The question has moved from whether to adopt AI to how. A dedicated environment gives your model risk, vendor risk and security teams one boundary to review, scoped to your data, your use case and your regulator before anything is provisioned.
Every service is single-tenant and isolated. What differs is how much of the stack Sandlake runs for you.
Lease capacity and run your own stack on bare metal.
Sandlake fine-tunes a model on your data alongside your team and keeps it current. You own it.
Add metered inference capacity with no new infrastructure commitment.
Want one partner end to end? Take the full stack in a single environment. Pricing is scoped per engagement; request a proposal for indicative rates.
Define the use case, the risk tier and the data policy, and build your own test set.
Sandlake and your team train and fine-tune the model on your data inside the dedicated environment. Results are documented for model risk review.
You own the model and its weights. Sandlake runs it through Token-as-a-Service, monitors it and retrains it as your data changes, or your team takes it in-house.
No other customer's workload ever shares your compute, storage, or network.
The tuned model and its weights stay with you. No data is used to train a shared or public model.
Inference endpoints are reachable only from inside your network or VPN, not the open internet.
Logical separation between projects by default, with hard segmentation for workloads that need an internal wall, such as information barriers.
Validation results are produced during the engagement and documented for your model risk team, not retrofitted afterwards.
Hosted in North America or the EU, so data stays in the jurisdiction your regulator expects.
Denied-party and KYC screening on every customer before deployment.
Fine-tuning and inference run entirely in the dedicated environment. Nothing is routed through a third-party model API.
Your prompts, documents and model weights stay in one dedicated environment instead of passing through a third-party provider. That removes the data sovereignty and third-party vendor risk questions at the source.
Mid-size banks, payment processors, and credit unions that hold sensitive data and need AI inside their own governance, without matching the largest banks' AI investment.
Your team knows your data and how to govern a model. Large models add new work: GPU infrastructure, fine-tuning and serving at scale. With Platform-as-a-Service, Sandlake does that work alongside your team. If your team already works with large models, GPU-as-a-Service gives it bare-metal access to run its own stack.
You do. The tuned model and its weights belong to you and are never reused for another customer. Sandlake can run and maintain it, or your team can take it in-house.
Validation evidence is produced as part of the engagement and documented for model risk review, so your model risk team reviews results alongside the build, not after it.
GPU-as-a-Service is a contracted GPU-hour rate. Platform-as-a-Service is a setup fee per use case plus an annual fee to keep the model monitored, retrained and ready for review. Token-as-a-Service is billed per token. Everything is scoped per engagement.
North America or the EU: you choose the region your environment and your data are hosted in. See Infrastructure for detail.
It depends on the use case, the data and your review process. We give you a real timeline at scoping.