Tracer Documentation
Ready to get started?
Step 1: Install Tracer
Go to our onboarding to get your own personal token
Explore Tracer by features
Understands Scientific Pipelines Telemetry by pipeline, run, step, tool. | Full, Real-Time Pipeline Overview See every pipeline run live, in full detail. | Real-Time Cost & Resource Tracking Shows metrics and storage per tool or sample. | ||||||
See Which Tools Are Running Instantly see every binary, script, or container. | Automatic Logging (Even Without Logs) Tracer generates timelines, even without logs. | Debugging & Root-Cause Insights Correlates issues with resources. | ||||||
| Find out all our features in our technology section. | ||||||||
Get answers in your environment
Tracer supports a wide array of environments to fit your infrastructure and workflow needs.Your environment is not shown here? Check out all our environments.
For all your frameworks
Tracer is compatible with all frameworks, so whatever you use, Tracer can be used to monitor your workflows.Your framework is not shown here? Check out all our integrations / frameworks.
Tutorials
Use Tracer as efficiently as possible. In these tutorials, we guide you through the most common use cases of Tracer.Or dive deeper into our groundbreaking technology:How It Works
High-level overview.
High-level overview.
Revolutionary Linux kernel tech.
Architecture and capabilities
Comparisons
Understand how Tracer compares to your existing workflow stack.
Seqera
Observability for Nextflow.
Prefect
Just beyond orchestration.
Dagster
Scheduled runs visibility.
Grafana
Customizable observability.
To see all analyses, go to Comparisons.
Key Use Cases
Deep Observability for Scientific Pipelines
Monitor pipelines end-to-end.
Team-Wide Observability and Collaboration
Beyond individual pipelines.
Cost and Resource Optimization
Reduce cloud compute costs.
Debugging and Failure Analysis
Accellerated through data.
Mini History
Tracer was founded in 2023 by Vincent Hus and Laura Bogaert to transform how scientists understand and manage computational workloads.Frustrated by legacy tools, broken pipelines, and infrastructure bottlenecks, they built the world’s first observability platform purpose-built for scientific computing.