Zylon is positioned as an **on-premise private AI platform for regulated industries**, emphasizing deployment inside enterprise infrastructure without external cloud dependencies.

Building a comparable platform internally typically means standing up (and continuously operating) a multi-layer stack: GPU infrastructure, model inference services, retrieval-augmented generation (RAG) pipelines, identity and access controls, audit logging, observability, incident response integration, and compliance evidence generation. These are feasible goals—but the “build” path shifts risk and accountability onto your organization’s engineering, security, and governance functions, and it tends to become a long-lived product rather than a one-time project.

Zylon’s differentiation, based on its published platform and documentation, is not that it eliminates the need for governance—regulated enterprises always need governance—but that it packages a pre-integrated private AI stack (AI Core + Workspace + API Gateway), supports on-prem and air-gapped deployment, and includes governance primitives such as audit logging, role-based access control, and enterprise authentication integration.

Zylon also explicitly contrasts itself with building fully in-house by stating that a custom AI stack can require 12–18 months, significant engineering resources, and ongoing maintenance; while its own platform is presented as deployable in hours with built-in governance (though it still requires GPU hardware).

What Is Zylon

Private AI for regulated industries positioning

Zylon frames itself as an enterprise AI platform delivering private generative AI and on-premise AI software for regulated industries, enabling secure deployment inside enterprise infrastructure without external cloud dependencies.

This matters for enterprise buyers because “private AI” is not a brand label—it is an architectural and operating model that typically implies:

• Data stays inside the organization’s controlled environment (or cloud tenant)

• No ungoverned prompts to consumer AI services

• Security controls (authn/authz, audit, encryption, segmentation)

• Compliance evidence for auditors and regulators (logs, policies, model documentation, risk assessments)

In Zylon, “data never leaves your environment” It is “air-gap capable,” and provides complete “audit trails.”

Zylon AI Core

Zylon’s AI Core is the foundation: a self-contained AI infrastructure including local LLMs, vector databases, and GPU orchestration, deployable in cloud VPCs, on-prem servers, and fully air-gapped environments.

This is relevant to an enterprise AI infrastructure conversation because AI Core corresponds to what internal teams normally have to assemble from multiple components (model runtime + vector storage + scheduler + security baselines) before any business unit sees value.

Zylon Workspace

Zylon Workspace is presented as the daily interface for teams: AI assistant, document creation, knowledge base access, collaborative projects, and data connectors “powered by your private data.”

The Workspace documentation describes it as a collaborative AI interface designed to replace consumer tools like OpenAI’s ChatGPT or Anthropic’s Claude with an on-premise solution.

Workspace governance artifacts appear directly in the docs, including:

• Roles and permissions for knowledge bases (explicitly described in the user manual)

• Audit logs accessible in Workspace administration

For regulated enterprises, these are not “nice-to-haves.” They become core evidence when demonstrating policy enforcement, activity traceability, and security monitoring maturity.

Zylon API Gateway

Zylon’s  API Gateway is an extensibility layer with:

• OpenAI-compatible endpoints

• Built-in authentication, logging, rate limiting, and observability

• Integration patterns that mention LangChain and n8n

This matters for enterprise product buyers because it anchors “private AI” into existing integration economics: identity providers, internal tools, workflow engines, and developer platforms can interact with private inference similarly to public API patterns—but contained inside your perimeter.

On-prem deployment model and air-gapped options

Zylon supports online, semi-airgapped, and full-airgapped installation paths, with documentation-labeled install times on the order of tens of minutes to ~90 minutes (installation time is not the same as organizational production readiness, but it is an important operational input).

For regulated industries and defense/public sector environments where network segmentation or full disconnection is required, Zylon’s industry pages emphasize “air-gap capable” operations and “no cloud exposure.”

Governance, auditability, and observability controls in docs

Zylon’s documentation exposes knobs for audit and observability:

• An “Audit Log” feature that stores audit events & traces and can be enabled/disabled via config

• An “Observability” page describing crash reporting (via Sentry) and usage metrics pipelines (via Grafana), with an opt-out configuration

Operational maturity features such as backups/disaster recovery are described, including zylon-cli backup/restore workflows and Velero-based approaches for Kubernetes environments.

