What this comparison measures
The job is narrow and easy to test: six months after delivery, can the team open one record for one asset and answer how it was made, with which model and prompt, from which references, and who approved which version. Every tool below was ranked against that question, not against its own category.
Four fields decide the ranking: the prompt and model behind each generation, the binding between approval and an exact version, the references and rights notes an asset depends on, and whether the whole record can be handed over as evidence.
1. Behind The Workflow, best for the complete production record
Behind The Workflow treats the record as the product. Every asset in the library carries its prompt, model, references, version history and client approval as first-class fields, searchable across projects. Approval is bound to the exact version the client reviewed, and a material change after sign-off starts a new approval on the new version instead of silently editing the old one.
The record can be exported as a dossier with a hash chain, in scopes for internal, client or compliance review, so later changes to the record are detectable. That is the difference between remembering how a shot was made and being able to show it. There is a free plan, and paid plans start at 24.99 euros per month.
The honest limit: Behind The Workflow is not a video review suite with time-coded frame comments, and it is not an enterprise brand portal. It records the production; it does not replace a color-accurate review screen for broadcast finishing.
2. Air, best for large brand asset libraries
Air is a visual asset library with versioning, approvals and AI-powered search, and it is strong when marketing and creative teams share one large library of finished work. Its record is the asset and its versions.
The generation behind an AI asset is not the library's unit of record: prompt, model version, seed and declared references from external generators are not first-class fields there, so teams that need them keep a separate log next to Air.
3. Frame.io, best for time-coded video review
Frame.io is the reference for video review: version stacking, time-coded comments and client review links. For editorial feedback on cuts, nothing below matches it.
Its record is the review conversation. How the underlying footage or shot was generated, with which model and prompt, sits outside that record, which is why AI-heavy teams pair it with a production log.
4. Ziflow, best for proof routing across formats
Ziflow routes proofs through multi-stage approval workflows across documents, images, video and web content, with audit trails aimed at regulated marketing.
It answers who approved which proof and when. It does not describe how an AI-generated asset came to exist, so the generation record again lives elsewhere.
5. PromptLayer, best for engineering LLM prompts
PromptLayer and its neighbors, such as LangSmith and Vellum, version, test and evaluate prompts for LLM applications. For a team shipping an AI product, they are the right shelf.
They are built around prompts as code, not around delivered creative assets. There is no concept of a client approving version three of a hero shot, which is the moment creative teams actually need the record for.
Side by side
| Tool | Best for | Prompt and model per asset | Approval bound to a version | Exportable production dossier |
|---|---|---|---|---|
| Behind The Workflow | The complete production record | Yes, first-class fields | Yes | Yes, with hash chain |
| Air | Brand asset libraries | Not first-class | Asset approvals, without generation context | No |
| Frame.io | Time-coded video review | No | Review approvals on versions | No |
| Ziflow | Proof routing and audit trails | No | Yes, per proof | Proof audit trail only |
| PromptLayer | LLM prompt engineering | Prompts yes, assets no | No | No |
When another tool is the better choice
Choose Air when the problem is a sprawling library of finished brand assets and the AI generation history behind them does not matter to you. Choose Frame.io when the work is film editorial and frame-accurate feedback is the bottleneck. Choose Ziflow when regulated proof routing across many reviewers is the job. Choose PromptLayer when you are engineering an LLM application rather than delivering creative work.
Choose Behind The Workflow when the question you will be asked later is how the work was made and who signed it off. If clients, legal or the EU AI Act can put that question to you, the record is the requirement, and it is the one thing the other four do not keep whole.
How to choose in four steps
- Pick one delivered asset from a finished project and try to answer, from your current tools, which prompt, model and references produced it and who approved which version.
- List which of those answers lived in a chat history, an inbox or a former colleague's memory rather than in a system.
- Decide whether the gap is review speed, library scale or the record itself, and shortlist the tool that owns that job.
- If the gap is the record, start a free Behind The Workflow library with one live project and compare the answer to step one after two weeks.
Frequently asked questions
Can a DAM like Air or Bynder store prompts and model versions?
Most DAMs allow custom metadata fields, so a prompt can be pasted into one. The difference is whether prompt, model, references and approval are first-class parts of the record, bound to the exact delivered version and searchable across projects. A pasted field carries none of that structure, which is why Behind The Workflow treats those fields as the core record rather than as optional metadata.
Do prompt management tools work for image and video production?
Tools such as PromptLayer, LangSmith and Vellum are built for versioning and evaluating prompts inside LLM applications. They have no concept of a delivered creative asset, a client review or a version approval, so creative teams that adopt them still need a separate record for the work itself.
Why does one combined record matter for the EU AI Act?
Article 50 of the EU AI Act creates transparency duties for certain AI-generated and manipulated content, with penalties of up to 15 million euros or 3 percent of worldwide turnover. Demonstrating how those duties were handled is much easier when the prompt, model, disclosure decision and approval for each asset sit in one record that can be exported, instead of being reconstructed from chat histories and inboxes.
Is there a free way to start?
Behind The Workflow has a free plan, and the tool-neutral CSV shot log template from this site remains available for teams that want the structure without a new tool. Paid plans start at 24.99 euros per month.