# Lack of Graviton Usage in Databricks Clusters

Canonical: https://www.pointfive.co/efficiency-hub/inefficiencies/lack-of-graviton-usage-in-databricks-clusters

[Cloud Efficiency Hub](https://www.pointfive.co/efficiency-hub) 

# Lack of Graviton Usage in Databricks Clusters

## Explanation

Databricks supports AWS Graviton-based instances for most workloads, including Spark jobs, data engineering pipelines, and interactive notebooks. These instances offer significant cost advantages over traditional x86-based VMs, with comparable or better performance in many cases. When teams default to legacy instance types, they miss an easy opportunity to reduce compute spend. Unless workloads have known compatibility issues or specialized requirements, Graviton should be the default instance family used in Databricks Clusters.

## Relevant Billing Model

Databricks compute is billed based on:

- Databricks Units (DBUs): Determined by cluster configuration, including instance type

- AWS Infrastructure Charges: Graviton-based instances (e.g., m6g, r6g, c6g) are generally priced lower than their x86 equivalents, billed per second or minute

Choosing x86-based instances when Graviton would suffice leads to higher infrastructure costs for equivalent performance.

## Detection

- Query cluster configurations to identify use of non-Graviton (x86-based) instance types

- Check for absence of Graviton enforcement in workspace-level compute policies

- Review workload compatibility for clusters running frequently on x86

- Engage with teams to confirm whether Graviton instances have been tested or benchmarked

## Remediation

- Monitor utilized instance types and recommend Graviton-based families

- Reconfigure default cluster templates to use Graviton by default

- Allow exceptions only for workloads with documented compatibility or performance issues

## Relevant Documentation

- [Databricks Support for Graviton Instances](https://www.databricks.com/blog/2022/04/18/announcing-databricks-support-for-aws-graviton2-with-up-to-3x-better-price-performance.html)

- [AWS Graviton Overview](https://aws.amazon.com/ec2/graviton/)

[Submit Feedback](https://hub.pointfive.co/inefficiencies/lack-of-graviton-usage-in-databricks-clusters)

## At a glance

Reference

CER-0116

Cloud provider

Databricks

Service

Databricks Clusters

Category

[Compute](https://www.pointfive.co/efficiency-hub/service-category/compute)

Inefficiency type

Suboptimal Instance Selection

## Contributed by

- ### Matt Weingarten

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Source: the public page above. Product screenshots and illustrative interfaces are examples, not live customer data.