On-Premise Agentic AI Platform
Run the full Xyner AI stack — agents, orchestration, integrations, governance and the LLMs themselves — on Kubernetes inside your own data center. No data egress, your controls, your upgrade windows, with sovereign-cloud and fully air-gapped variants for the environments that demand them.
What is an on-premise agentic AI platform?
An on-premise agentic AI platform is the complete agentic stack — agent runtime, orchestration, connectors, governance, audit and model access — installed on infrastructure you operate, instead of consumed as a vendor-hosted service. Xyner AI ships this as a Kubernetes deployment for your data center or private cloud, with sovereign-cloud and air-gapped variants, so case data, agent reasoning and optionally the language models themselves never cross your boundary.
The decision is ultimately about where processing is allowed to happen. For many organizations a dedicated VPC or the hybrid model answers that with far less operational weight — and we will say so. But for government, defense, banks under strict outsourcing rules, healthcare and critical infrastructure, "on infrastructure we operate" is the requirement. This page describes what that actually involves — trade-offs included.
Who genuinely needs on-premise
Six signals that the answer is your own infrastructure — not a vendor cloud with promises.
Regulated no-egress mandates
Banking outsourcing rules, health-data law, classification regimes and contractual commitments that say processing happens on infrastructure you control. When the regulator's question is "where does the data go?", the strongest answer is "nowhere."
Sovereignty requirements
National-security and public-sector programs where jurisdiction over hardware, operators and keys matters as much as geography. On-premise and sovereign-cloud postures keep all three inside your legal perimeter — relevant across the markets Xyner serves, including the UAE and Saudi Arabia.
Air-gap obligations
Defense, critical infrastructure and classified networks where external connectivity is not restricted but absent. Xyner AI supports fully air-gapped installation with locally hosted models and offline release packages.
Model confidentiality
When prompts and outputs are themselves sensitive — deal flow, incident detail, intelligence — hosting LLMs inside the boundary removes the question of what a third-party model provider sees. The answer becomes: nothing.
Adjacency to core systems
Agents that work your mainframe-adjacent, latency-sensitive or network-isolated systems benefit from sitting on the same network — integration without opening inbound paths through your perimeter.
An existing Kubernetes practice
If your platform team already operates production Kubernetes, the marginal cost of on-prem drops sharply — Xyner AI arrives as workloads your team already knows how to run, monitor and upgrade.
The on-premise architecture, component by component
What lands on your cluster — described at the level your architecture review will start from.
Kubernetes-native core
Agent runtime and orchestration as containerized workloads on your cluster — scaled, scheduled and monitored like the rest of your estate.
Private model serving
Locally hosted LLMs — Llama, Mistral and other private models among 12+ supported families — served inside the boundary and routed per task.
Connector gateway
The 1,000+ integration library running inside your network — scoped credentials, no inbound holes punched through the perimeter.
In-boundary audit store
The immutable trail of every agent step lives on your storage, under your retention policy — evidence that never leaves.
Your identity stack
RBAC binds to the directory and SSO you already run; agent permissions live inside your access-governance regime.
Your observability
Platform metrics and logs flow to the monitoring you already trust — no separate pane of glass required.
Knowledge inside
Document stores, policies and retrieval indexes built and queried in-boundary — agents reason over your facts without exporting them.
Versioned release path
Upgrades as versioned releases applied in your maintenance windows — including offline packages for air-gapped sites.
Capacity and node sizing depend on workload volume and which models you host, so they are established in technical scoping — not quoted as a brochure figure.
Three on-premise postures, in depth
Deployment is this page's core subject — here is the real difference between the variants.
On-premise Kubernetes
The full platform on a cluster in your data center or private cloud. Your team operates infrastructure and applies releases; Xyner supports the platform layer. Internet connectivity exists but data egress is governed by you — external model endpoints are reachable only if your policy allows them. The default choice for strict residency and outsourcing regimes.
Sovereign cloud
The same stack deployed into a sovereign cloud region — infrastructure, operators and jurisdiction inside your national or sectoral perimeter. You gain cloud elasticity without surrendering jurisdictional control; a frequent fit for government and regulated-sector programs in sovereignty-focused markets.
Air-gapped
No external connectivity at all. Models are hosted locally, releases arrive as offline packages through your controlled transfer process, and every capability — agents, connectors, audit — operates in isolation. The strictest posture, for defense, critical infrastructure and classified networks, and the one to plan the most lead time for.
One fact matters more than the labels: agent definitions are portable across all three — and across SaaS, VPC and hybrid too. Teams commonly build in a connected posture and promote to a stricter one, carrying workflows unchanged. The full matrix lives on the platform deployment page and the deployment flexibility capability.
