Private Agentic AI Platform
Privacy is not one deployment — it is a spectrum. Xyner AI runs the same governed agents in a dedicated VPC, a hybrid split, your own data center, a sovereign cloud or a fully air-gapped network, with private LLM hosting and data boundaries that are enforced by architecture, not by promise.
What is a private agentic AI platform?
A private agentic AI platform runs autonomous AI agents where your data policy says they must run: inside a boundary you control. The boundary might be a dedicated VPC, your own data center, a sovereign cloud region or an air-gapped network — the common property is that case data, documents, prompts, agent reasoning and audit trails stay inside it, and optionally the language models do too. Xyner AI delivers the full spectrum of these postures with identical agent definitions and governance.
"Private" fails as a yes/no question because organizations have different strictest obligations: tenant isolation, environmental control, operational control, jurisdictional control, or physical disconnection. Each maps to a different rung of the spectrum below — and choosing a stricter rung than your obligation requires buys operational burden, not extra compliance. This page walks the whole ladder so you can pick the rung, not the slogan.
It also helps to separate three words that get used interchangeably. Residency is about where data is stored; privacy is about who can see data and prompts; sovereignty is about who holds legal and operational authority over the stack. A deployment can satisfy one without the others — which is why the spectrum below is described in terms of what each posture actually guarantees, not the label on the box.
The private deployment spectrum
Five postures, in increasing order of isolation — plus the honest note on what each one costs you.
Dedicated VPC
A single-tenant deployment in a cloud environment dedicated to you. Answers the isolation question — no shared tenancy — while keeping fully managed operations and the fastest provisioning on the spectrum. The rung most "we need private AI" requirements actually resolve to.
Managed control plane, your data plane
Xyner operates the control plane; agent execution, data, documents and audit live in a data plane inside your VPC or data center. Sensitive material never leaves your environment, yet your team does not inherit a platform-operations job. The spectrum's best effort-to-control ratio.
On-premise Kubernetes
The entire platform on a cluster you operate, in your data center. Full environmental and operational control; your team owns infrastructure and release windows. Covered in depth on the on-premise platform page.
Sovereign cloud
Deployment into a jurisdictionally controlled cloud — hardware, operators and legal authority inside the perimeter your regulator recognizes. Cloud elasticity with answers to the questions residency alone cannot settle. Explored further on the sovereign AI platform page.
Air-gapped
No external connectivity at all: models hosted locally, releases applied as offline packages, every capability operating in isolation. Maximum assurance, maximum operational ownership — chosen when the network path itself must not exist.
Portability between rungs
Agent definitions, workflows, policies and integrations carry across every posture unchanged. Start in a VPC, promote to sovereign; run air-gapped in one country and hybrid in another. The spectrum is a path, not five products.
Data boundaries: what stays in, posture by posture
Privacy conversations go in circles until someone lists what actually crosses the line. The list:
Case data & documents
Inside your environment in every private posture — VPC, hybrid, on-prem, sovereign, air-gapped. The records agents work on never sit in shared infrastructure.
Prompts & model traffic
Stays inside whenever routing sends work to in-boundary models. Routing rules per data class decide — and enforce — which workloads may ever reach an external endpoint.
The models themselves
Hosted inside from on-premise rung upward — and optionally in VPC and hybrid postures too, wherever you provision the capacity for private models.
Audit trails & evidence
Written and retained inside your boundary under your retention policy in all private postures — the regulator's copy never depends on a vendor export.
Credentials & secrets
Connector credentials are scoped and held within your environment's secret management — agents get the minimum access each task requires.
Platform management
The one honest difference: in VPC and hybrid postures, Xyner's managed control plane handles orchestration and operations. Where policy excludes any external management plane, on-premise, sovereign and air-gapped postures remove it.
Put this list in front of your security review early — most private-AI evaluations stall on exactly these six items, and every one of them has a definite answer per posture.
SaaS vs. hybrid vs. self-hosted — where privacy actually differs
The trade-offs cut both ways; the right column is not automatically the right answer.
| Dimension | Multi-tenant SaaS | Hybrid (managed control plane) | Self-hosted / on-premise |
|---|---|---|---|
| Where case data lives | Vendor cloud, contractually bound | Your VPC or data center | Your infrastructure, entirely |
| Who sees prompts | Platform + routed model providers | In-boundary, per routing rules | In-boundary, down to air-gap |
| Model choice | All 12+ families instantly | External + private, split by policy | What you host or approve |
| Ops responsibility | None; 99.95% SLA | Light — data-plane infrastructure only | Full stack is yours |
| Provisioning speed | Days | Fast — control plane pre-exists | Cluster and hardening come first |
| Jurisdictional control | Provider-dependent | Strong for data; external control plane | Complete, through air-gap |
| Upgrade control | Continuous, vendor-run | Managed, with scheduled data plane | Your windows, your testing |
| Typical chooser | Speed-first teams | Regulated data, lean platform team | Mandated control or classification |
Rule of thumb: take the leftmost column that satisfies your strictest written obligation — not the rightmost column that satisfies your anxiety.
