For some organisations, sending data to a hosted model is simply not an option — because of regulation, a customer contract, data-residency rules, or an internal policy that predates the question. Private and on-premise AI deployment means running the models and agents inside an environment you control.
It is entirely achievable. It also involves real trade-offs, and we would rather set those out before a project starts than discover them halfway through.
Deployment Options
- Fully on-premise — models run on your hardware; nothing leaves the building. Maximum control, highest infrastructure commitment.
- Private cloud / VPC — dedicated infrastructure in your own cloud tenancy, isolated from shared services.
- Sovereign or region-locked — hosted models constrained to a specific jurisdiction for residency requirements.
- Hybrid — sensitive retrieval and data stay private, while non-sensitive workloads use hosted models. Often the pragmatic answer.
The Trade-Offs, Stated Plainly
Self-hosted open models have improved enormously, but the strongest frontier models are generally still hosted. Running privately means accepting some capability difference, taking on GPU infrastructure and its cost, and owning the upgrade path yourself. In exchange you get complete data control and no dependency on a third party’s availability or policy changes.
For many organisations the right answer is hybrid: keep the retrieval layer and the sensitive documents private, and be deliberate about what — if anything — is allowed to reach a hosted model. We will help you draw that line rather than treating it as all-or-nothing.
What We Deliver
Requirements and constraint analysis, model selection for the deployment target, infrastructure and inference architecture, private retrieval, agent and MCP layers inside the boundary, evaluation, and operational handover to your team.
Tell us what your constraints are — regulatory, contractual or policy — and we will design a deployment that meets them without pretending the trade-offs do not exist.
Related services
- MCP Server Development — Related: MCP server development.
- AI Agent Development — Back to the AI agents overview.
Which Constraint You Actually Have
The four constraint types do not behave the same way, and the design changes depending on which one is actually driving the request. A regulation or a data-residency rule is fixed: you design to it, and the useful detail is that such rules usually govern where data rests and which borders it crosses rather than which model produces the answer, so they constrain infrastructure more than they constrain architecture. A customer contract clause is also fixed for its term, but it is often narrow in scope, covering one customer's data rather than your whole estate, which sometimes makes segregating that data a better answer than moving everything. An internal policy is the one type that can be changed, so the first question is whether it should be, and that is a decision for the policy owner rather than for us.
It is worth settling separately what the constraint attaches to, because these get treated as one thing and are not: the data itself, the network path it travels, the jurisdiction where processing happens, or the model weights and who supplied them. A rule about where personal data may be stored and a rule about which vendors may process it point toward different deployment targets. A project that quietly applies the strictest reading of both ends up buying infrastructure it did not need.
Private deployment is also not the right response to every case where it gets requested. Three situations where a smaller change is the better one:
- No one can name the specific rule. If the requirement traces back to general unease rather than a written obligation, private infrastructure will not settle it, and the unease usually resurfaces later as a question about what the system is allowed to do, which private hosting does not answer.
- The sensitive material is a small, well bounded set. Keeping it out of the retrieval layer, or holding it in a separate index with its own access rules, is a narrower change than moving everything, and it is easier to keep true as the estate grows.
- The workload is occasional and low volume. Dedicated inference capacity spends most of its life idle and still has to be patched, monitored and upgraded, and that is paid in staff attention whether or not anyone is using the system.
The Boundary Is Not the Same as Access Control
Private deployment answers the question of where data can go. It does not answer who inside the boundary may see what, and the largest share of the design effort usually goes there rather than into the hardware. Retrieval is where it surfaces first: an index built by crawling a document store flattens the source system's permissions unless it is deliberately built to carry them, and once flattened, anyone who can query the system can reach anything in the index. The two workable approaches are filtering at query time against the caller's identity, or partitioning indexes by audience, and they differ in query latency, in how much maintenance they need as groups change, and in how quickly a revoked permission actually takes effect.
The agent and MCP layers make this sharper, because they act rather than answer. A connector holds credentials to a system of record, and if it holds a single shared service account, every user of the agent inherits that account's rights regardless of their own. Binding a connector to the calling user's identity is more work and is normally the right choice for anything that reads restricted records or performs a write. Approvals for write actions belong in the connector layer, not in the prompt: an instruction telling a model not to do something is not an access control and should never be the only thing between a user and a change in a production system.
Logs and evaluation data need the same treatment as the documents. Prompt and response logs are a second copy of sensitive content, and they are the copy most likely to be shipped into a monitoring stack that was never scoped to hold it. An evaluation set has the same property from the other direction: it is a curated collection of the questions that matter most and the documents that answer them. Both need retention rules, access rules and a named reader group, decided during design rather than after the first audit asks.