SaaS vs. hybrid vs. on-premise — the honest table
On-premise wins some rows and loses others. Decide on the rows that are mandatory for you.
| Dimension | Multi-tenant SaaS | Hybrid (managed control plane) | On-premise / air-gapped |
|---|---|---|---|
| Data egress | Leaves your boundary, contractually governed | Case data stays in your VPC/DC | Nothing leaves; air-gap removes the path |
| Time to first value | Fastest — days to start | Fast — control plane is ready | Slower — cluster setup precedes use cases |
| Operations burden | None on you; 99.95% SLA | Light — Xyner runs the control plane | Yours: cluster, storage, GPUs, upgrades |
| Upgrade cadence | Continuous, managed | Control plane managed; data plane scheduled | Entirely your windows and your testing |
| Model breadth | All 12+ families on tap | External + private models per policy | What you host or explicitly allow |
| Sovereignty & jurisdiction | Provider-dependent | Strong for data; control plane external | Complete — hardware to keys |
| Hardware investment | None | Your data-plane compute | Cluster plus GPU capacity if hosting LLMs |
| Fit | Speed-first, standard compliance | Regulated data, lean ops team | Mandated control, classification, air-gap |
Private LLMs behind your firewall
On-premise is only as private as its models. Here is how model hosting works inside the boundary.
Host open and licensed models
Meta Llama, Mistral and other privately hostable families run on your GPU capacity, served as in-cluster endpoints the platform treats like any other model target.
Route by data class
Routing rules bind data classes to models: regulated case data only to in-boundary models, while less sensitive tasks may use external endpoints — if your policy allows any.
Right-size per task
Triage doesn't need your largest model. Per-task routing across hosted models keeps GPU demand proportional to the work — capacity planning follows usage, not hype.
Keep prompts as secrets
Prompts, retrieved context and outputs stay inside your network end to end — the confidentiality argument that external APIs can only approximate contractually.
Swap models without rework
Model-agnostic agents mean a better private model next year is a routing change, not a rebuild — see the multi-LLM architecture.
Plan GPUs honestly
GPU need is a function of the models and throughput you choose; we size it in technical scoping against your actual workload rather than quoting generic hardware lists.
Who runs agentic AI on-premise
Sector → why on-prem → what agents do there.
Government & public sector
Sovereignty and classification → citizen-service and case-management agents on national or agency infrastructure, with full in-boundary audit.
Defense & security
Air-gap mandates → document, logistics and analysis agents operating with zero external connectivity and locally hosted models.
Banking & financial services
Outsourcing and residency rules → KYC, reconciliation and servicing agents processing regulated data without a third-party processor in the chain.
Healthcare
Patient-data law → clinical-admin and claims agents working PHI inside hospital infrastructure, HIPAA-aligned.
Energy & critical infrastructure
OT-adjacent isolation → maintenance, compliance and field-support agents on segmented networks external clouds can't reach.
Telecom
Network-data sensitivity → service and operations agents next to BSS/OSS, inside the operator's own boundary.
Manufacturing
IP protection → engineering-document and supply-chain agents where designs and costs never leave the site.
Legal & professional services
Privilege and confidentiality → matter and research agents whose prompts are themselves privileged material.
An on-premise rollout, stage by stage
Platform installation and first use case run in parallel — infrastructure is not a prerequisite phase that eats the quarter.
1. Technical scoping
Workload, model strategy and sizing agreed with your platform team — the honest capacity conversation happens here.
2. Cluster setup & hardening
Deployment onto your Kubernetes, wired to your identity, storage, network policy and monitoring.
3. Model hosting
Private models served in-cluster; routing rules bound to your data classes.
4. Integration wiring
Connectors to the ERP, CRM, ITSM and data systems the first use case touches — all inside the network.
5. Governance & evaluation
RBAC, approval gates, action budgets; the first agent evaluated against historical cases.
6. Go live, then operate
First workflow in production within 6–12 weeks; releases then flow through your maintenance windows.
Integration without leaving the network
The connector library runs beside your systems — which is precisely the point.
ERP & finance
SAP and Oracle estates — often themselves on-prem — reached over the LAN, not the internet.
CRM & service
Salesforce, ServiceNow and in-house service platforms through governed, scoped connections.
HR & workforce
Workday and HRMS platforms joined to lifecycle workflows under your access rules.
Databases & repositories
Internal warehouses and document stores queried with lineage — the knowledge layer stays home.
Email & collaboration
Internal mail and chat as agent channels — intake and approvals without external relays.
REST & SOAP APIs
The in-house and legacy systems that define on-prem estates — first-class, logged, permissioned.
Security posture, compounded
Platform governance layered on the physical and network controls you already operate.
RBAC on your directory
Agent and user permissions bound to your identity stack and reviewed in your access regime.
Human approvals
Approval gates and HITL checkpoints configured per workflow, enforced in-boundary.
Action budgets
Hard caps on agent activity per run and period — bounded autonomy, mechanically enforced.
Immutable audit
Every agent step logged and replayable, stored on your infrastructure under your retention rules.
Model governance
Routing rules decide which models may see which data — enforceable down to in-boundary-only.
Residency by construction
Data stays where the cluster is — residency satisfied by architecture, not contract clauses.
Certifications
SOC 2 Type II and ISO 27001 certified; GDPR-compliant; HIPAA-aligned where relevant.
Continuous evaluation
Regression suites run in your environment before any agent change ships to production. More at platform security.
Ten decisions on the road to on-prem
The questions to settle, in the order they actually bite.
1. Prove the mandate
Name the regulation, classification or contract requiring on-prem. If a VPC satisfies it, save yourself the data center.
