Time to Fill vs Time to HireTwo metrics everyone mixes up, and which one to fix
People throw these two terms around like they mean the same thing. They do not. One measures how long a role stays open. The other measures how fast your team moves once a candidate shows up. Confuse them and you will fix the wrong problem. Here is the clean version, with formulas, benchmarks, and a simple way to tell which number is failing you.
Two metrics, two start lines, one finish line
The gap between the two numbers is your sourcing time. A wide gap means finding people is slow, not interviewing them.
Most hiring dashboards show one big number labeled something vague like "average days to hire," and everyone nods along without checking what it actually counts. That single number hides the real story. A 45-day hiring cycle could mean your interview process is slow, or it could mean you spent five weeks just finding someone worth interviewing. Those are completely different problems with completely different fixes.
Time to fill and time to hire split that story in two. The SHRM definitions are the standard most recruiters reference, and they are worth getting right because the wrong definition quietly corrupts every report built on top of it. If you want the bigger picture on hiring speed, our guide on how to reduce time to hire goes deeper on the fixes, and our recruitment metrics overview shows where these two sit among the numbers that matter.
My view, after watching plenty of teams argue about slow hiring: the argument usually ends the second someone separates these two metrics. The villain reveals itself fast. Let me show you how.
The Definitions
What each metric actually counts
Both metrics end at the same moment: the candidate accepts the offer. The difference is entirely about where the clock starts.
Time to fill starts when the job requisition is approved. It counts every day the role is open: writing the job description, posting it, sourcing, screening, interviewing, and the final offer. It answers the question a finance leader or a hiring manager cares about: how long until this empty seat has a person in it?
Time to hire starts later, when a specific candidate first enters your pipeline. It counts only the days that candidate spent moving through your process. It answers a different question: once a qualified person shows up, how fast and smooth is our machine? This is the metric that maps directly to candidate experience, because it is the part of the timeline the candidate actually feels.
Time to fill
- Starts
- Requisition approved
- Ends
- Offer accepted
- Measures
- Total days a role stays open
- Best for
- Workforce planning, cost of vacancy, hiring forecasts
Time to hire
- Starts
- Candidate enters pipeline
- Ends
- Offer accepted
- Measures
- Process efficiency once candidates exist
- Best for
- Candidate experience, interview friction, team SLAs
The Formulas
How to calculate both, without overcomplicating it
Keep these simple. The moment you bolt on weighting, exclusions, and special cases, people stop trusting the numbers and quietly go back to guessing.
Time to fill
Offer accepted date − requisition approved date
Example: a role approved on March 1 that gets a signed acceptance on April 12 has a time to fill of 42 days. To report at the team level, take the median across closed roles, not the mean. One executive search that dragged 120 days will wreck an average and tell you nothing useful.
Time to hire
Offer accepted date − candidate pipeline-entry date
Measured for the person you actually hired. If they applied on March 22 and accepted on April 12, time to hire is 21 days. Because it starts later, it is almost always the smaller of the two numbers. Use the median here too, calculated across your hires for a given role or quarter.
Notice the relationship: time to fill minus time to hire equals your sourcing time. In the example above, that is 42 minus 21, so 21 days spent finding the candidate before they ever entered the funnel. That subtraction is the single most useful thing you can do with these two metrics.
Stop calculating these by hand
Prepzo tracks time to fill, time to hire, and stage-level aging automatically, so the bottleneck shows up on a dashboard instead of in a spreadsheet you forget to update.
Try Prepzo freeThe Diagnosis
What the two numbers reveal when you read them together
This is where these metrics earn their keep. On their own, each number is mildly interesting. Side by side, they point straight at the part of your hiring that is broken. Here is the quick read on every combination.
High time to fill, low time to hire
What it means: Sourcing is the bottleneck. You move fast once people apply, but it takes weeks to find them.
What to do: Invest in sourcing channels, referrals, and a warm talent pool. The interview process is fine.
Low time to fill, high time to hire
What it means: Rare, and usually a measurement error. Check whether your pipeline-entry timestamp is set correctly.
What to do: Audit how your ATS records when a candidate first enters the funnel.
High on both
What it means: Both sourcing and process drag. The most common pattern in teams without analytics.
What to do: Fix process first because it is faster to control, then attack sourcing.
Low on both
What it means: Healthy. Keep tracking stage-level aging so it stays that way as volume grows.
What to do: Maintain SLAs and protect your interview windows.
The most common real-world case is high on both, and the honest answer is to attack process first. You control your own calendar and your own feedback deadlines. You do not fully control how long it takes a great backend engineer to notice your job exists. Fix what is yours to fix, then turn to sourcing with our guide on how to source passive candidates.
The Benchmarks
What good looks like, with a warning
Everyone wants a benchmark, so here is one, with a clear caveat: these are planning ranges, not targets to copy. Hiring speed varies wildly by role, seniority, location, and labor market. The Bureau of Labor Statistics JOLTS data shows how much hiring conditions swing with the broader market, and a tight market stretches every number on this chart.
The reliable move is to build your baseline from your own history. Pull your median time to fill and time to hire per role family over the last year, and measure improvement against that. A 40-day time to fill is a problem if your own median is 25 and a win if your own median is 55.
Customer support
Sales
Marketing
Software engineering
Finance & accounting
Illustrative ranges for planning. Build your own baseline from your historical median before comparing to anyone else.
