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Category guide

Nothing in this category does all three things

An AI production has to answer three questions later: what made this, what came before it, and who said yes. Review tools answer the last two. Prompt tools answer the first. Here is who covers what, with the vendors' own documentation behind every claim.

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The three jobs

Why the field is split down the middle

Review and approval grew out of video post-production, where the file is finished before anyone looks at it. Prompt versioning grew out of software engineering, where the artefact is text and no client signs anything. Both halves are mature and neither was built for a workflow where the file is generated, iterated and then approved by someone outside the team. That is why a team doing AI work usually ends up running two tools and keeping the join in their head.

Keeps the prompt
The wording, the model and the settings that produced the file are stored with the file and survive it being copied, converted and sent on.
Keeps the versions
Every iteration stays attached to the one before it, so the third attempt can still be read against the first without anyone renaming files.
Carries the approval
A named person outside the team said yes to a specific version, at a specific time, and that decision is part of the record rather than an email.

At a glance

Who covers which job

Coverage is written as a word rather than a tick, because partial is the honest answer more often than yes or no.

Behind The Workflow

Production record for AI creative work

Keeps the prompt

Yes

Keeps the versions

Yes

Carries the approval

Yes

Frame.io

Review and approval for video teams

Keeps the prompt

No

Keeps the versions

Yes

Carries the approval

Yes

Air

Digital asset management for creative teams

Keeps the prompt

No

Keeps the versions

Yes

Carries the approval

Yes

ComfyUI

Node-based generation on your own machine

Keeps the prompt

Yes

Keeps the versions

No

Carries the approval

No

Langfuse

Prompt management for engineering teams

Keeps the prompt

Yes

Keeps the versions

Partly

Carries the approval

No

Tool by tool

What each one holds, and when to pick it

Every claim about a tool we do not own links to that vendor's own documentation. Every entry also names the case where it beats us, because a comparison that never does that is an advertisement.

Behind The Workflow

Production record for AI creative work

What it holds
The prompt, the model and the seed are stored next to the file they produced, iterations stay stacked as one piece of work, and a client reviews and signs off in a read-only space without an account in the main app.
What it does not set out to hold
Capture is a handover a person performs. Work arrives through the browser side panel, the ComfyUI plugin or the API broker, and nothing is read from another platform in the background.
When to choose it instead
If your team generates nothing and only reviews finished video with a client who already works in a review tool, a review tool alone is lighter and you do not need this.

Frame.io

Review and approval for video teams

What it holds
Assets are stacked vertically as versions so iterations stay together for side-by-side review, and a reviewer answers a version with Approved, Needs Work, or no response at all. Source
What it does not set out to hold
It works on the finished file. How that file was generated, meaning the prompt, the model and the settings, is not something it sets out to record.
When to choose it instead
Video review with external clients is what it was built for, and it is very good at it. If your record only needs to answer who approved which cut, this is the shorter path.

Air

Digital asset management for creative teams

What it holds
Version control stacks iterations and shows which one is current and who approved it, while status moves an asset from in progress to approved with time-stamped comments pinned to the frame. Source
What it does not set out to hold
It is a library first. It organises and finds the files a team already has, rather than recording how a generated file came to exist.
When to choose it instead
If the problem you actually have is that nobody can find last year's campaign in a large brand archive, a DAM solves that and a production record does not.

ComfyUI

Node-based generation on your own machine

What it holds
The SaveImage node writes the prompt and the entire workflow graph into the output PNG itself, as JSON in the file's text chunks, so a single image carries the recipe that made it. Source
What it does not set out to hold
That record lives inside one PNG. Convert the file to JPEG, run it through a resizer or hand it to a client, and the embedded JSON does not travel with it. There is no shared history and no place for a client to respond.
When to choose it instead
A solo operator who keeps every original PNG and never converts them has a complete record already, at no cost and with no second tool.

Langfuse

Prompt management for engineering teams

What it holds
Every edit to a prompt creates a new immutable numbered version, and labels such as production point at one specific version, so rolling back a live prompt is a label change rather than a code change. Source
What it does not set out to hold
The versions it keeps are versions of prompts, not of pictures or cuts, and the people it is built for are engineers shipping a feature rather than a client signing off a campaign.
When to choose it instead
If you are shipping an LLM feature and the thing you need to roll back is the prompt running in production, this is the right category of tool and a creative production record is not.

Questions

The things people ask next

Why does almost no tool cover all three?
The two halves grew up in different places. Review and approval came out of video post-production, where the file is finished before anyone looks at it, so how it was made was never part of the job. Prompt versioning came out of software engineering, where the artefact is text and nobody outside the team approves anything. AI production is the first workflow that needs both at once.
Does the EU AI Act require me to keep this record?
No. Article 50 has applied since 2 August 2026 and creates transparency and disclosure duties for certain generated or manipulated content. It does not prescribe a document and there is no such thing as a mandatory AI production log. What it means in practice is that you have to be able to say what was AI generated and how, and a record you kept anyway is what makes that answerable in a minute rather than a week.
Can any of these prove which reference image the model actually used?
No, and be careful with any tool that says it can. What a record can honestly show is that a specific file and a specific prompt were present together at a specific time, and that neither has been altered since. What the model internally took from a reference is not observable from outside the model, so nobody can attest to it, ourselves included.
Is a folder structure and a naming convention not enough?
It works while one person does the work. It stops working at the first handover, because the convention lives in that person's head and the prompt lives in a chat window that gets cleared. The question that breaks it is always the same one, asked weeks later by someone who was not in the room.

Sources

Where these claims come from

Checked against each vendor's official documentation on 16 August 2026. If one of these pages has changed since, the claim above it is the one that needs correcting.

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