If the answer is "not until we're sure it can't leak to a model provider," a dedicated pod is built for exactly that constraint. It's scoped to your data, your workload, and your security requirements before anything is provisioned.
Every tier is single-tenant, isolated hardware. The difference is scale and dedicated capacity, not the isolation guarantee.
A single dedicated GPU environment sized for one fine-tuning or inference workload against one dataset.
Dedicated capacity for concurrent projects, logically separated by default, with hard segmentation available for workloads that need an internal wall, not just isolation from other Sandlake customers.
Larger dedicated capacity serving multiple workloads across the organization, still isolated from every other Sandlake customer.
Pricing is scoped per engagement based on GPU count, storage, and term; request a proposal for indicative rates against your workload.
We review the workload, the data classification, and any compliance requirements to size the pod correctly.
Dedicated hardware is isolated and provisioned, network, storage, and compute scoped to you alone.
Load your data, fine-tune the model, and connect it to internal tools via a private endpoint.
No other customer's workload ever shares your compute, storage, or network.
Your fine-tuning data and prompts are never used to train a shared or public model.
Fine-tuning and inference run entirely on the dedicated pod, nothing is routed through a third-party model API.
Inference endpoints are reachable only from inside your network or VPN, not the open internet.
Denied-party and KYC screening on every customer before deployment.
Hosted in North America or the EU, you choose the jurisdiction your data sits in.
You get dedicated, single-tenant hardware instead of a shared endpoint. Your prompts and training data never leave the pod, are never logged by a third party, and are never used to train anyone else's model.
Any enterprise with proprietary data or internal know-how it doesn't want exposed to a public model provider, regardless of industry or which team owns the workload.
You bring or select an open-weight base model, and we fine-tune it on your data inside your dedicated pod. We don't train a model that's shared across customers.
Based on dedicated GPU count, storage, and term, scoped per engagement, not a shared, metered API rate.
North America or the EU, you choose the region your pod and your data are hosted in. See Infrastructure for detail.
Depends on size and current capacity, single-workload pods move faster than larger multi-workload or enterprise deployments. We'll give you a real timeline at the scoping stage.