# Digital asset management software comparison: what actually separates them > A digital asset management software comparison built from feature grids will not tell you much… Canonical URL: https://vivu.ai/guide/digital-asset-management-software-comparison A digital asset management software comparison built from feature grids will not tell you much, because nearly every product in the category will tick nearly every row. The differences that decide the outcome are structural: which class of product it is, who has to maintain it after launch, what your files look like going in, and whether search is the job you are buying it for. Sorting the market into classes first makes the rest of the comparison tractable. ## The classes, and which products sit in them Marketing and brand DAM is the largest class, built around distributing approved assets to people outside the creative team. Bynder, Brandfolder, Canto, MediaValet and Acquia DAM sit here. Enterprise content suites are a class of their own, where the asset library is one module of a larger platform, and Adobe Experience Manager Assets is the obvious example. Broadcast and production MAM grew up around video workflows and storage rather than around brand distribution, and iconik, CatDV, Dalet and axle ai belong to that group. Creative review and collaboration platforms such as Frame.io are a fourth thing again, organised around work in progress rather than around a finished library. Developer-facing media infrastructure like Cloudinary is a fifth, aimed at delivery rather than at people browsing. Comparing a product from one class against a product from another is where most evaluations go wrong, and [putting two products from different classes side by side](https://vivu.ai/guide/how-does-iconik-compare-to-frame-io) usually reveals a shortlist that was assembled by keyword rather than by need. If the class distinction is new to you, [the DAM and MAM split](https://vivu.ai/guide/what-is-the-difference-between-a-dam-and-a-mam) is the thing to settle before drawing up any list at all. ## Who owns it in month seven This is the axis nobody writes into the RFP and everybody feels a year later. A DAM is a system with an administrator, a taxonomy that drifts, permissions that need changing and an ingest routine that someone has to run or supervise. In-house creative teams often discover that the person maintaining it is the same producer who was hired to make videos, and that the maintenance is not a small part of their week. Compare products on how much ongoing human attention the model assumes. A system whose value depends on people tagging things correctly at upload has a different long-run cost from one that does not, and that difference will outweigh most of the feature grid. ## What your files look like going in The migration is the project. Everything else is configuration. Ask what happens to a decade of folders named by shoot date and client, whether the existing structure survives, and whether anything is searchable before the tagging work is done. If the honest answer is that the library becomes useful only after a metadata effort, that effort is part of what you are buying, and it should be scoped before you sign. ## If search is the actual reason Many of these evaluations start because somebody could not find a video. That is worth separating out, because finding a specific moment inside footage is a different problem from cataloguing files, and it is only partly addressed by [automatic tagging](https://vivu.ai/guide/what-software-can-automatically-tag-video-files), which describes a clip as a whole rather than telling you where in it something happens. A retrieval layer answers the second question instead. [Vivu](https://vivu.ai/platform) takes that approach with a deliberately narrow trade. Footage you want to search gets uploaded and indexed next to the library you already have, and Vivu covers finding; the rights, versioning and distribution work a DAM is bought for stays with the DAM. ## When you should not buy one Under roughly a few thousand assets with a team that fits in one room, shared cloud storage with a naming convention that people actually follow will outperform a DAM, because the DAM's overhead is fixed and its benefit scales with size and with headcount. The inflection point is usually not volume alone. It is the arrival of people who need assets but were not there when they were made. ## What to do with your shortlist Take your three candidates and check whether they are in the same class. If they are not, the comparison is not real and you should first decide which class you need. If they are, stop comparing features and compare the two things that vary most: what the migration demands from you, and whose job it becomes afterwards. Those answers are usually available in a discovery call and they will separate your shortlist faster than a scored matrix ever does. ## FAQ ### Do we need a DAM if all our files are already in cloud storage? Cloud storage solves access. A DAM is bought for the things storage does not do: controlling who can use which version of an asset, tracking when usage rights expire, and giving people outside the creative team a way to find approved material without asking someone. If nobody outside your team requests assets, nothing you produce is rights-restricted, and versions are not a recurring source of mistakes, storage plus a naming convention will hold for longer than most vendors will tell you. When one of those three conditions changes, the case changes with it. ### Who should own the DAM after it launches? Someone with a name, and that name should be decided before purchase. Systems bought as shared infrastructure with no designated owner drift within a year: the taxonomy fragments, uploads stop being tagged, and people go back to asking a colleague for files. The realistic options are a creative operations or production role that absorbs it as an explicit part of the job, or an IT owner paired with a creative lead who owns the taxonomy. What does not work is assuming the vendor's onboarding covers it, because onboarding ends and the drift starts afterwards. ### Can we run a DAM comparison with a feature checklist? You can build one, but expect it to come back nearly tied, because mature products in the same class converge on the same feature set. A tied matrix pushes the decision onto price and onto whoever gave the better demo. More useful is to write down three real requests your team received last quarter, the awkward ones with vague descriptions and unclear dates, and ask each vendor to walk through exactly how someone would fulfil them. The differences show up immediately in the number of steps and in who has to take them. ### How much does the metadata work cost us before the system is useful? That depends on what state your files are in, and it is the number most likely to be missing from your business case. If your archive already has consistent naming and structure, the mapping work is real but bounded. If it is a decade of folders named by shoot date, someone is deciding what each asset is called, and that is human time measured in weeks or months. Ask each vendor what is findable on day one, before anyone has tagged anything. The gap between that answer and the fully tagged state is the size of the project you are actually approving.