Private deployments start with a conversation.

It depends which offer you run, and the difference is worth understanding before you choose. deeplit® Private runs entirely on your hardware, with no runtime dependency on deeplit® infrastructure. No phone-home, no cloud license check, no service the platform calls to keep working. If deeplit® disappeared tomorrow, your deployment would keep serving models until the hardware did. A source-code escrow arrangement is available as part of the commercial engagement; ask for it during scoping. deeplit® Cloud runs on servers we operate. If deeplit® stops operating, those instances stop with it. That is the trade for not buying hardware, and we would rather say it than let you find out. The models are open-weight and the work is yours, so what you built is portable. The running service is not. If that risk is unacceptable to your organization, deeplit® Private is the answer, and we will tell you so.

Yes. Your deployment continues to run under the terms of the platform license. What stops is new platform releases and the support relationship. The models, the infrastructure, and every fine-tuned artifact stay where they are. Leaving is an operational decision, not a migration project.

No. On deeplit® Private, training, indexing, and inference all run on your hardware. The fine-tuned weights, the training data, and the retrieval index stay on your infrastructure. On deeplit® Cloud they run on a single-tenant instance nobody else shares, your data and your fine-tuned weights stay yours, and we do not train on your data. Either way, deeplit® has no read access to your training pipeline by default and no telemetry that includes your data. Aggregated performance metrics surfaced back to the deeplit® team for support are opt-in, and the scope is yours to define.

No. deeplit® includes the MLOps layer that teams usually have to build themselves. That covers model lifecycle, rollback, monitoring, drift detection, and the offline update path. Your team interacts with the platform through configuration, not through Python notebooks. If you have an ML team, the platform removes the infrastructure work and frees them for models and applications. If you do not, our engineers handle deployment and support; running the platform in production does not require ML expertise on your side.

If you are a small organization with light AI usage, deeplit® is not the right fit. A public AI API is cheaper and simpler at that stage. Come back when the bill, the rules, or the risk has started to hurt. Two places where the math works. First, data-sensitive organizations. The legal, contractual, or reputational cost of a token leaving your perimeter outweighs the convenience of a cloud API. Second, organizations running high-volume analysis on unstructured documents, records, transcripts, or archives. Per-token cloud pricing stops being a line item and starts being a reason to kill the project. deeplit® turns runaway operating cost and compliance tension into a fixed, owned platform.