# Continuous Mode on Databricks Lakeflow Pipelines Without a Latency Requirement Canonical: https://www.pointfive.co/efficiency-hub/inefficiencies/continuous-mode-on-databricks-lakeflow-pipelines-without-a-latency-requirement Lakeflow pipelines (the declarative pipelines product that appears as DLT in Databricks billing data) run in one of two modes. By: PointFive Updated: 2026-09-28 [Cloud Efficiency Hub](https://www.pointfive.co/efficiency-hub) The short version Lakeflow pipelines (the declarative pipelines product that appears as DLT in Databricks billing data) run in one of two modes. PointFive Research Cloud cost research at PointFive Databricks service [Databricks Lakeflow Pipelines](https://www.pointfive.co/efficiency-hub/cloud-services/databricks-lakeflow-pipelines) Category [Compute](https://www.pointfive.co/efficiency-hub/service-category/compute) Reference CER-0526 Type Inefficient Configuration ## Explanation Why the waste happens and who it affects. In triggered mode, the default, an update refreshes all tables from the data available when it starts and then stops. In continuous mode, the pipeline keeps processing new data as it arrives. Databricks' pipeline-mode documentation states the cost difference directly: triggered pipelines reduce resource consumption and expense because the cluster runs only long enough to update the pipeline, while continuous pipelines require an always-running cluster, which is more expensive. Pipelines are switched to continuous for convenience, during development, or because a source is a stream, even when every consumer of the output tables reads them on an hourly or daily schedule. Once set, the pipeline's compute runs 24x7 whether or not any consumer needs that freshness. This applies to pipelines on both classic and serverless compute; Databricks' broader cost guidance to balance always-on and triggered streaming makes the same point for streaming workloads generally. ## Billing model The pricing dimensions that drive this cost. Pipeline compute Billed in DBUs for as long as the pipeline's compute runs, plus cloud instance charges on classic compute; the product tier (dlt\_tier CORE, PRO or ADVANCED) sets the classic DBU rate Triggered mode Compute runs only for the duration of each update, so cost follows the update schedule Continuous mode Compute stays up between arrivals of new data, so the pipeline bills around the clock ## How to detect 4 checks to find it in your estate. - Query the latest record per pipeline in system.lakeflow.pipelines (Public Preview) for settings.continuous = true and settings.development flags, and list the owners - Join to system.billing.usage where billing\_origin\_product = 'DLT' by usage\_metadata.dlt\_pipeline\_id to confirm 24x7 DBU consumption and rank by cost - For each continuous pipeline, check how its target tables are consumed - dashboard refresh schedules, downstream jobs, query history on the tables - and flag pipelines whose consumers read on an hourly or longer cadence - Review the Pipeline mode setting in the pipeline UI or the continuous field in pipeline JSON and bundle definitions to find pipelines set to continuous without a documented latency requirement ## How to fix 5 ways to remove the waste. - Switch batch-tolerant pipelines to Triggered in the Pipeline mode setting and schedule updates with a job at the freshness consumers need; streaming tables still process only new data incrementally on each triggered update - For serverless triggered pipelines, also consider clearing Performance optimized in the schedule to use standard performance mode where a start of about four to six minutes is acceptable - Where continuous processing is truly required, follow Databricks' recommendation to run the pipeline with a continuous job rather than the pipeline's built-in continuous mode, which enables serverless performance modes - For continuous pipelines that stay on, set pipelines.trigger.interval on individual flows to process less frequently where near-real-time is not needed - Record the freshness requirement on each pipeline and revisit continuous pipelines when their consumers change ## Documentation Vendor references for pricing and configuration. - [Triggered vs. continuous pipeline mode docs.databricks.com](https://docs.databricks.com/aws/en/ldp/pipeline-mode) - [Configure a serverless pipeline docs.databricks.com](https://docs.databricks.com/aws/en/ldp/serverless) - [Best practices for cost optimization docs.databricks.com](https://docs.databricks.com/aws/en/lakehouse-architecture/cost-optimization/best-practices) - [Jobs system table reference docs.databricks.com](https://docs.databricks.com/aws/en/admin/system-tables/jobs) - [Billable usage system table reference docs.databricks.com](https://docs.databricks.com/aws/en/admin/system-tables/billing) - [Lakeflow Pipelines databricks.com](https://www.databricks.com/product/pricing/lakeflow-pipelines) ## Related inefficiencies [Browse the library](https://www.pointfive.co/efficiency-hub) - 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 - Databricks SQL CER-0225 ### [Underuse of Serverless for Short or Interactive Workloads](https://www.pointfive.co/efficiency-hub/inefficiencies/underuse-of-serverless-for-short-or-interactive-workloads) Many organizations continue running short-lived or low-intensity SQL workloads - such as dashboards, exploratory queries, and BI tool integrations - on traditional clusters. This leads to idle compute, overprovisioning, and high baseline... Compute - 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 --- Source: the public page above. Product screenshots and illustrative interfaces are examples, not live customer data.