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AI on infrastructure your auditors will approve.

Public AI APIs are disqualified by procurement, compliance, or contract for the workloads where AI matters most. We deploy AI in any of four configurations — private AI cloud, your existing public cloud tenancy, on-premises in your data center, or hybrid — with documented data flows and procurement-grade attestations.

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Four deployment configurations

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The problem

Public AI APIs do not survive procurement review. Healthcare, legal, public-sector, and regulated financial services workloads cannot send client data to third-party AI providers. The default architecture is disqualified at the contract stage.

Vendor lock-in is back. Building on a single foundation model provider means accepting their pricing, policy, and availability changes — with no architectural response when they happen.

Per-token pricing breaks at scale. Workloads with high query volume, large retrieval contexts, or agentic patterns produce quarterly bills that surprise finance teams. Predictable economics require a different deployment model.

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What we do

Four secure deployment configurations. You pick the one that fits your data, security posture, regulatory requirements, and economics — we document the tradeoffs in writing before we ship.

On-premises in your data center

The full Skyview AI stack — self-hosted models, vector databases, retrieval, observability — installed inside your perimeter. Air-gapped supported. We spec, procure, and rack the hardware. Right answer for sovereignty, ITAR, federal research, and air-gapped workloads.

In your public cloud (Azure / AWS / GCP)

Architected, deployed, and operated inside your existing hyperscaler tenancy — wherever you have standardized. We work within your IAM, network controls, and existing commitments, integrating with managed AI services (Azure OpenAI, Bedrock, Vertex) where appropriate.

Skyview private AI cloud

Hosted in our Tier III TierPoint colocation facilities. Capacity, availability, and operations are our problem. Right answer for a turnkey hosted environment without standing up your own AI infrastructure or competing for cloud capacity.

Hybrid configuration

A mix of the above, per workload. Sensitive data on-prem, hot path in your cloud, frontier reasoning to a public API by exception. One engagement, multiple surfaces, documented data flows across the boundaries. The right answer for most enterprise deployments.

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Built for compliance and control

Facility-level attestations

SOC 2 Type II, SOC 1 Type II, HIPAA / HITECH, ISO 27001, PCI DSS, GLBA, and ITAR posture available across our colocation footprints. EU-US Data Privacy Framework alignment.

Documented data flows

Every engagement ships with a written data-flow document — what data traverses what component, where it is stored, who has access. Procurement-defensible.

Open-weight model deployment

Llama, Mistral, Qwen, DeepSeek, or other open-weight models appropriate to your workload, running in our Kubernetes environment, updated on a schedule you control.

Private connectivity

Direct-connect, private peering, and VPN options for clients requiring low-latency private paths into the platform.

Per-tenant isolation

Workload isolation by default. No shared inference queues against unknown neighbors regardless of where deployment lands.

Operational tooling

Monitoring, alerting, logging, and observability across the stack. Your engineering team (or ours) can see what is happening at any time.

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Business outcomes

AI on infrastructure procurement will approve

The deployment story survives security review, legal review, and CFO review. Workloads that would have been blocked under public-API architecture get unblocked.

Predictable, capacity-based economics

Workload-scaled monthly pricing replaces per-token surprises. The bill does not move when end-user behavior does.

No vendor lock-in

Open-weight models and documented architectures mean you are never trapped by a foundation-model provider's pricing, policy, or availability changes.

Same engineering team, every deployment

The team that designs and operates the Skyview private cloud is the team that deploys into your cloud or on-premises. Consistent operational rigor regardless of where the workload lands.

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How it is priced

Engagement model
Secure AI Deployment & Hosting

Monthly engagement priced to workload — GPU allocation, model sizes, storage, bandwidth, and managed scope. Capacity scales as needs change. No per-token surprises.

For on-premises deployments, pricing covers a one-time build / install plus monthly managed operations. Hardware is itemized separately and billed at cost. For clients also engaging Skyview Labs for application development, hosting is typically bundled into the engagement.

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Secure AI Deployment & Hosting — frequently asked questions

The questions we get most often, answered. If yours isn't here, ask it on a 30-minute call — we answer the awkward ones too.

What's included in private AI cloud hosting?
Dedicated GPU + CPU inference capacity in our Marlborough or Chicago Tier III facilities, self-hosted open-weight model deployment (Llama, Mistral, Qwen, DeepSeek, or others appropriate to your workload), vector database + retrieval infrastructure, monitoring and observability, security posture (Cloudflare-fronted, zero-trust admin, scoped secrets), and a direct line to our engineering team. Not a ticket queue.
Can you host on-premises at our data center instead?
Yes — for enterprises with data sovereignty, ITAR, FedRAMP-adjacent, or air-gap requirements, we install the entire Skyview stack inside your perimeter. We spec the hardware, procure it, rack it, and operate it remotely (with documented access) or on-site as your security posture demands.
How does pricing work?
Monthly engagement priced to workload — GPU allocation, model sizes, storage, bandwidth, managed scope. Capacity scales as your needs change. No per-token surprises. For on-prem deployments, pricing is a one-time build/install plus monthly managed-ops; hardware is itemized at cost.
What compliance attestations do your facilities carry?
Our colocation facilities maintain SOC 1 Type II, SOC 2 Type II, SOC 2 + HITRUST, HIPAA/HITECH, GLBA, PCI DSS v4.0, NIST SP 800-53 Rev. 5, ISO 27001:2022, ITAR, and EU–US Data Privacy Framework. Skyview Labs' own corporate-level SOC 2 / ISO 27001 attestation is on roadmap; we're transparent about that distinction. See full Trust & Security page.
How long does a Skyview Labs AI engagement take?
Most production AI engagements ship in 4 to 12 weeks from kickoff. Discovery Engagements run two weeks. The build phase runs two to eight weeks depending on scope. Integration and testing run one to two weeks. We don't run multi-year transformation engagements — we ship working software early and iterate against real usage.
How much does AI consulting cost?
Discovery Engagements are flat-fee, typically $10,000–$25,000 depending on organizational complexity, and credited against a build engagement that starts within 90 days. Custom build engagements are fixed-price for the scope defined in Discovery — most fall in the $50k–$500k+ range depending on scope. Private AI cloud hosting and Managed AI Operations are monthly engagements priced to workload, typically bundled with the build for a single predictable monthly cost. No per-token surprises.
Where will my data be hosted?
By default, in our private AI cloud — Tier III TierPoint colocation facilities in Marlborough, MA (MRL-01) and Chicago, IL (CHI-01), with regional capacity at our Connecticut office (CT-01). For workloads with strict data sovereignty, ITAR, or air-gapped requirements, we install the entire Skyview stack on-premises in your own data center. Every engagement includes a written data flow document covering every component, every integration, and every external API call.
Do you operate the system after launch?
Yes. The team that builds your system is the team that runs it. Most engagements transition into a monthly Managed AI Operations agreement covering hosting, monitoring, model and dependency updates, performance tuning, security patching, and ongoing refinement against real usage. The engineers who designed the system stay accountable for it — six months and three years from now.
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Talk to us about your deployment

Our cloud, your cloud, on-premises, or hybrid — we will tell you which fits your workload, your data, and your auditors.

Talk to us about deployment