What Does Building In-House Mean

Building a private AI platform “in-house” for enterprise use typically means you are becoming the platform vendor for your own organization. In regulated industries, that includes both the technical system and its governance evidence pipeline.

A reference architecture usually spans at least these layers:

• GPU procurement and lifecycle (hardware selection, drivers, firmware, RMA processes, capacity planning)

• Model hosting and inference (serving runtimes, multi-model routing, isolation boundaries, patching cadence)

• RAG pipelines (document ingestion, chunking, embeddings, vector indexing, retrieval quality evaluation)

• Orchestration (workload scheduling, scaling, failover, backups/restore, cluster security)

• Monitoring and observability (metrics, traces, logs, alerting, SLOs, incident response integration)

• Security hardening (identity, access control, secrets, network segmentation, audit logging, supply chain assurance)

• Compliance engineering (mapping controls to GDPR/HIPAA/SOC 2/EU AI Act/NIS2/DORA requirements; evidence retention)

• Ongoing maintenance (model updates, retraining cycles where applicable, vulnerability management, policy changes)

Hidden complexity: “RAG” is not a feature checkbox

Retrieval-Augmented Generation (RAG) is widely used in enterprise settings because it lets language models incorporate enterprise documents at query time. The core concept—combining parametric model knowledge with a retriever over a dense vector index—is well described in foundational literature.

Operationally, the hard part is seldom “build the retriever once.” In regulated environments, the hard part is:

• governing what content flows into the index,

• tracing which sources were used for which outputs,

• ensuring access controls match policy expectations, and

• producing audit-proof evidence for oversight.

Why long-term ownership burden dominates regulated TCO

Zylon’s own comparison page describes building a fully custom AI stack as an option “best for large tech-driven organizations,” but also calls out a 12–18 month timeline plus ongoing maintenance and long-term cost.

That timeline is consistent with what many enterprises observe when moving from a pilot to a production-grade platform with governance, reliability, and compliance evidence. In regulated environments, the platform almost always expands after initial launch because new use cases introduce new data domains, new controls, and new audit obligations—especially under the EU AI Act’s high-risk requirements for risk management, data governance, record-keeping, technical documentation, and human oversight.

On-Prem AI Platform Comparison: Zylon vs In-House Build

Deployment model comparison for enterprise AI infrastructure

Zylon positions its platform as deployable “in hours,” with “single-command deployment,” “automatic GPU optimization,” and “built-in monitoring/HA/governance,” while stating that building in-house can take 12–18 months.

Zylon’s operator documentation also describes discrete install modes (online, semi-airgap, full-airgap) and corresponding install time expectations.

Deployment and operational complexity comparison table

Data privacy and sovereignty analysis for private AI

For regulated industries, “data sovereignty” often means more than residency. It includes:

• technical control of infrastructure,

• third-party processor minimization,

• auditability and traceability,

• and the ability to operate in disconnected environments when required.

With Zylon “All data stays on your servers,” there is “No cloud exposure,” and deployment is “Air-gap capable,” “No third-party processors,” and “Every AI interaction logged for accountability.”

For an in-house stack, sovereignty is theoretically maximal—because you control everything. But in practice, sovereignty can degrade through:

• fragmented tooling and unclear ownership boundaries,

• misconfiguration across identity/logging/orchestration layers,

• inconsistent access control enforcement between the application layer and the retrieval layer, and

• supply chain dependencies that are poorly inventoried (model weights, containers, internal libraries, retriever services).

Compliance and governance

This section maps governance capabilities to major frameworks explicitly requested: GDPR, HIPAA, SOC 2, EU AI Act, NIS2, and DORA. This is not legal advice; it is a technical governance analysis aimed at enterprise buyers.

GDPR

The European Commission describes that non-compliance with GDPR can result in enforcement actions including bans on processing and fines up to €20 million or 4% of worldwide turnover.

The European Data Protection Board provides guidance that stresses risk-based security measures for personal data (security of processing), emphasizing that measures must be adapted to context, state of the art, and risk.

From a platform perspective, this translates into requirements like:

• strong access control,

• robust audit logs,

• secure configurations,

• incident detection, and

• evidence that measures are actually implemented.

