Your PR Cycle Time Is Probably Fine (Here's the Real Benchmark)

"What's a good PR cycle time?" is one of the most common questions engineering leaders ask. The answer depends on your team size, industry, and engineering maturity—but having benchmarks helps you understand where you stand and set realistic improvement targets.

This guide provides PR cycle time and DORA metric benchmarks based on team size and industry, along with guidance on how to use benchmarks constructively without falling into common traps.

Why Benchmarks Matter (and Their Limits)

The Value of Benchmarks

Benchmarks help you:

• Calibrate expectations: Know if your 3-day cycle time is excellent or needs work
• Set realistic goals: Target improvements based on what's achievable
• Justify investment: Show leadership how you compare to industry standards
• Identify opportunity: Large gaps to benchmarks indicate room for improvement

The Limits of Benchmarks

But benchmarks also have significant limitations:

• Context matters: A fintech with compliance requirements will naturally be slower than a startup moving fast
• Self-reported data: Most benchmarks come from surveys, which tend to skew optimistic
• Survivor bias: Companies that track metrics well enough to participate in surveys are already above average
• Measurement differences: "Cycle time" can be measured from PR open, first commit, or ticket start—definitions vary

Key principle: Use benchmarks as a starting point for conversation, not as absolute targets. Your goal is continuous improvement from your current baseline, not hitting an arbitrary external number.

Cycle Time Benchmarks by Team Size

How Team Size Affects Cycle Time

Larger teams typically have longer cycle times due to:

• Coordination overhead: More people to align with
• Review queues: More PRs competing for reviewer attention
• Specialization: Specific reviewers needed for specific code
• Process overhead: Larger orgs have more required approvals, checks, etc.

Benchmarks by Team Size

Based on industry data from DORA, LinearB, and CodeClimate reports (2023-2024):

PR Cycle Time Benchmarks (PR open → merged)

Small Teams (< 10 engineers):
  Elite:    < 4 hours
  Strong:   4-12 hours
  Average:  12-36 hours
  Needs work: > 36 hours

Medium Teams (10-50 engineers):
  Elite:    < 12 hours
  Strong:   12-24 hours
  Average:  1-3 days
  Needs work: > 3 days

Large Teams (50-200 engineers):
  Elite:    < 24 hours
  Strong:   1-2 days
  Average:  2-5 days
  Needs work: > 5 days

Enterprise (200+ engineers):
  Elite:    < 2 days
  Strong:   2-3 days
  Average:  3-7 days
  Needs work: > 7 days

Breaking Down Cycle Time

Total cycle time is the sum of several stages. Knowing where time is spent helps you target improvements:

Cycle Time Components:

Time to first review:
  Elite:    < 1 hour
  Strong:   1-4 hours
  Average:  4-24 hours
  Needs work: > 24 hours

Review iterations (time in review):
  Elite:    < 2 hours
  Strong:   2-8 hours
  Average:  8-24 hours
  Needs work: > 24 hours

Time to merge (after approval):
  Elite:    < 1 hour
  Strong:   1-4 hours
  Average:  4-12 hours
  Needs work: > 12 hours

DORA Metric Benchmarks by Industry

The Four DORA Metrics

DORA (DevOps Research and Assessment) tracks four key metrics that predict software delivery performance:

1. Deployment Frequency: How often you deploy to production
2. Lead Time for Changes: Time from commit to production
3. Change Failure Rate: Percentage of deployments causing failures
4. Mean Time to Recovery: How quickly you recover from failures

DORA Benchmarks (2024 State of DevOps Report)

Deployment Frequency:
  Elite:    Multiple times per day
  High:     Weekly to daily
  Medium:   Monthly to weekly
  Low:      Less than monthly

Lead Time for Changes:
  Elite:    Less than 1 hour
  High:     1 day to 1 week
  Medium:   1 week to 1 month
  Low:      More than 1 month

Change Failure Rate:
  Elite:    0-5%
  High:     5-10%
  Medium:   10-15%
  Low:      > 15%

Mean Time to Recovery:
  Elite:    Less than 1 hour
  High:     Less than 1 day
  Medium:   1 day to 1 week
  Low:      More than 1 week

Industry Variations

Different industries have different constraints and typical performance levels:

SaaS / Consumer Tech:
  Typical deployment: Daily to multiple times daily
  Typical lead time: < 1 day
  Context: Fast iteration, A/B testing culture

