SRE Toil: What It Is, How to Measure It, and How to Reduce It

Toil is the manual, repetitive, automatable work that keeps systems running without actually improving them. SRE practice says: measure toil, set a ceiling, and invest engineering effort in eliminating it.

Google's SRE book introduced the concept of toil as a specific category of work worth tracking and actively reducing. The definition is precise: toil is work that is manual, repetitive, automatable, tactical (it doesn't produce lasting value), and that scales linearly with service growth.

The reason to track it separately from other work: teams that spend too much time on toil have no capacity for the reliability improvements that would eliminate toil. They are on a treadmill. The SRE principle is a ceiling on toil — no more than 50% of an SRE's time — with explicit investment in automation to reduce it.

Recognizing toil

The examples are recognizable to anyone who has operated production infrastructure:

  • Manually responding to pager alerts that could be handled by an automated recovery script
  • Running a database query by hand to answer a recurring business question that could be a scheduled report
  • Manually approving deployments that could be automated with sufficient testing
  • Manually copying data between systems that could be connected with a pipeline
  • Restarting a service on a schedule because it has a known memory leak (the leak is tech debt; the restart is toil)
  • Manually granting access to a system for new team members who always need the same access (an automation problem, not a policy problem)

The common thread: someone doing this work is doing it because no automation exists, not because human judgment is required.

Measuring toil

You cannot reduce what you do not measure. Toil measurement approaches:

Incident-linked toil: when responding to a PagerDuty/OpsGenie alert, mark whether the response was manual and repetitive (toil) or required investigation and judgment (not toil). Over time, you can see the toil fraction of on-call work.

Time tracking for ops work: a week-long exercise where the team tracks their time in categories: toil, project work, meetings, unplanned work. The granularity doesn't need to be high — 30-minute buckets are sufficient. This produces a surprisingly clear picture of where time actually goes.

Ticket analysis: tag support and operational tickets as toil vs. not. Review the toil tickets monthly. What categories of toil appear repeatedly? These are the automation candidates.

A team discovering that 40% of their time is toil is not discovering a problem unique to them — it is discovering a baseline that exists broadly. The question is what to do about it.

Prioritizing what to automate

Not all toil is equally worth automating. The priority framework:

High-frequency + manual + high-consequence: a manual step in a deployment that is done 20 times per week and occasionally causes incidents is the highest priority for automation. The frequency means the automation saves significant time; the incident rate means it reduces risk.

On-call toil: manual alert responses that happen at 2am have both a reliability cost (delayed response) and a human cost (degraded sleep, on-call burnout). Automating these has disproportionate value compared to the time saved during business hours.

Toil that blocks other teams: a manual approval step that other teams wait on for hours each time is a bottleneck. Automating it removes a dependency.

Scaling toil: work that increases linearly with service growth will eventually consume the team. Automating it before it reaches that point is cheaper than automating under pressure.

The investment conversation

Reducing toil requires trading short-term capacity for long-term capacity. The team needs to spend time building automation instead of doing the manual work. This is a conversation with leadership: we are spending 35% of our engineering capacity on work that doesn't improve the system, and we need 2 sprints to build automation that will reduce this to 15%.

The ROI argument helps: if 2 engineers spend 1 sprint building an automation that saves 4 hours per week across 3 engineers, the payback is within 2 months. The argument is more convincing with actual measurements than with intuitions.

Working through a toil reduction initiative for an operations or SRE team? The measurement step is often the most revealing part. Happy to compare approaches.

Working on this in production?

We do this work directly alongside engineering teams — architecture review, migration, and hands-on enablement.