Ask someone what AI video tool they need and you usually get one answer: a generator. Then the work starts, and it turns out the generator was the part they needed least. The category behaves like six separate tools that happen to share a shelf, and picking in the wrong order is what makes the bill feel unreasonable.
The six are video generators, video editors, voice generators, text to speech, music generators and subtitle tools. A side by side comparison of all six, including where the free tiers stop, lives in the AI video and audio hub.
Generation Is The Smallest Part Of The Job
Text to video has moved fast. The current generation of models produces clips that hold up in a feed, and image to video gives you a way to keep a look consistent across shots by feeding the same still. What it does not give you is a finished piece of content.
Generated footage has to be cut, ordered, narrated and captioned like any other footage. Teams that start here end up paying for the most expensive tool in the stack to produce raw material they then process by hand.
There is one case where starting with a generator is right: you need footage that does not exist and cannot be filmed. Otherwise the material you already have is cheaper and more distinctive than anything a prompt returns.
Editing Is Where The Time Actually Goes
An AI video editor automates the tedious middle of post production. Cutting dead air and filler words, finding the moments worth keeping in a long recording, reframing a horizontal recording for vertical, and pulling short clips out of something that ran an hour.
That is the part of the process that consumes real hours, and it is the part where automation pays back immediately. A one hour recording turned into five usable clips is a bigger content win than any single generated video, and the source material is yours.
The limitation is judgment. These tools are good at finding pauses and speech boundaries, and much weaker at knowing which thirty seconds actually make the point. Treat the automatic cut as a first pass, not a final one.
Voice And Captions Decide Whether Anyone Watches
Voice generation and text to speech overlap but solve different problems. Text to speech turns a script into narration, which is what most explainer and tutorial content needs. Voice cloning reproduces a specific voice from a short sample, which matters when a series has an established sound or when a person cannot record every update themselves.
Captions are the least glamorous category and the one with the clearest return. Speech recognition produces timed captions in minutes rather than the hours manual transcription took, with styling and translation on top. Most feeds autoplay muted, so an uncaptioned video is asking viewers to opt in before they know whether it is worth it.
Accuracy is where these tools differ most. Names, product terms and accented speech are still where errors cluster, so budget a pass to fix the handful of words that matter rather than trusting the transcript wholesale.
What To Take Away
The order that wastes the least money is the reverse of how most people shop. Caption what you already publish. Edit the recordings you already have. Add synthetic narration where nobody needs to be on camera. Reach for a generator when you genuinely need footage that does not exist.
Music generation sits off to the side of that path, useful when licensing is the blocker rather than production.
Pick per job rather than per vendor. The six categories fail in different ways, and a bundle that is strong at generation is often weakest at the captions that decide whether the video gets watched at all.
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