"Are we shipping fast enough?"
This question haunts every engineering leader, from early-stage startups to enterprise organizations. Speed is a competitive advantage, and metrics like Cycle Time and Lead Time define how fast your engineering team delivers value. But here’s the kicker—many teams misinterpret these metrics, leading to inefficiencies, misaligned goals, and frustration across the organization.
If you've ever felt like your team is delivering features too slowly, yet developers are always busy, you're not alone. In this article, we’ll break down Cycle Time and Lead Time, why they matter, and how to optimize them to balance speed, efficiency, and quality in software development.
Cycle Time measures how long it takes to complete a task once work has started. In software development, it’s the time from the first commit of a feature or bug fix to its deployment in production.
Think of Cycle Time as the "in-progress" time—the time a task spends actively being worked on before it's delivered.
Cycle Time is calculated as:
Cycle Time = Coding Time + Review Time + Merge Time
Breaking it down further:
For example, if a developer starts coding on Monday at 10 AM and the feature is merged into production by Thursday at 4 PM, the Cycle Time is 78 hours.
It impacts speed to market – The faster your Cycle Time, the more frequently you can deploy updates and respond to customer needs.
Lead Time measures the total time from when a task is requested to when it is delivered. It includes waiting times, prioritization delays, development, testing, and deployment.
Lead Time is an end-to-end measure—from idea to production. If Cycle Time is about execution efficiency, Lead Time is about overall process effectiveness.
Lead Time is calculated as:
Lead Time=Time when work is delivered−Time when work is requested
Breaking it down:
For example, if a feature request is created on January 1st but only starts development on January 10th, and is finally deployed on January 15th, the Lead Time is 14 days (10 days waiting + 4 days Cycle Time).
It directly affects customer experience – Faster Lead Time ensures users receive new features and bug fixes more quickly.
• Short Cycle Time, Long Lead Time → Your team works efficiently, but work sits in the backlog too long before starting.
• Short Lead Time, Long Cycle Time → Work is prioritized correctly, but slow development processes delay releases.
• Long Cycle Time, Long Lead Time → Your engineering process is slow from both a prioritization and execution standpoint—this is a red flag.
• If your team is constantly busy but features take too long to release → Optimize Cycle Time (improve coding and review efficiency).
• If your customers complain about delays in getting features → Optimize Lead Time (streamline prioritization and reduce waiting time).
Improve cross-team collaboration – Reduce dependencies and handoff delays.
Many teams mistakenly think reducing Cycle Time will automatically reduce Lead Time. But if work sits idle before being picked up, Lead Time will still be high.
If a PR sits in "review" for days, your development team isn’t slow—your process is. Look at where work gets stuck, not just how long it takes to code.
Optimizing for Cycle Time without considering Lead Time can create local improvements without solving real business problems. Both metrics must align with company objectives.
Understanding the difference helps teams identify where inefficiencies lie. Whether it’s slow development cycles (Cycle Time) or excessive waiting periods before work begins (Lead Time), optimizing both leads to faster value delivery, happier customers, and more efficient engineering teams.
So, the next time someone asks, “Are we shipping fast enough?”—you’ll know exactly where to look.
🚀 Now go and optimize your delivery pipeline!
Cycle Time measures execution speed; Lead Time measures the entire delivery timeline, including delays before work starts.
Yes—this happens when work sits in the backlog for too long before starting.
Automate testing, speed up code reviews, and reduce WIP.
Prioritize tasks effectively, remove unnecessary waiting times, and improve collaboration across teams.
Jira, Linear, GitHub Insights, and engineering analytics platforms like Hivel.