One more reason to segment by role: an average across a warehouse hire and a VP of Engineering search is meaningless. Harvard Business Review has written about how blunt hiring metrics push teams toward speed for its own sake, which is exactly the trap a single blended number sets for you.
Why The Distinction Pays Off
The cost of getting this wrong
An empty seat is not free. Every open role carries a cost: lost output, overloaded teammates picking up the slack, and sometimes lost revenue when the role is customer-facing. Time to fill is the metric that quantifies that pain, which is why it belongs in workforce planning conversations and budget reviews. When a hiring manager complains that a vacancy is killing the team, time to fill is the number that makes the case.
Time to hire carries a different cost: lost candidates. Strong people interview in multiple places at once, and a slow process loses them to a faster competitor. LinkedIn talent research has long shown that top candidates come off the market quickly, so a bloated time to hire is a direct leak in your funnel. If your offer-accept rate is sliding, time to hire is the first place I would look.
Here is the trap of merging them. If you only watch one blended number and it creeps up, you cannot tell whether to hire a sourcer or fix your interview loop. So you guess. Maybe you pour money into job ads when the real problem was a hiring manager sitting on feedback for a week. Splitting the metrics turns a guess into a diagnosis.
There is also a quality angle. Speeding up by cutting evaluation steps is a false economy, and it tends to show up later as a bad hire. The right way to compress these metrics is to remove waiting time, not judgment time. Faster screening, tighter scheduling, and on-time feedback shrink the calendar without lowering the bar.
Putting It To Work
How to move both numbers in the right direction
Tighten the requisition before it opens
A vague role drags time to fill from day one because sourcing wanders. Lock the must-haves, salary range, and interview loop in a 30-minute kickoff. Our guide on writing job descriptions is a clean starting point.
Screen in hours, not weeks
Resume backlogs are pure time to hire poison. Review new applicants daily and let AI handle the first triage so humans only look at the top tier. That is the core job of AI screening.
Set feedback SLAs and report on them
Most time to hire damage is a hiring manager treating interview feedback like optional homework. Put deadlines in writing: feedback within 24 hours, debrief within 48. A pre-submitted interview scorecard makes this painless.
Track stage aging, not just the totals
The total is a scoreboard. Stage aging is the diagnosis. Watch days in review, days to schedule, and days from final round to decision so you can see exactly where the calendar leaks. This is what hiring analytics should surface automatically.
Pre-close finalists early
Surface salary expectations, competing offers, and start dates during the loop, not after the decision. It shaves days off the offer stage and stops late surprises from reopening a role you thought was filled.
Frequently Asked Questions
What is the difference between time to fill and time to hire?
Time to fill counts the days from when a job requisition is approved to when a candidate accepts the offer. Time to hire counts the days from when a candidate enters your pipeline to when that same candidate accepts. Time to fill measures the whole opening, including sourcing. Time to hire measures how fast you move once a real person shows up.
What is a good time to fill?
It depends heavily on role and seniority. Across industries, time to fill commonly lands between 30 and 45 days, with engineering and senior roles running longer. Use your own historical median as the baseline rather than a generic benchmark, because a software engineer requisition and a warehouse requisition are not the same race.
What is a good time to hire?
For most non-executive roles, 14 to 28 days from pipeline entry to accepted offer is competitive. Time to hire is usually shorter than time to fill because it excludes the sourcing phase. If your time to hire drifts past a month, you almost always have a scheduling or feedback problem rather than a candidate-quality problem.
Should I track time to fill or time to hire?
Track both, because they answer different questions. Time to fill tells you how long roles stay open, which matters for workforce planning and the cost of an empty seat. Time to hire tells you how efficient your interview process is once candidates are in the funnel. If the two numbers are far apart, your sourcing is slow. If time to hire alone is high, your process has friction.
Does time to fill include the sourcing stage?
Yes. Time to fill starts at requisition approval, so it captures everything: job posting, sourcing, screening, interviewing, and the offer. Time to hire starts later, when a candidate first enters the pipeline, so it deliberately excludes how long it took to find that person.
How can AI reduce time to fill and time to hire?
AI shortens the parts of hiring that are admin, not judgment. Resume screening, scheduling coordination, and interview note capture are the usual time sinks. Automating those compresses both metrics without touching the human decision. The goal is to remove waiting time, not evaluation time.
Resources & Further Reading
Related Guides
- How to Reduce Time to Hire: 10 Practical Fixes
The follow-through once you know which metric is failing
- 15 Recruitment Metrics & KPIs Every Hiring Team Should Track
Where these two numbers sit among the rest
- The Real Cost of a Bad Hire
Why cutting evaluation to save days backfires
- How to Source Passive Candidates
For when sourcing is the bottleneck, not the process
External Sources
- SHRM: How to Calculate Time to Fill and Time to Hire
The standard definitions most recruiters reference
- Bureau of Labor Statistics: JOLTS Report
How labor-market conditions move hiring timelines
- Harvard Business Review: Your Approach to Hiring Is All Wrong
On the limits of speed-first hiring metrics
- LinkedIn Talent Solutions: Resources
Data on candidate behavior and hiring speed
See your real bottleneck, not a blended average
Prepzo separates time to fill, time to hire, and stage-level aging by role, so you fix the part of hiring that is actually slow. Free to start.
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