Every AI video tool markets generation speed in the best possible light, a single clip generated on an uncrowded server with no queue. Real working conditions during a busy afternoon look nothing like that, so I started actually logging my render times across a normal work week to see what the honest numbers looked like.
Queue time versus generation time
The gap between marketed generation speed and my actual experience almost never comes from the model being slower than advertised. It comes from queue time during peak hours, which none of the marketing pages mention because it's not really a model property, it's a capacity property that shifts by time of day and how many other people are generating at that exact moment. Early morning in my timezone, generation is close to instant. Mid-afternoon, when the US is fully awake and generating too, queues stretch out meaningfully.
What I actually do about it
I've shifted my heaviest generation batches to early morning specifically to dodge peak queue times, running large batches of variations before I've had coffee rather than mid-afternoon when I used to default to generating. It sounds like a small scheduling trick and it's saved real hours over a month, hours that used to just disappear watching a progress bar that could've been productive time somewhere else.
- Generation speed itself is usually close to advertised
- Queue time during peak hours is the real, unmarketed variable
- Batch heavy generation runs early or late, not during peak usage windows
Why this matters for client deadlines
Quoting a client turnaround based on marketed generation speed alone is a good way to blow a deadline you didn't need to blow. I now build queue-time buffer into any estimate involving AI generation as a core deliverable, same instinct as building weather buffer into an outdoor shoot schedule. Not glamorous planning, but it's the difference between a comfortable delivery and an apologetic one.