Private model hosting, without the mystique
Six practical facts about running LLMs inside your boundary on Xyner AI.
Hostable families
Meta Llama, Mistral and other privately deployable models are among the 12+ LLM families the router supports — served from your infrastructure as in-boundary endpoints.
Routing by data class
Classify your data once; the router enforces which classes may reach which models. Confidential classes can be pinned to private models permanently.
Mixed estates are normal
Most private deployments run both: external frontier models for low-sensitivity work, private models for the rest. The split is policy, enforced by the platform.
Agents don't care which model
Model-agnostic agent definitions mean upgrading or swapping a private model is a routing change — your workflows, prompts and governance stay put.
Capacity follows workload
GPU needs depend on which models you host and the throughput you drive; we size them in technical scoping against your real volumes, not from a generic datasheet.
Details live elsewhere
The full routing architecture is documented at multi-LLM architecture and the multi-LLM capability page.
Who sits where on the spectrum
Requirement → rung → what runs there.
Financial services
Outsourcing and confidentiality rules → hybrid or on-premise → KYC, reconciliation and servicing agents with regulated data pinned to private models.
Government
Sovereignty requirements → sovereign cloud or on-premise → citizen-service and case agents inside national jurisdiction; see UAE and Saudi Arabia programs.
Healthcare
Patient-data law → hybrid or on-premise, HIPAA-aligned → clinical-admin and claims agents that keep PHI inside hospital boundaries.
Defense & critical infrastructure
Disconnection mandates → air-gapped → document and operations agents with locally hosted models and offline releases.
Legal & professional services
Privilege → dedicated VPC or hybrid → matter and research agents whose prompts are themselves confidential work product.
Manufacturing & energy
IP and OT isolation → on-premise near the plant → engineering-document and maintenance agents on segmented networks.
Telecom
Network-data sensitivity → hybrid or on-premise → service agents beside BSS/OSS inside the operator boundary.
Multinationals
Different rules per market → different rungs per region, one platform → the same agents deployed to each jurisdiction's required posture.
Insurers & pension funds
Policyholder-data obligations → dedicated VPC or hybrid → claims, underwriting-support and servicing agents with regulated classes pinned to in-boundary models.
From requirement to private production
The lifecycle of a private deployment — posture choice is step one, not an afterthought.
1. Write the obligations down
Regulations, contracts and classifications, with their exact words — the rung is chosen by text, not instinct.
2. Choose the lightest sufficient rung
Match each obligation to the spectrum; take the least operationally heavy posture that satisfies all of them.
3. Set the model policy
Data classes mapped to permitted models — what may go external, what stays in-boundary, what gets hosted.
4. Provision the boundary
VPC, data plane, cluster or sovereign environment stood up and wired to identity and monitoring.
5. Integrate and govern
Connectors, RBAC, approval gates and audit retention configured; first agent evaluated on history.
6. Go live, re-assess annually
First use case in 6–12 weeks; posture reviewed as regulation and model availability evolve.
Everything else comes with the platform
Choosing a private posture gives up none of this.
250+ agentic capabilities
Goal execution, planning, document understanding, memory — identical across all five postures.
Multi-agent orchestration
Planner, executor and reviewer agents cooperating inside the boundary.
1,000+ integrations
SAP, Oracle, Microsoft, Salesforce, ServiceNow, Workday, databases and APIs — all in-boundary.
Human-in-the-loop
Approval gates, escalation paths and review tiers configured per workflow.
RBAC & action budgets
Scoped permissions and hard activity caps on every agent, in every posture.
Immutable audit
Every step logged and replayable, retained inside your boundary under your rules.
Certifications
SOC 2 Type II, ISO 27001, GDPR-compliant, HIPAA-aligned where relevant.
Continuous evaluation
Regression suites and drift detection run where the agents run. Full picture at platform security.
Ten questions that settle the private-AI decision
Answer these in order and the posture usually picks itself.
1. What must never leave?
Name the data classes with residency, confidentiality or classification constraints — in writing.
2. Isolation or control?
If the issue is shared tenancy, a dedicated VPC ends the discussion. If it's who operates the stack, keep reading.
3. May a managed control plane exist?
Yes → hybrid gives you in-boundary data with managed operations. No → on-premise or beyond.
