Use cases
Who needs local storage and local intelligence
Five verticals with the same shape: a large owned multimodal corpus, and a real reason they cannot simply call a public cloud API on it.
When is an on-premise AI data center worth it?
In four situations, and they are narrower than vendors on either side of the argument suggest. When the data legally or contractually cannot leave your perimeter. When the site has no reliable connectivity. When you retrain often enough that per-run cloud billing dominates. And when your read pattern is far heavier than your write pattern, which is the case for multimodal indexing because every model or pipeline change re-reads the entire corpus.
If none of those describe you, cloud object storage is almost certainly the cheaper and simpler answer, and the honest recommendation is to stay there.
01 · Studios, broadcasters, sports rights holders, post houses
Media and entertainment archives
- The problem
- Petabyte-scale video libraries that cannot be sent to a public cloud vendor for IP and licensing reasons, and are effectively unsearchable because nobody can move them.
- Why a private rack answers it
- The object pool and the extraction sit in the same rack, so indexing a decades-old archive never crosses a licensing boundary or an egress meter. Search returns a timestamp inside a reel rather than a filename.
02 · Defense, intelligence, civil agencies and their contractors
Government and defense
- The problem
- Drone, satellite and surveillance footage that has to be processed air-gapped or on-premise, which rules out every hosted multimodal API regardless of the contract wrapped around it.
- Why a private rack answers it
- Every layer runs in the rack. The secure overlay is a client convenience rather than an infrastructure dependency, so substituting an approved on-premise access method makes the deployment genuinely air-gapped without redesigning anything.
03 · Hospital systems, imaging networks, research institutions, medical device makers
Healthcare and life sciences
- The problem
- Radiology, pathology and procedure video under strict data-residency rules, where the value of multimodal search is obvious and the transfer is not permitted.
- Why a private rack answers it
- Data residency becomes a physical fact rather than a contractual promise, because the disks are in a room you control. Domain models run locally on the same accelerator that serves retrieval.
04 · Banks, insurers, brokerages, payment processors
Financial services
- The problem
- KYC video, recorded calls and branch surveillance under compliance regimes that mandate the data never leaves customer infrastructure.
- Why a private rack answers it
- Retention, access and audit all happen inside your perimeter, and the same index that makes the corpus searchable holds the per-asset metadata a regulator will ask for later.
05 · Factories, energy, logistics, quality engineering teams
Manufacturing and industrial
- The problem
- Factory-floor video and QC imagery that is sensitive for IP-protection reasons, latency-sensitive, or both, and often generated where the bandwidth is not.
- Why a private rack answers it
- Inference happens next to the line rather than a region away, and the rack is movable, so a site with poor connectivity is a deployment target rather than a blocker.
When it is the wrong call
A rack is a fixed cost, a power draw and an operational responsibility. If your corpus is small, if it grows slowly, if you index it once and query it rarely, or if you have no one who wants to own hardware, the cloud version of this is better and cheaper and you should use it.
The same Mixpeek stack runs either way, which is the point: the decision is about where the bytes live and what reading them costs, not about which product you can use. The cost breakdown for indexing a media library is the arithmetic worth doing before deciding.