Monitor and optimize GitHub Actions costs
GitHub Actions cost monitoring shows where CI time and modeled spend accumulate, without granting Pipetrics access to your application source code. Then connect your coding agent through MCP to investigate changes using repository context you control.
GitHub Actions cost monitoring
Attribute usage and modeled cost to repositories, workflows, jobs, runners, outcomes, and billing categories.
Reduce GitHub Actions costs
Prioritize unnecessary runs, repeated failures, cache misses, matrices, concurrency, and runner choices.
Find slow GitHub Actions
Compare median and p95 duration, queue delays, jobs, steps, reruns, and commit-associated regressions.
Find the waste. Fix the workflow.
GitHub Actions cost monitoring connects a repository-level signal to the workflow, job, and step behind it. From there, decide what to change.
Ask your coding agent
Pipetrics MCP brings measured runtime, queue, failure, retry, and cost evidence into Codex, Claude Code, OpenCode, and compatible clients.
Learn moreSee where spend goes
Filter Cost Profiler by repository, branch, runner, trigger, conclusion, or billing category.
Learn moreSee minutes at a glance
A workflow treemap shows which pipelines consume the largest share of runner minutes, so you know where to investigate first.
Learn moreTrace bottlenecks
Compare average, median, and p95 duration, then drill into retries, outcomes, jobs, and the slowest steps.
Learn moreKeep source code private
No application source-code access required. Pipetrics uses read-only Actions and supporting metadata permissions; your coding agent controls its own repository access.
Learn moreShare the signal
Send workflow outcomes to Slack or bring Pipetrics metrics into your existing Grafana dashboards.
Your CI evidence. Your coding agent.
Use Pipetrics MCP for code-aware GitHub Actions optimization without granting Pipetrics access to your application source code. Your agent can inspect the repository using its own permissions.
CI execution context
GitHub Actions telemetry → Pipetrics → MCP → your coding agent
Durations, queue delays, outcomes, reruns, workflow paths, and modeled cost.
Repository context
Your repository → your coding agent, under permissions you choose
Workflow YAML and application files are read by your tool, not supplied to Pipetrics through MCP.
Start with a question.
Copy a prompt into your connected coding agent.
- Find the workload
Which workflows consume the most runner time?
- Investigate failures
Where are failures and reruns wasting time?
- Plan a change
Inspect this workflow and propose one safe change.
Pricing
$0/mo
- unlimited users
- 14 days data retention
- cost profiler(current month)
Coming in 2026
- weekly summary
$10/repo/mo
- unlimited users
- 360 days data retention
- cost profiler (full history)
- grafana integration
- slack notifications
Coming in 2026
- weekly summary
- budget alerts
- automated optimization suggestions
custom price per repo
- unlimited users
- unlimited data retention
- grafana integration
- talk with experts
Coming in 2026
- weekly summary
- budget alerts
- automated optimization suggestions
- GitHub App for GitHub Enterprise Server
- Bring-Your-Own-Database (BYOD)
- GitHub log archive
Follow the evidence
Start with the finding that matters, then drill into the workflow data behind it.
The treemap shows each workflow's share of runner minutes, not billed cost.
Cost Profiler breaks modeled usage down from repositories to workflows and jobs.
Filter Cost Profiler to failures to see where failed modeled cost accumulates.
See execution counts by trigger event before changing workflow configuration.
Inspect job runtime share, duration statistics, modeled cost, and top steps.
Compare average and p95 duration on a workflow trend chart.
About us
Jakub Różycki
DevOps Engineer and Frontend Development Enthusiast with over 8 years of experience in infrastructure and automation. Started as a Linux Administrator, working with bare metal servers and automating systems using Ansible. Later transitioned into cloud technologies, with hands-on experience across GCP, AWS, and Azure. Currently focused on developer tooling and automation - especially around GitHub Actions and GitLab CI/CD - to help teams move faster and deploy with confidence. Works primarily in the TypeScript ecosystem, with solid experience in Python as well.
Piotr Piwowar
DevOps Engineer with over 5 years of experience designing and operating cloud-native and hybrid infrastructure. Started out building scalable systems for one of Europe’s largest push notification platforms, working extensively with AWS, Azure, and OVHCloud. Comfortable across both cloud and bare-metal environments, with a strong focus on automation, reliability, and performance. Experienced collaborating with engineering teams using Python, Go, Rust, and Java to deliver modern, efficient DevOps solutions.