4. Does jurisdiction matter?
If operators and legal authority must sit inside a perimeter, sovereign cloud is the rung — residency alone won't satisfy it.
5. Is connectivity itself the risk?
Then air-gap, and plan for offline releases and locally hosted models from day one.
6. Which workloads need private models?
Split your data classes: external-model-eligible vs. in-boundary-only. Most estates are mixed.
7. Who operates what?
Be honest about platform-team capacity — the rungs differ most in the ops they hand you.
8. What evidence will auditors want?
In-boundary audit trails under your retention policy — confirm the posture produces them natively.
9. Will the posture change?
Plan for portability: the agents you build this year should survive next year's regulation unchanged.
10. What ships in 6–12 weeks?
Pick the first use case now — posture decisions go faster when a concrete workflow is waiting on them.
Neighboring decisions, their own pages
Each rung of the spectrum, examined in depth.
Frequently asked questions — private agentic AI
What security, compliance and platform leaders ask when "private" becomes a requirement.
What is a private agentic AI platform?
It is an agentic AI platform deployed so that your data, your agents' reasoning and optionally the models themselves stay inside a boundary you define — a dedicated VPC, your data center, a sovereign cloud or an air-gapped network. Xyner AI offers the full spectrum of these postures with the same agent definitions, governance and integrations in each.
What private deployment options does Xyner AI offer?
Five, in increasing order of isolation: a dedicated VPC; a hybrid model with a Xyner-managed control plane and a data plane in your environment; on-premise Kubernetes in your data center; sovereign cloud inside a national or sectoral jurisdiction; and fully air-gapped installation with no external connectivity.
How do we choose between VPC, hybrid, on-premise, sovereign and air-gapped?
Work backwards from your strictest obligation. If isolation from other tenants is the requirement, a dedicated VPC suffices. If data must stay in your environment but you want managed operations, choose hybrid. If processing must run on infrastructure you operate, go on-premise. If jurisdiction over operators and hardware matters, sovereign cloud. If connectivity itself is prohibited, air-gapped.
What exactly stays inside the boundary in each posture?
In a dedicated VPC and hybrid deployment, case data, documents, prompts, agent execution and audit trails stay inside your environment. On-premise and sovereign postures add the platform itself and, typically, the models. Air-gapped adds the final element: no network path exists for anything to leave.
How does private LLM hosting work?
You host privately deployable models — Meta Llama and Mistral families among the 12+ LLM families Xyner AI routes across — inside your boundary, and the platform's router treats them as first-class targets. Agents are model-agnostic, so moving a workload from an external model to a private one is a routing change, not a rebuild.
Can some workloads use external models while others stay private?
Yes — that split is the point of routing by data class. You define which data classes may reach which models: public-facing content may use external frontier models, while regulated or confidential classes are restricted to models inside your boundary. The rules are enforced by the platform, not by convention.
In the hybrid model, what does the managed control plane see?
The control plane handles platform management and orchestration, while customer data, documents, prompts and agent execution remain in the data plane inside your VPC or data center. Organizations whose rules exclude any external management plane choose the on-premise or air-gapped posture instead — both are standard options.
Is Xyner AI compliant with GDPR and other frameworks?
Yes. Xyner AI is GDPR-compliant and HIPAA-aligned, certified to SOC 2 Type II and ISO 27001, and its private deployment postures are designed to support data-residency and sector-specific regimes. Compliance obligations stay with the data controller; the platform supplies the controls and the evidence.
Who operates what in each private posture?
Dedicated VPC and hybrid: Xyner carries most operations, with a 99.95% SLA on managed services. On-premise and sovereign: your team operates infrastructure and applies versioned releases in its own windows, with Xyner supporting the platform layer. Air-gapped: your team operates everything, with releases delivered as offline packages.
Can we change deployment posture later?
Yes. Agent definitions, workflows, governance policies and integrations are portable across every posture, so starting in a dedicated VPC and later moving to on-premise or sovereign deployment — or running different postures per region — does not mean rebuilding your agents.
Do private deployments still get the full integration library?
Yes. All 1,000+ pre-built integrations — SAP, Oracle, Microsoft, Salesforce, ServiceNow, Workday, databases, document repositories and REST/SOAP APIs — run inside the private boundary under scoped credentials and full audit.
How long does a private deployment take to first production use case?
Typically 6–12 weeks, with platform provisioning and the first use case progressing in parallel. Dedicated VPC and hybrid land at the faster end; on-premise, sovereign and especially air-gapped programs should plan toward the longer end.
Go deeper on the platform
The reference pages behind this one.
Find your point on the private spectrum
Bring your obligations — we'll map them to the lightest posture that satisfies them, and scope the first use case to 6–12 weeks.
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