What is the difference between time to fill and time to hire?

Time to fill is calculated from the job requisition open date (or job posting date) to the offer acceptance date. It captures everything: internal approval delays, sourcing time, screening, interviews, and offer. Time to hire is calculated from the candidate's first application or entry into the pipeline to offer acceptance. A role with slow internal approvals but a fast candidate-facing process will show a long time to fill but a short time to hire — and that distinction tells you where to look for improvements.

Both metrics serve different audiences. Time to fill is most useful for business planning — it tells hiring managers how long to expect before a vacancy is filled. Time to hire is most useful for optimizing the candidate experience and the interview process — it shows how long candidates spend in your funnel. Teams serious about recruiting operations track both, segment them by role level and department, and set separate SLA targets. Tracking only one can lead to optimizing the wrong part of the process.

Time to fill is often inflated by slow requisition approvals, delayed job postings, and passive sourcing strategies. Time to hire is most often inflated by scheduling bottlenecks between interview rounds and slow offer generation after a final decision. Fixes are distinct: reduce time to fill by streamlining pre-posting approvals and building a proactive talent pipeline; reduce time to hire by implementing self-scheduling, setting debrief SLAs, and pre-drafting offer letters before the final interview.

The confusion comes from the fact that both measure hiring speed but from different start points, and people use the names interchangeably in conversation. Time to fill counts from when a role is opened or approved to when a candidate accepts — it includes the sourcing and requisition phase. Time to hire counts from when a specific candidate enters your pipeline to when they accept — it measures how efficiently you move a person through your process. One is about how long the whole role takes to close; the other is about how fast your funnel converts an individual. Mixing them up leads teams to diagnose the wrong problem.

Time to fill exposes upstream problems: slow requisition approvals, a cold pipeline, or difficulty attracting applicants in the first place. If time to fill is long but time to hire is short, your process is efficient once candidates arrive — you are simply slow to get them into the funnel. Time to hire exposes downstream friction: screening backlogs, scheduling delays, and slow decisions. If time to hire is long, the problem lives inside your process, not in sourcing. Watching both together tells you not just that hiring is slow, but which half of the journey to fix, which is why sophisticated teams track the pair rather than one number.

The practical workflow is to look at the two in relation to each other for each role type. A long time to fill with a short time to hire points you toward pipeline building and faster requisition sign-off — invest in proactive sourcing and remove approval bottlenecks. A short time to fill with a long time to hire points you toward internal process: automate screening, add scheduling links, and set feedback SLAs. When both are long, you have work at both ends. This diagnostic use is the whole point; the numbers are not a scoreboard but a map that tells you where to spend your improvement effort.

Common inflators split cleanly by metric. Time to fill grows from reactive sourcing, cold pipelines, and slow requisition approvals — fixed by always-on pipeline building and streamlined sign-off. Time to hire grows from unreviewed resume queues, scheduling friction, and slow interviewer feedback — fixed by automated screening, self-serve booking, and stage SLAs. Vague hiring criteria inflate both, because they cause re-sourcing and re-interviewing. The fix for that is an upfront alignment meeting on the scorecard. Matching each cause to the metric it distorts keeps you from applying a sourcing fix to a scheduling problem, which is the usual reason improvement efforts stall.