How we choose the best tool for this job
We publish Behind The Workflow, so this is our editorial recommendation, not an independent lab ranking. The comparison uses official product documentation and our own product workflow. We have not run a timed benchmark of every plan or configuration. The test we recommend is concrete: can someone who did not make a delivered asset find its prompt, model, references and version-specific approval?
Evaluate four things on the same asset: how you add generation details, how a decision relates to the chosen version, how references and rights notes stay connected, and what the recipient can actually open or export. A custom field can be useful; the deciding question is whether the complete workflow holds together for your team.
1. Behind The Workflow, best for the complete production record
Behind The Workflow is our best choice for this production-record task because the asset, generation context, version history and review decisions share one workspace. Your team adds the prompt, model and references through uploads, explicit capture or supported generation workflows. It does not silently collect the history of every external AI tool. Keep the reviewed version and subsequent changes explicit instead of treating an old approval as approval of a new export.
A dossier can package the recorded production context for handoff. Check the scope and contents of a sample export before promising a client specific evidence. The free plan is a way to try the record; plan-dependent collaboration and delivery features are listed on the current pricing page.
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.
Air supports custom fields on assets and files, including searchable and filterable text. A team can store a prompt and model there. Configure those fields deliberately and test whether the metadata and approval still identify the correct version when an asset is updated or reused.
3. Frame.io, best for time-coded video review
Frame.io is our best pick here for video review, where time-coded comments, version stacking and client review links are the main job. Start there when your bottleneck is getting a cut reviewed.
Frame.io V4 also supports custom metadata, so it is inaccurate to say it cannot store a prompt. Test how your configured fields, media versions and review decisions work together, and whether a recipient can retrieve the generation context you need to hand over.
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.
Its core job is to answer who approved a proof and when. If generation context matters too, test how your team will connect prompts, references and model details to that proof and include them in the recipient's handoff.
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 focus on prompts, runs and evaluations for software workflows. For a creative delivery, separately test media version review, reference rights and the client-facing handoff. Do not assume a prompt registry alone covers those steps.
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 | Dedicated fields supplied by your team | Yes | Dossier export; inspect scope |
| Air | Brand asset libraries | Configurable custom fields | Asset workflow; test version binding | No |
| Frame.io | Time-coded video review | Configurable custom metadata | Review approvals on versions | No |
| Ziflow | Proof routing and audit trails | No | Yes, per proof | Proof audit trail only |
| PromptLayer | LLM prompt engineering | Prompt versions and application runs | 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 expensive gap is the connection between how the work was made and which version was signed off. That is the task we design for. A client may contractually require such a record; Article 50 itself does not mandate a particular production log or make an export a substitute for disclosure.
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?
Yes. Air documents searchable and filterable custom fields, and many DAM workflows can store a prompt and model. Compare how those fields relate to references, versions, approvals and exports in your actual setup. BTW's advantage is a workspace designed around that linked production record, rather than a claim that custom metadata cannot hold text.
Do prompt management tools work for image and video production?
Tools such as PromptLayer, LangSmith and Vellum focus on prompt versioning and evaluations for AI applications. They may fit parts of a creative pipeline. Test the additional delivery requirements explicitly: media versions, reference permissions, client decisions and the package the recipient can access.
Why does one combined record matter for the EU AI Act?
Article 50 creates marking and disclosure duties for certain AI-generated or manipulated content. A combined record can help your team review what was decided and published. It is not a statutory form, does not determine whether the law applies to a particular work, and does not carry out a public disclosure by itself.
Is there a free way to start?
Behind The Workflow has a free plan. The tool-neutral CSV shot log is another starting point. Check the current pricing page for paid plans and the collaboration or delivery features your trial needs; do not assume every feature is included in Free.