How to choose an AI video generator, including when not to choose ours

Three quite different products get compared against each other in every roundup, and picking from the wrong category is a more expensive mistake than picking the wrong vendor within the right one.

Every one of these is answerable from a trial or a docs page in under ten minutes, and each one has sunk a channel for somebody.

• Does it publish, or does it export?. The single biggest divide in the category. A tool that hands you an MP4 has automated production and left you the ten minutes per video of uploading, titling and scheduling. Over a year at a daily cadence that is around sixty hours of work that the word automated did not cover.
• How different are two videos from the same series?. Generate two on related topics and put them side by side. If the structure, pacing and visual grammar are identical, you are looking at a template, and a library of templated uploads is a monetization problem rather than a taste problem.
• Are subtitles timed to the word or to the line?. Most short-form video is watched muted, so this is a retention feature and not a decoration. Line-level captions are what you get from generic auto-captioning; word-level needs the narration transcribed back after it is generated.
• What does it cost to redo one thing?. Not to generate a video, but to regenerate one image or rewrite one chapter. You will do this constantly. A tool that charges a full video credit to fix a single scene is far more expensive than its headline price implies.
• Do you own the output?. Check for watermarks, for licence terms on commercial use, and specifically for what happens on cancellation. Some tools revoke rights to previously generated content when a subscription lapses, which is catastrophic for a monetized channel and is never on the pricing page.
• Can you stop a bad video before it goes out?. Full automation with no gate means the first time you see a video is after your audience did. A review window is what makes unattended publishing survivable.
• Which platforms does it actually publish to, and does it report back?. Publishing and analytics are separate capabilities with separate API requirements. Ask about both. TikTok in particular returns no per-video statistics without an approved scope, so any dashboard showing TikTok numbers is either approved or inventing them.

Because a demo is one output selected from many, on a topic chosen because it generates well. That is not deception; it is what a demo is. But it means the demo tells you the ceiling, and you need the floor.

The way to find the floor is to test on your actual subject, including the awkward parts of it. Generate something in your niche that involves a specific number, a proper noun, and an abstract concept. Abstract concepts are where image generation reliably falls apart, and every niche has them. A finance channel will need to illustrate inflation; a history channel will need a named person.

Then generate five in a row and look at them as a set. The first video is a product test. The set is the actual product.

There are several clear cases, and knowing them saves everyone a refund conversation.

• You want one video, not a channel. The whole model is a series on a cadence. For a single piece, a clip generator is cheaper and better suited.
• You need your own footage in the video. Visuals are generated per scene rather than assembled from your assets. If you have a library to edit, you want a template editor.
• You need a presenter on camera. There is no avatar or talking head. Some subjects genuinely require a face, and faceless production cannot substitute for one.
• You need TikTok analytics specifically. Publishing to TikTok works fully. Per-video statistics do not come back, so if TikTok reporting is the requirement, this will not meet it.
• You want to approve every video by hand before it goes out. You can, and the review window exists for exactly that. But if you intend to gate every single upload manually, you are paying for scheduling you are not using.

If none of those apply, the pipeline in detail is the fastest way to judge whether the model fits: the six stages and what each one decides

Less than the marketing suggests. Tools advertise the models they run because it is a concrete-sounding differentiator, but the model layer turns over roughly twice a year and every serious product swaps as the market moves. A tool chosen for its model in January is running something else by December.

What is durable is everything around the model: whether the aspect ratio gets checked before assembly, whether narration and subtitles are actually aligned, whether a failed generation retries or silently ships. Those are engineering decisions, they persist, and they are what determines whether the fiftieth video is publishable.

As a concrete example, generated imagery does not reliably respect an aspect-ratio instruction from any current model. A pipeline that checks orientation and re-frames before assembly will produce correct vertical video regardless of which generator sits underneath. One that trusts the instruction will letterbox some percentage of your Shorts forever, whatever model it upgrades to.

This is the unglamorous half of the category, and it is the half that decides whether you are still publishing in a year. The tools worth paying for are the ones where somebody has already sweated the boring checks, because that is the whole difference between a thing you operate and a thing that runs.