Skip to main content
Workflow orchestration tools define pipeline structure, dependencies, scheduling, retries, and execution state. They determine what should run and when, but they do not observe how tasks behave while they execute. Tracer complements workflow orchestrators by observing execution behavior at runtime. It captures how tasks and subprocesses consume CPU, memory, disk, and network resources, and maps this behavior back to pipeline runs, steps, and workflows.
If you’re new to Tracer or want a high-level mental model, see How Tracer fits in your stack.

​
Supported workflow orchestrators

The integrations below describe how Tracer works with common orchestration frameworks used in scientific, data, and bioinformatics pipelines. Each page focuses on the execution gaps specific to that tool.

Tracer and Apache Airflow

What DAGs don’t show at runtimeTracer observes execution behavior inside Airflow tasks, including subprocesses and external tools.

Tracer and Dagster

Execution insight beneath assets and jobsTracer reveals how resources are consumed during execution of Dagster assets, ops, and jobs.

Tracer and Flyte

Execution insight beneath tasks and workflowsTracer shows how Flyte tasks actually behave at runtime, beyond task state and execution metadata.

Tracer and Prefect

Runtime visibility beneath flow stateTracer captures system-level behavior of Prefect tasks, including work performed outside Python.

Tracer and Seqera

Execution behavior inside Nextflow pipelinesTracer observes what happens inside Nextflow tasks at runtime, beyond scheduling and task state.

​
When Tracer is useful with orchestration tools

Tracer is most useful alongside workflow orchestrators when teams need to:
  • Understand why tasks run slower than expected
  • Distinguish CPU-bound, I/O-bound, and idle execution
  • Diagnose performance issues not visible in task logs
  • Attribute resource usage and cost to specific workflows or runs
Tracer does not replace orchestration. It does not change scheduling, retries, or pipeline definitions. It adds execution-level visibility beneath existing workflows.

​
Where to go next