Fintech / Financial Services:
  Typical deployment: Weekly
  Typical lead time: 1-2 weeks
  Context: Compliance reviews, risk management

Healthcare / Life Sciences:
  Typical deployment: Bi-weekly to monthly
  Typical lead time: 2-4 weeks
  Context: Regulatory requirements, validation

Enterprise B2B:
  Typical deployment: Weekly to bi-weekly
  Typical lead time: 1-2 weeks
  Context: Customer coordination, stability focus

E-commerce:
  Typical deployment: Daily
  Typical lead time: < 1 day
  Context: Competitive pressure, rapid iteration

How to Use Benchmarks Constructively

Step 1: Know Your Current State

Before comparing to benchmarks, accurately measure your current performance:

• Use consistent definitions (what counts as "cycle time" for you?)
• Measure over a meaningful period (at least 30-90 days)
• Segment by team, repo, or PR type if needed
• Exclude outliers thoughtfully (don't hide problems)

Step 2: Choose Relevant Comparisons

Don't compare apples to oranges:

• Match team size: A 5-person startup shouldn't compare to Google
• Match industry: A bank shouldn't target SaaS startup metrics
• Match maturity: A 2-year-old company differs from a 20-year-old one

Step 3: Focus on Improvement, Not Rank

The goal isn't to be "elite" by external standards. It's to:

• Improve from your current baseline
• Remove obstacles blocking your team
• Sustain improvements over time

A team that goes from 5-day cycle time to 3-day cycle time has achieved more than a team that was always at 1 day but hasn't improved.

Step 4: Investigate Gaps Thoughtfully

If you're significantly below benchmarks, ask why before taking action:

• Is it a measurement issue? Maybe you're measuring differently
• Is it justified? Compliance requirements, quality standards
• Is it a process problem? Too many approvals, slow CI
• Is it a people problem? Not enough reviewers, skill gaps

Step 5: Set Incremental Targets

Don't try to jump from "needs work" to "elite" overnight:

Example improvement roadmap:

Current state: 5-day average cycle time

Quarter 1 target: 4 days (20% improvement)
  Focus: Reduce time to first review

Quarter 2 target: 3 days (25% improvement)
  Focus: Reduce review iterations

Quarter 3 target: 2 days (33% improvement)
  Focus: Improve PR size and quality

Quarter 4 target: 1.5 days (25% improvement)
  Focus: Automation and process refinement

Setting Your Own Targets

Building a Target-Setting Framework

Use this framework to set meaningful targets:

1. Baseline (where are we?)

• Measure current performance over 90 days
• Understand variance (some weeks better than others)
• Identify biggest time sinks

2. Aspiration (where could we be?)

• Look at benchmarks for similar teams
• Look at your own best weeks/teams
• Talk to engineers about what feels achievable

3. Constraints (what limits us?)

• Compliance or regulatory requirements
• Team size and availability
• Technical limitations (slow CI, monorepo)

4. Target (what should we aim for?)

• Ambitious but achievable (20-30% improvement per quarter)
• Specific and measurable
• Time-bound

Example Target-Setting

Team: Platform team, 15 engineers, fintech

Baseline:
  PR cycle time: 4.2 days
  Time to first review: 18 hours
  Review iterations: 2.1 average
  Deployment frequency: 2x per week

Benchmarks (medium fintech teams):
  PR cycle time: 2-3 days
  Time to first review: 4-8 hours
  Review iterations: 1.5 average
  Deployment frequency: 3-5x per week

Constraints:
  - Compliance requires 2 approvers
  - Can't deploy Friday-Sunday
  - Some specialists only available certain days

Targets (next quarter):
  PR cycle time: 3.0 days (29% improvement)
  Time to first review: 8 hours (56% improvement)
  Review iterations: 1.8 average (14% improvement)
  Deployment frequency: 3x per week (50% improvement)

Actions:
  - Implement review SLA (4-hour target)
  - Add second approver from each specialty area
  - Improve PR templates to reduce iterations

Avoiding Common Traps

• Don't game the metric: Splitting PRs artificially or merging without proper review defeats the purpose
• Don't ignore context: A 5-day cycle time for a complex, well-reviewed PR is better than 4 hours of rubber-stamping
• Don't compare unfairly: Your maintenance team shouldn't be held to feature team benchmarks
• Don't forget quality: Faster isn't better if change failure rate increases

Benchmarks are useful reference points, but they're not the goal. The goal is a healthy, improving engineering organization that delivers value sustainably. Use benchmarks to inform that journey, not to define it.