Pipeline orchestration without deployment, powered by eBPF
No code changes, 1-minute setup
Why Tracer?
Tracer runs at OS level, detecting every run and tool in real time without setups or code changes. Whatever you launch, is tracked, automatically, instantly and accurately.
By operating at the operating system (OS) level,
Tracer automatically adapts to any framework,
any language without code rewrites.By operating at the operating system (OS) level, Tracer automatically adapts to any framework, any language without code rewrites.
Pipelines are expensive, Tracer makes cost
control a superpower, spotting waste instantly
and right-sizing compute for you in just 2 clicks.Pipelines are expensive, Tracer makes cost control a superpower, spotting waste instantly and right-sizing compute for you in just 2 clicks.
Tracer detects hangs, crashes, and silent failures
directly from the operating system, even when logs
stay quiet or traditional orchestrators miss the signal.Tracer detects hangs, crashes, and silent failures directly from the operating system, even when logs stay quiet or traditional orchestrators miss the signal
Pay only for what you use, without upfront
platform fees. Tracer is about 5% of pipeline
compute cost, far cheaper than fixed license platform models.Pay only for what you use, without upfront platform fees. Tracer usually comes out to about 5% of your pipeline's cloud cost, far cheaper than fixed license platform models
SOLUTIONS IN ACTION
Derived from internal benchmarking and real-world testing, each brief highlights measurable results and replicable solutions.
Objective: Quantify impact of idle instance elimination on cloud cost
Method: Observed 1,247 pipeline runs using the Tracer kernel instrumentation
Result: Avg. compute utilization improved by 28.1%, idle hours reduced by 42%
Reproducibility: Scripts and anonymized telemetry dataset available upon request
Objective: Evaluate impact of AI instance recommendations on pipeline cost
Method: Observed 830 Nextflow runs using Tracer kernel instrumentation
Result: Compute spend reduced by 30.4%, utilization improved by 26%
Reproducibility: Scripts and anonymized telemetry dataset available upon request
Objective: Measure cost impact of instance family and region optimization
Method: Conducted controlled tests across 540 AWS Batch pipeline runs
Result: Average cost reduction of 39.7%, peak savings reached 55%
Reproducibility: Configurations and anonymized cost data available upon request
Get Started Now
Tracer is the first pipeline monitoring system purpose-built for high-compute workloads that lives in the OS.
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