Noah Bennett

Posted on Sep 21

How Long Does It Take to Generate an AI Video? I Timed My Own Workflow to Find Out

#ai #api #tutorial #devtools

The first thing I wanted to know when I started making AI video was simple: how long does it take to generate an ai video? Everyone I asked gave me a different answer, and every answer left out the part that actually ate my week.

So I stopped asking and started timing. This post covers what drives generation time, why the wait for one clip is rarely the real cost, and a small timing log you can copy today.

How long does it take to generate an AI video? It depends on four things

Any single number you read online is a guess, and I won't give you one either. What actually moves the wait:

The model. Different models make different trade-offs between speed and quality.
The clip itself. Longer duration and higher resolution mean more work per generation.
The spec you pick. A draft-quality run and a final-quality run are not the same job.
The queue. Load at the moment you submit changes the wait, even for identical settings.

That's why the honest answer is "measure your own stack." I'll show you how in a minute.

The wait for one clip isn't the real cost

Once I started logging, I noticed something. The wait for a single clip mattered less than everything around it. Here's the math I ended up with:

total time ≈ attempts × (wait + review + handoff)

Shaving seconds off the wait only touches one term. The other two, and the number of attempts, are where my afternoons went:

Wasted attempts. Wrong aspect ratio, soft input photo, a model that couldn't do the shot.
Handoffs. Editing in one tool, upscaling in another, animating in a third. Every switch means downloading, renaming, and re-uploading.
Blind spend. Not knowing the cost until a run finished made me hesitate on every retry.
No record. I couldn't tell which model or setting was actually faster for my shots.

It hit hardest in the two use cases I kept building for: an AI product video generator for small sellers, and a "tap to animate a photo" feature for a social app.

What I wanted from a faster AI video workflow

Since the loop matters more than the wait, I wrote down what would shorten it:

Fewer wasted attempts. Control the first frame and try another model without starting over.
Fewer handoffs. Images and video in the same place.
Visible cost. See the spec and the estimated cost before I submit.
A record. Timing data from my own runs, not somebody's blog post.

Where the loop got shorter: simple AI video creation in one workspace

That's when I tried VOKOO, a multi-model AI creation platform built around video. Its tagline is "Create more. Switch less," and it covered most of my list.

I spun up a quick test with a single prompt and had a video back before I finished my coffee. One quick test isn't a benchmark, though, and I won't pretend it is. What changed my total time wasn't one fast run. It was fewer detours.

Prompt to clip, in one place

The AI video generator turns a text prompt, or a prompt plus an image, into a video. Make a video before the idea gets cold.

Generate the still, then move it

The AI image generator builds the first frame, and the platform carries it into motion. That's the image to video AI loop in one flow, and it's the quickest way I've found to animate a photo without re-uploading anything.

Switch models, not tabs

The AI agent lets me try different models without rebuilding my workflow. Same prompt, different model, then compare the result and how long each one took. That's data you can't get from a blog post.

Choose the spec, see the cost

I can pick quality and generation specs per stage and see the estimated credit cost before I submit. I draft first, then render the final at full quality. Less setup. More creative output.

How long does it take to generate an AI video on your stack? Log it yourself

Since I generate in a web workspace, I time runs by hand. Two tiny scripts do the job: one logs each run, one summarizes the log. Neither depends on any particular platform.

Start the timer when you submit, stop it when the video is ready:

#!/usr/bin/env bash
# vtimer.sh - log how long each generation takes
# usage: ./vtimer.sh start shot_01 model_a draft
#        ./vtimer.sh stop
LOG="runs.csv"
STATE=".vtimer_state"

case "$1" in
  start)
    echo "$(date +%s),$2,$3,$4" > "$STATE"
    echo "timer started: $2 / $3 / $4"
    ;;
  stop)
    [ -f "$STATE" ] || { echo "no timer running"; exit 1; }
    IFS=',' read -r t0 shot model spec < "$STATE"
    secs=$(( $(date +%s) - t0 ))
    [ -f "$LOG" ] || echo "date,shot,model,spec,seconds" > "$LOG"
    echo "$(date +%F),$shot,$model,$spec,$secs" >> "$LOG"
    rm "$STATE"
    echo "logged: $shot / $model / $spec = ${secs}s"
    ;;
  *) echo "usage: $0 start <shot> <model> <spec> | stop"; exit 1 ;;
esac

Then summarize after a day of work:

# summarize.py - median wait per model/spec, total time per shot
import csv
from collections import defaultdict
from statistics import median

by_combo, by_shot = defaultdict(list), defaultdict(int)
with open("runs.csv") as f:
    for row in csv.DictReader(f):
        secs = int(row["seconds"])
        by_combo[(row["model"], row["spec"])].append(secs)
        by_shot[row["shot"]] += secs

print("median wait per model/spec:")
for (model, spec), vals in sorted(by_combo.items()):
    print(f"  {model:10} {spec:8} n={len(vals)} median={median(vals)}s")

print("total generation time per shot (all attempts):")
for shot, total in sorted(by_shot.items()):
    print(f"  {shot:10} {total}s")

Two numbers matter here. The median wait per model and spec answers your original question for your own stack. The total time per shot, across every attempt, is the number that actually predicts your afternoon.

Keeping the time and the bill down

The cheapest attempt is the one you never make. Anchor the first frame, change one variable per run, and keep the log so you stop repeating experiments.

Fewer bad prompts also means fewer wasted attempts. If you'd like an LLM to draft and tighten your prompts before you submit, RouteAI provides a cost-effective, OpenAI-compatible API gateway with multiple models, so setup stays simple.

Try this next

Asking how long it takes to generate an AI video is a fair first question. It just isn't the last one. The better question is how long the whole loop takes, and how many handoffs you can cut. VOKOO shortened mine by removing them. Stop managing tools. Start making things.

Here's a 20-minute test you can run today:

Save both scripts and run ./vtimer.sh start shot_01 model_a draft.
Generate the same shot on two different models, stopping the timer each time.
Run python summarize.py and compare the median waits.
Check the estimated cost before you scale up.

If you want an easy AI video generator that keeps simple AI video creation simple and still leaves room to explore, try VOKOO at https://vokoo.ai.

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