and vary by instance type, region, and cloud provider, which can be controlled somewhat by reserved instances or spot pricing.
Databricks pricing tiers (Standard, Premium, Enterprise) add layers of pricing mainly around features and governance.
The combined cost for Databricks is roughly 2-3 times the raw infrastructure cost alone, depending on workload type, cluster configuration, and usage patterns.

In summary, Databricks adds a significant cost premium over just cloud infrastructure due to its managed platform features, orchestration, and productivity tools. The infrastructure portion is a major part but not the full cost. For budgeting, it is important to consider both DBU and cloud provider charges together to understand true cost. Efficient cluster usage, autoscaling, and workload tuning can reduce the overall spend.

This means Databricks is more costly than running just bare infrastructure because you pay for both cloud resources and the Databricks managed services layered above them.finout+2

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