Private AI Platform Development Services

Private AI means running AI on infrastructure whose trust boundary you define, so your prompts, documents, and model outputs stay inside it. In practice that covers four topologies: your own datacenter, a virtual private cloud, a sovereign cloud in a chosen jurisdiction, and a fully disconnected air-gapped network. They differ by who can legally compel access to the data, which makes the choice a legal question more than a technical one.

The term gets used loosely. A vendor saying "private" sometimes means a shared service with a contractual promise, which is a different guarantee from inference that physically never leaves your building.

Hardware is the smallest part. Published market ranges put a capable single-GPU workstation at roughly $1,500 to $4,000, a build that handles 32B-class models under $12,000, and a 100B-class local build between $15,000 and $25,000. An NVIDIA H100 sits around $25,000 to $40,000 per card, and hosted GPU instances start near $479 a month. The cost that decides the business case is people: industry figures put ongoing MLOps and infrastructure staffing at 0.5 to 2 full-time engineers, or roughly $75,000 to $300,000 a year.

Put that against hosted API pricing and the picture usually inverts. At publicly listed rates a mid-tier hosted model costs a few dollars per million tokens, so a 200-person internal assistant running around 8 million tokens a day lands in the low thousands per year on a hosted API against roughly eighty thousand for a self-hosted equivalent once you include half an engineer. We do not publish a DBB price band for the platform itself, because it depends entirely on topology and scope. If you want a fixed number before committing to a build, the paid Discovery Sprint starts at $1,777 and produces a costed plan.

When the driver is cost rather than a requirement. The token arithmetic favours hosted APIs by roughly an order of magnitude at typical mid-market volume, and it keeps favouring them after you add data-processing agreements and zero-retention terms. Going private to save money works only at sustained high volume with hardware you can keep saturated and the operations capability already in-house.

Three cheaper options are worth exhausting first: zero-retention terms on a hosted API, renting the same open-weight model from a host rather than running it, and a regional endpoint for data residency at roughly a ten percent premium. If one of those closes your requirement, we will tell you.

Only an air-gapped deployment gets you there, and it is expensive to run. Everything short of that is a spectrum: a VPC deployment keeps data inside your cloud account but still runs on someone else's hardware, and a hosted API with zero-retention terms is a contractual guarantee rather than a physical one.

The useful question is which layer has to be private. Many teams need their documents and credentials to stay internal while the model itself can be hosted, and that is a much smaller build than a full platform.

Not necessarily. Three routes work: rent GPU capacity from a specialist cloud, colocate hardware you own, or install in your own datacenter. Renting removes the capital cost and the refresh cycle, and it is where most first deployments should start.

Size against peak concurrency rather than daily token volume. A GPU provisioned for twenty simultaneous users costs the same whether those users send one million or fifty million tokens a day, which is why token-based sizing tables mislead. If demand is bursty, hardware sized for the peak sits idle most of the day and the real cost per token climbs sharply.

With an evaluation set built from your own work before anything ships. Public leaderboards measure general capability, not whether a model handles your contracts, your vocabulary, or your edge cases, so we build a task-specific set with acceptance thresholds agreed up front and treat go-live as a gate rather than a date.

We run this on ourselves. DBB benchmarked ten models on our own production chatbot with our real system prompt and tool catalogue, and the results contradicted the marketing benchmarks in ways that would have shipped a broken product. One model silently failed tool-calling entirely while its older sibling succeeded.

Your team, which is the point of how we hand over. You receive Terraform, Helm charts, runbooks, and a recorded walkthrough, so operating the platform does not depend on a standing retainer with us. Where a client prefers it, we stay on for managed operation.

Be realistic about the load. Published accounts of air-gapped deployments describe six to seven months to production and an operations team roughly two to three times larger than a connected deployment, covering certificate management, registry mirrors, and release engineering. A VPC deployment is a fraction of that.

You do. Everything built during the engagement, including fine-tuned weights, evaluation sets, and infrastructure code, is yours. Open-weight base models carry their own licences, and we check the terms against your intended scale before recommending one, because some licences change obligations above a user threshold.

A working private deployment in about a week and a governed production platform in about a month, for cloud and VPC targets. That covers the serving layer, the gateway, retrieval over a first data source, and identity integration.

On-premise and air-gapped timelines are different, and anyone quoting you a month for an air-gapped install is guessing. Accreditation, registry mirroring, and staged installs dominate the schedule there.

AI Development covers building and fine-tuning custom models and LLM applications wherever they run, including on hosted APIs. This page covers the platform underneath: where inference happens, who is allowed to call it, what gets logged, and how your team operates it.

If you are adding AI to a product on hosted models, start with AI Development or AI Integration. If the requirement is that inference and data stay inside a boundary you control, start here.

Both are components of a private platform and both are also standalone engagements. RAG Development Services covers retrieval accuracy and permission-aware search as the primary deliverable, which is often all a team needs when only the documents are sensitive. AI Agent Orchestration covers multi-agent workflows and can run cloud or on-premise.

This page owns the layer beneath both: the serving infrastructure, the governance gateway, identity, and audit. For the infrastructure and pipeline work around it, see IT Infrastructure and DevOps.

Yes. We deploy to EU-hosted infrastructure including OVHcloud, Scaleway, and Hetzner, as well as on-premise clusters in a chosen jurisdiction, and we distinguish data residency from data sovereignty when the two diverge. Residency is where bytes sit; sovereignty is whose law can compel access.

EU AI Act obligations became binding on 2 August 2026, which changes documentation and oversight requirements for a number of deployments. For US healthcare work, HIPAA Compliant Software Development covers the compliance engagement alongside the platform build.