What You Own After Handover
Capacity is the part that shows up soonest. Inference capacity is sized by concurrency and context length, not by headcount: a workload that reads long documents and returns summaries consumes far more of the same hardware than one answering short questions for the same set of users, and scheduled batch work competes with interactive use for it. Sizing against expected peak concurrency, and deciding in advance what happens when it is exceeded (a queue, a slower response, or a fallback), is more useful than sizing against a user count.
Model changes are the part that gets underestimated. New open model releases arrive on their own schedule, and a swap changes behavior even when the published scores are better, because prompts, tool descriptions and retrieval settings get tuned to one model's habits. Without an evaluation set built from your own questions and your own documents, there is no way to answer whether an upgrade helped, and the practical result is that the system freezes on whichever version happened to work. The evaluation set is what keeps that decision open.
Name an owner before go-live rather than after. Someone has to be accountable for capacity, for the permission model as groups and documents drift away from what was designed, and for deciding when a model version changes. Handover to a team without a named owner is where a private deployment quietly stops being maintained, and that is rarely visible until an answer is wrong in a way that matters. If nobody in the organization wants that responsibility, it is a real argument for a hosted or hybrid shape, and better established before hardware is specified.
Frequently Asked Questions
What is a private AI deployment, concretely, and what is it not?
It is an engagement that ends with a running system your own team operates, not a product you license from us and not a fixed package. It is also not a hosted application with the endpoint swapped for a private one, because the model call is rarely the only path data takes: the retrieval index, the logs and the connectors into your systems of record are usually where content actually accumulates or leaves. A model installed on a server with no retrieval, no authorization model and no way to measure whether its answers are right is a demonstration rather than a deployment.
Does this mean the models have to run on our own hardware?
Only in the fully on-premise shape. Running on your own hardware means running models whose weights you can obtain and license, which is a different selection problem from picking an API. That narrows the field before answer quality is even discussed: available GPU memory, the context length the workload needs, the concurrency you have to support and the license terms attached to the weights each rule candidates out, and what is left is usually a short list rather than an open choice.
What has to already exist before this can start?
An identity source that already reflects who works where and who may see what, because the deployment should inherit those groups rather than invent a parallel set. Data sources with named owners who can grant access to real content, not sanitized samples, since document quality and permission gaps only appear in the real corpus. Somewhere for inference to run, which for a fully on-premise target means either capacity you already have or a procurement path you can actually use. If those are missing, the honest first step is a scoped design engagement rather than a build.
How does a rollout proceed, and why in that order?
The order follows dependencies rather than preference. Constraint work comes first because it eliminates options rather than ranking them, and nearly every later decision changes depending on which deployment target you are building for. Retrieval is settled before anything is built on top of it, because an agent can only act on what it can reliably find, and a narrow first corpus surfaces permission gaps and document quality problems while they are still cheap to fix. Measurement goes in before the corpus is widened, otherwise there is no way to tell whether widening helped or hurt. Handover runs alongside the build rather than after it, because the people who will operate the system need to have seen it fail at least once.
How does it integrate with the systems we already run?
Through the retrieval layer for content, and through MCP connectors for anything the system needs to read or do inside a system of record: document stores, ticketing, line of business databases, internal APIs. Those connectors run inside the boundary and hold their own credentials, which is the point where integration becomes an access question rather than a plumbing question. The practical limit is usually the source system itself, since what its API exposes about permissions determines whether a connector can act as the calling user or only as a service account. Read paths are normally built and stabilized before any write path is enabled.
What happens to our data, and what stays under our control?
Source documents stay where they are. The retrieval layer builds a derived index, and that index is a second copy of the content living inside the boundary, so it needs the same retention and access rules as the originals. Prompts, retrieved passages and responses are logged, which makes the log sensitive material rather than ordinary operational telemetry. Nothing you run privately trains a model unless someone deliberately builds that step, and fine tuning is a separate decision with its own data handling questions.
What do we have to decide, as opposed to what you build?
Which data classes exist and who may see each one, because that drives the permission model and cannot be inferred from the documents themselves. What the system is allowed to do without a person approving it, per workload, which is the difference between a system that drafts and one that acts. What a wrong answer costs in each workload, since that sets how strict the evaluation thresholds are and where a human stays in the loop. And who owns the system after handover, by name, with the authority to approve a model change.
Can we start hybrid and move fully on-premise later?
Yes, if the system is built against interfaces from the start rather than against one vendor's endpoint. Retrieval, the permission model, the connectors and the orchestration around them carry across deployment targets with little change, because none of them depend on who serves the model. What does not carry cleanly is behavior tuned to a particular model: prompts, tool descriptions and retrieval settings adjusted to one model's habits usually need retuning. That is the reason the evaluation set built in the first phase matters, since it makes the second phase measurable instead of a matter of opinion.