# Performance-Optimized Serverless Mode for Non-Time-Sensitive Databricks Jobs Canonical: https://www.pointfive.co/efficiency-hub/inefficiencies/performance-optimized-serverless-mode-for-non-time-sensitive-databricks-jobs Serverless Lakeflow Jobs and Lakeflow pipelines can run in one of two performance modes. By: PointFive Updated: 2026-09-28 [Cloud Efficiency Hub](https://www.pointfive.co/efficiency-hub) The short version Serverless Lakeflow Jobs and Lakeflow pipelines can run in one of two performance modes. PointFive Research Cloud cost research at PointFive Databricks service [Databricks Serverless Compute](https://www.pointfive.co/efficiency-hub/cloud-services/databricks-serverless-compute) Category [Compute](https://www.pointfive.co/efficiency-hub/service-category/compute) Reference CER-0527 Type Suboptimal Tier or SKU ## Explanation Why the waste happens and who it affects. Performance-optimized mode, the default, keeps warm compute ready so work starts and runs faster. Standard mode uses less compute and starts within about four to six minutes, and Databricks states that it can reduce costs by up to 70% compared to performance-optimized mode. Both modes bill under the same SKU; standard mode simply consumes fewer DBUs for the same work. Because performance-optimized is the default, scheduled batch jobs and triggered pipelines often run in it without anyone having chosen it: nightly ETL, hourly aggregations and backfills whose deadlines are measured in hours pay for fast start-up they do not use. Databricks' serverless best practices say that for scheduled jobs where startup latency is not critical, standard mode typically offers the best value. Standard mode is not available for notebooks, and interactive or tightly scheduled time-critical workloads are legitimate reasons to stay on performance-optimized. ## Billing model The pricing dimensions that drive this cost. Serverless DBUs Serverless jobs and pipelines are billed in DBUs for the compute used, with infrastructure included in the DBU price Performance-optimized mode The default mode; faster startup and execution backed by warm compute, consuming more DBUs for the same work Standard mode Same SKU but fewer DBUs, with startup typically within 4 to 6 minutes; available for Lakeflow Jobs and Lakeflow pipelines, not notebooks performance\_target attribute Recorded in system.billing.usage product\_features as PERFORMANCE\_OPTIMIZED or STANDARD for each usage record ## How to detect 4 checks to find it in your estate. - Query system.billing.usage where product\_features.is\_serverless = true and product\_features.performance\_target = 'PERFORMANCE\_OPTIMIZED', grouped by usage\_metadata.job\_id and usage\_metadata.dlt\_pipeline\_id, to find the jobs and pipelines consuming the most DBUs in performance-optimized mode - Map those job and pipeline IDs to their schedules and triggers and flag the ones that run on a cron schedule or file-arrival trigger with no tight completion deadline - Review job settings in the Jobs UI (the Performance optimized toggle on the job details page) or the job definition's performance\_target field in the Jobs API or bundle configuration - For triggered serverless pipelines, check the Performance optimized setting in the pipeline scheduler; for continuous pipelines, check whether they run under a continuous job ## How to fix 5 ways to remove the waste. - Clear the Performance optimized setting (or set performance\_target to STANDARD in the job or bundle definition) for scheduled batch jobs and triggered pipelines that can tolerate a 4 to 6 minute start - Keep performance-optimized mode for interactive work, jobs chained under tight SLAs, and short jobs where start-up latency dominates run time - For continuous pipelines that should use standard mode, run the pipeline with a continuous job and clear the Performance optimized checkbox in its schedule, as Databricks documents - Compare DBUs per run in system.billing.usage before and after switching to confirm savings and check that run completion times still meet downstream deadlines - Set a team default, for example in bundle templates, so new scheduled jobs start in standard mode unless a latency requirement is documented ## Documentation Vendor references for pricing and configuration. - [Best practices for serverless compute docs.databricks.com](https://docs.databricks.com/aws/en/compute/serverless/best-practices) - [Run your Lakeflow Jobs with serverless compute for workflows docs.databricks.com](https://docs.databricks.com/aws/en/jobs/run-serverless-jobs) - [Configure a serverless pipeline docs.databricks.com](https://docs.databricks.com/aws/en/ldp/serverless) - [Billable usage system table reference docs.databricks.com](https://docs.databricks.com/aws/en/admin/system-tables/billing) - [Databricks Pricing: Flexible Plans for Data and AI Solutions databricks.com](https://www.databricks.com/product/pricing) ## Related inefficiencies [Browse the library](https://www.pointfive.co/efficiency-hub) - Databricks Serverless Compute CER-0182 ### [Underuse of Serverless Compute for Jobs and Notebooks](https://www.pointfive.co/efficiency-hub/inefficiencies/underuse-of-serverless-compute-for-jobs-and-notebooks) Databricks Serverless Compute is now available for jobs and notebooks, offering a simplified, autoscaled compute environment that eliminates cluster provisioning, reduces idle overhead, and removes the need to manage instance types. For... Compute - Databricks Clusters CER-0116 ### [Lack of Graviton Usage in Databricks Clusters](https://www.pointfive.co/efficiency-hub/inefficiencies/lack-of-graviton-usage-in-databricks-clusters) 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... Compute - Databricks Workflows CER-0114 ### [Inefficient Use of Job Clusters in Databricks Workflows](https://www.pointfive.co/efficiency-hub/inefficiencies/inefficient-use-of-job-clusters-in-databricks-workflows) When multiple tasks within a workflow are executed on separate job clusters - despite having similar compute requirements - organizations incur unnecessary overhead. Each cluster must initialize independently, adding latency and cost. This... Compute --- Source: the public page above. 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