2. Pick the posture
Connected on-prem, sovereign cloud or air-gapped — this drives everything downstream, from models to release flow.
3. Decide the model strategy
Fully in-boundary models, or a policy-split between private and approved external endpoints.
4. Size with real workload
Capacity and GPU planning against the actual first use cases — scoped jointly, not guessed from a datasheet.
5. Land platform ownership
Which team operates the cluster and owns release windows — settled before installation, not after.
6. Wire identity & audit first
Directory binding, RBAC model and audit retention configured before the first agent runs.
7. Install and harden
Cluster deployment aligned to your network policy, secrets management and monitoring.
8. Build use case one in parallel
Agent configuration and integration proceed while infrastructure hardens — 6–12 weeks end to end.
9. Rehearse the upgrade
Apply a release in a maintenance window early — the operational muscle matters more than the first one's content.
10. Scale on the same cluster
Subsequent use cases reuse the platform, models and governance — the marginal cost per workflow falls fast.
Deciding between private postures?
Neighboring pages for neighboring requirements.
Frequently asked questions — on-premise agentic AI
What infrastructure and security teams ask before an on-prem commitment.
What is an on-premise agentic AI platform?
It is the full agentic AI stack — agent runtime, orchestration, integrations, governance and model access — installed on infrastructure you control, inside your own data center or private cloud, rather than consumed as SaaS. Xyner AI deploys on-premise on Kubernetes, with sovereign-cloud and fully air-gapped variants, so agents, data and optionally the LLMs themselves never leave your boundary.
What infrastructure does Xyner AI require on-premise?
The platform runs on an on-premise Kubernetes cluster; surrounding requirements — storage, networking, identity integration — follow standard enterprise Kubernetes practice. Exact sizing depends on your workload volume and on whether you host LLMs locally, so capacity is determined during technical scoping rather than quoted as a one-size figure.
Do we need GPUs to run it on-premise?
Only if you host LLMs inside your boundary, which most on-premise customers do. The platform itself runs on standard compute; locally served models are what need GPU capacity, and that sizing depends on the model families and throughput you choose. Where policy permits, the platform can also route to approved external model endpoints, reducing or removing local GPU needs.
Can the platform run fully air-gapped?
Yes. Air-gapped installation is a supported configuration: the platform, its integrations and locally hosted models operate with no external connectivity, and updates are applied through your controlled release process. It is the posture of choice for defense, critical infrastructure and classified environments.
How do private LLMs work in an on-premise deployment?
You host open or licensed models — such as Meta Llama or Mistral family models, among the 12+ LLM families the platform routes across — on your own infrastructure, and Xyner AI's model router treats them as first-class targets. Routing rules can require that specific data classes are only ever processed by in-boundary models.
How do upgrades work on-premise?
Upgrades ship as versioned releases that your operations team applies in your own maintenance windows — you control timing, testing and rollback. Air-gapped sites receive transferable release packages. If you want the platform current without owning that cycle, the hybrid option keeps a Xyner-managed control plane while execution and data stay in your environment.
Who actually needs on-premise rather than VPC or hybrid?
Organizations whose regulation, classification or policy requires that data and processing remain on infrastructure they operate: government and defense, banks under strict residency or outsourcing rules, healthcare systems, critical infrastructure, and enterprises with contractual no-egress commitments. If a dedicated VPC already satisfies your obligations, it is usually the simpler path.
Do we lose capability compared with SaaS?
The agent definitions, governance and integration layer are the same. What changes honestly: your team owns infrastructure operations and upgrade cadence, and model breadth is bounded by what you host or approve — a trade most on-premise adopters accept deliberately in exchange for full control and zero data egress.
Can on-premise agents still integrate with our enterprise systems?
Yes — often more directly, because the platform sits inside your network next to them. The same 1,000+ pre-built integrations apply: SAP, Oracle, Microsoft, Salesforce, ServiceNow, Workday, databases, document repositories and REST/SOAP APIs, all under scoped credentials and full audit.
What security certifications and controls apply?
Xyner AI is SOC 2 Type II and ISO 27001 certified, GDPR-compliant and HIPAA-aligned, and the on-premise platform layers role-based access control, human-in-the-loop approvals, action budgets and immutable audit on top of the physical and network controls you already operate.
How long does an on-premise implementation take?
A first production use case typically lands in 6–12 weeks, running platform installation in parallel with use-case configuration: cluster setup and hardening, integration wiring, governance configuration, evaluation and go-live. Air-gapped programs plan for the longer end of the range.
Is there a middle path between SaaS and full on-premise?
Yes — two. A dedicated VPC gives you isolation without data-center operations, and the hybrid model pairs a Xyner-managed control plane with a data plane in your VPC or data center, so sensitive data stays inside your boundary while Xyner carries the operational load. Many programs start hybrid and move postures later; agent definitions carry over unchanged.
Go deeper on deployment
The documentation behind the decisions on this page.
Scope your on-premise deployment
Bring your mandate, your Kubernetes estate and your first use case. We'll size it honestly — including whether you need on-prem at all.
Request a Xyner AI Demo Talk to Deployment Engineering