# Overuse of Photon in non-production workloads Canonical: https://www.pointfive.co/efficiency-hub/inefficiencies/overuse-of-photon-in-non-production-workloads Photon is frequently enabled by default across Databricks workspaces, including for development, testing, and low-concurrency workloads. By: Nicole Boyd Updated: 2026-09-28 [Cloud Efficiency Hub](https://www.pointfive.co/efficiency-hub) # Overuse of Photon in Non-Production Workloads The short version Photon is frequently enabled by default across Databricks workspaces, including for development, testing, and low-concurrency workloads. [Nicole Boyd](https://www.linkedin.com/in/nicolecboyd/) Managing Consultant - FinOps @ Capgemini Invent Databricks service [Databricks Compute](https://www.pointfive.co/efficiency-hub/cloud-services/databricks-compute) Category [Compute](https://www.pointfive.co/efficiency-hub/service-category/compute) Reference CER-0195 Type Inefficient Configuration ## Explanation Why the waste happens and who it affects. In these non-production contexts, job runtimes are typically shorter, SLAs are relaxed or nonexistent, and performance gains offer little business value. Enabling Photon in these environments can inflate DBU costs substantially without meaningful runtime improvements. By not differentiating cluster configurations between production and non-production, organizations may pay a premium for workloads that could run just as efficiently on standard compute. Cluster policies can be used to restrict Photon usage to explicitly tagged production workloads, helping enforce cost-conscious defaults and reduce unnecessary spend. ## Billing model The pricing dimensions that drive this cost. Photon-enabled compute clusters incur a higher Databricks Unit (DBU) rate compared to standard compute. While the performance boost can be significant for complex workloads, the DBU multiplier - which varies by cloud, instance type, and compute type - may not be justified in development, staging, or ad-hoc environments where performance is less critical. This results in unnecessary cost when Photon is enabled for non-production workloads that don't benefit from its capabilities. ## How to detect 5 checks to find it in your estate. - Identify compute clusters or job configurations with Photon enabled in dev, test, or sandbox environments - Review naming conventions, tags, or workspace metadata to isolate non-production workload - Analyze job characteristics: short runtimes, low concurrency, small data volumes, or non-SLA workloads using Photon - Compare cost and runtime of similar jobs executed with and without Photon enabled - Review cluster policies and workspace defaults to determine whether Photon is disabled by default for non-prod ## How to fix 5 ways to remove the waste. - Disable Photon by default in dev/test environments using workspace settings or cluster policies - Create separate cluster templates or policies for production and non-production workloads - Use tagging or automation to flag or block Photon usage in low-priority environments - Educate teams on the cost-performance tradeoffs of Photon and when its use is justified - Periodically review Photon usage by environment and update governance rules accordingly ## Documentation Vendor references for pricing and configuration. - [Databricks Photon Overview docs.databricks.com](https://docs.databricks.com/aws/en/compute/photon) - [Databricks Pricing Calculator databricks.com](https://www.databricks.com/product/pricing/product-pricing/instance-types) ## Related inefficiencies [Browse the library](https://www.pointfive.co/efficiency-hub) - Databricks Compute CER-0189 ### [Lack of Workload-Specific Cluster Segmentation](https://www.pointfive.co/efficiency-hub/inefficiencies/lack-of-workload-specific-cluster-segmentation) Running varied workload types (e.g., ETL pipelines, ML training, SQL dashboards) on the same cluster introduces inefficiencies. Each workload has different runtime characteristics, scaling needs, and performance sensitivities. When mixed... Compute - Databricks Compute CER-0222 ### [Poorly Configured Autoscaling on Databricks Clusters](https://www.pointfive.co/efficiency-hub/inefficiencies/poorly-configured-autoscaling-on-databricks-clusters) Autoscaling is a core mechanism for aligning compute supply with workload demand, yet it's often underutilized or misconfigured. In older clusters or ad-hoc environments, autoscaling may be disabled by default or set with tight min/max... Compute - Databricks Compute CER-0523 ### [Idle Instances Retained in Databricks Instance Pools](https://www.pointfive.co/efficiency-hub/inefficiencies/idle-instances-retained-in-databricks-instance-pools) Databricks instance pools keep a set of ready cloud instances so classic clusters can start and scale faster. While an instance sits idle in a pool, Databricks charges no DBUs for it, but the cloud provider still bills the virtual machine.... Compute --- Source: the public page above. Product screenshots and illustrative interfaces are examples, not live customer data.