VPC & On-Prem Model Hosting
Deployment of open-weight models (Llama, Qwen, Mistral-class and others) or licensed models with private endpoints, inside your cloud account or physical data center, with no data leaving your network boundary.
Service
Private AI deployment
Run large language models and retrieval infrastructure inside your own cloud account or data center, with the access controls, logging and data residency your compliance team already requires from every other system.
What it is
Most LLM products are built around a public API: your data leaves your network, goes to a third-party model provider, and comes back with an answer. For a lot of finance workloads, such as anything touching account data, KYC documents, credit files or non-public deal information, that is not a starting point your compliance or security team will sign off on, no matter how good the model is. Private AI deployment inverts that: the model runs where your data already lives.
We deploy open-weight and licensed models inside your VPC, your on-prem data center, or a dedicated private cloud tenancy, alongside the retrieval infrastructure, vector store and orchestration layer the system needs. Access control, audit logging and network isolation follow the same policies as the rest of your infrastructure, because the model is now just another workload inside it, not an external call. We also handle the part that gets skipped in most private-deployment projects: GPU capacity planning, model versioning, and a monitoring layer that tells you when the model has drifted or a dependency needs patching.
This is not a one-size answer. Some workloads genuinely need a frontier model, and a carefully scoped, logged API call is the right trade-off; others need nothing more than a smaller open-weight model running on hardware you already own. Part of the engagement is deciding which is which, backed by an evaluation that compares options on your data rather than a benchmark leaderboard.
What we build
Capabilities inside Private AI Deployment
02
GPU Capacity Planning
Right-sizing inference infrastructure against your expected load and latency targets, so you are not overpaying for idle GPU capacity or hitting a wall at quarter-end volume.
03
Model Selection & Evaluation
A structured comparison of candidate models against your own eval set on accuracy, latency and cost per call, rather than a public benchmark that may not reflect your documents or workload.
04
Access Control & Audit Logging
Every inference call logged with the prompt, response, model version and requesting identity, wired into your existing SIEM or logging stack for the same retention and review your other systems already get.
05
Data Residency & Isolation
Deployment architectures scoped to your jurisdiction's requirements, whether that is EU data residency under GDPR, a specific national on-prem mandate, or a client contract that specifies where data may physically sit.
06
Model Versioning & Rollback
A controlled release process for model updates, with the ability to pin a version for a regulated workflow and roll back if an update changes behavior in a way your validation catches.
07
Ongoing Monitoring & Drift Detection
Dashboards and alerting for latency, cost, and output drift, so a quietly degrading model gets caught by a metric, not by a client complaint.
How we work
Delivery process
01Requirements & constraints
Confirm data residency requirements, expected load, latency targets and which existing infrastructure the deployment needs to sit inside.
02Model evaluation
Benchmark 2-4 candidate models against your own eval set for accuracy, latency and cost, not a generic leaderboard.
03Infrastructure build
Stand up the hosting environment, vector store, orchestration layer and logging pipeline inside your network boundary.
04Security & access review
Wire access control and audit logging into your existing identity and SIEM stack, and walk your security team through the architecture before go-live.
05Deployment & handover
Go live with monitoring in place, plus documentation and runbooks so your infrastructure team can operate and update the deployment without us.
What to expect
100% in‑perimeter
inference traffic stays inside your VPC or data center, with no third-party model API in the request path
4‑8 weeks
typical time from kickoff to a production endpoint serving real traffic
2‑4 models
evaluated against your own data before one is selected for deployment
Frequently asked questions
Does this mean we can't use frontier models like GPT-5 or Claude at all?
No. Some workloads justify a frontier model behind a carefully scoped, logged API call with a data-processing agreement in place, and we'll say so where that is genuinely the better trade-off. Private deployment is for the workloads where that boundary is not acceptable to your compliance function, which in our experience is most of what touches account-level or non-public data.
What hardware do we need for on-prem deployment?
It depends on model size and load. Smaller open-weight models can run on a handful of modern GPUs; higher-throughput workloads need more. Part of the engagement is right-sizing this against your actual traffic instead of over-provisioning against a worst case that never arrives.
How do you handle GDPR and EU AI Act obligations for a deployed model?
Private hosting removes the cross-border transfer question GDPR raises with most public APIs, since data never leaves your environment. For EU AI Act purposes, we document the system's risk classification and the human-oversight and logging controls it needs, whether that's a high-risk use case like credit scoring or a lower-risk internal tool.
Can you deploy inside our existing SOC 2 environment?
Yes. We build inside whatever control environment you already operate, whether that's SOC 2, ISO 27001, or an internal framework, and align logging, access control and change management to those existing controls rather than introducing a parallel set.
What happens when the model provider releases a new version?
Nothing, until you decide it should. Because you control the deployment, model updates are your call, validated against your eval set before anything changes for a production workflow.
Explore more
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Industries we apply this in
Talk to us about Private AI Deployment
A 30-minute call to scope what a first version would look like against your own data and systems.
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