Zylon documents explicit audit logging controls and Workspace governance constructs (roles/permissions).

HIPAA

The U.S. Department of Health and Human Services explains that the HIPAA Security Rule establishes standards to protect electronic protected health information (ePHI) and requires administrative, physical, and technical safeguards.

When mapped to an enterprise AI platform, HIPAA concerns typically include:

• access control and authentication,

• audit controls and monitoring,

• integrity controls for information systems,

• and documented risk analysis/risk management practices.

Zylon is HIPAA compliant

SOC 2

The AICPA explains that SOC 2 reports address controls relevant to security, availability, processing integrity, confidentiality, and privacy.

In an AI platform context, SOC 2 alignment usually requires: evidence of access controls, change management, audit logging, incident response processes, vendor risk oversight, and availability commitments. Zylon’s supports compliance with SOC 2. Zylon’s documentation surface includes audit log controls, enterprise authentication integration, and operations procedures such as backups/disaster recovery.

EU AI Act

The EU AI Act is now a concrete compliance driver for organizations deploying AI in the EU. The AI Act Service Desk confirms that the AI Act entered into force on 1 August 2024.

For high-risk AI systems, the AI Act requires:

• a continuous risk management system (Article 9)

• data and data governance requirements for datasets (Article 10)

• record-keeping and logging expectations (Articles 12 and 19)

• human oversight requirements (Article 14)

• accuracy/robustness/cybersecurity expectations (Article 15)

• technical documentation requirements (Article 11)

It also defines penalty ceilings: up to €35,000,000 or 7% worldwide turnover for prohibited practices and up to €15,000,000 or 3% worldwide turnover for other specified violations (Article 99).

For CIO/CISO teams, the practical implication is that governance is not just “internal policy.” It needs to be auditable, with traceability and operating controls aligned to lifecycle risk management obligations.

For EU institutions, Zylon's architecture inherently satisfies the European AI Act requirements for high-risk AI systems. Complete transparency through audit logs, guardrails, human oversight built into workflows, data sovereignty by design, and documented risk management—all standard features, not compliance add-ons. EU agencies get AI Act compliance automatically.

Compliance and governance comparison table

Cost model comparison for build vs buy AI platform

Cost is not just infrastructure. For regulated industries, TCO is usually dominated by:

• labor (engineering + security + compliance),

• ongoing operations,

• audit and evidence production,

• and risk exposure if controls fail.

Zylon also stresses predictable scaling economics, including “no per-user fees” and “fixed cost regardless of scale” in public sector positioning.

Input benchmarks for a cost model

To make a cost model concrete, we use median US compensation benchmarks as one reference point (your enterprise may differ by geography and industry, and regulated financial institutions may run above median).

The U.S. Bureau of Labor Statistics reports median annual wages (May 2024):

• Software developers: $133,080

• Information security analysts: $124,910

• Data scientists: $112,590

BLS Employer Costs for Employee Compensation (ECEC) reports that in September 2025 employer costs for private industry averaged $32.37/hour in wages and $13.68/hour in benefits, implying benefits are a substantial additional load on top of wages (benefits are roughly 42% of wage cost in that snapshot).

Zylon’s hardware guide also provides example GPU price ranges (as of May 5, 2025) for common NVIDIA cards used in local inference deployments—useful for rough capex planning.

Cost comparison table

The following table is an illustrative model for a mid-size regulated enterprise deploying a private AI platform for knowledge work + RAG + internal APIs (replace assumptions with your own user counts, security posture, and procurement data).

This table uses Zylon’s own “build vs” positioning, Zylon’s hardware requirements documentation, and regulatory penalty ceilings from EU GDPR and EU AI Act references.

Security posture and threat model differences

For regulated industries, the security question is not “is it on-prem?” but “what is the attack surface and how do we prove control?”

An in-house stack introduces many “security seams”:

• container ecosystem risks and orchestration security (NIST’s container security guide highlights container-specific concerns and mitigations)

• expansive control catalogs (NIST SP 800-53 provides a broad security/privacy control set for organizational systems)

• governance and trustworthiness requirements specific to AI systems (NIST AI RMF frames AI risk management as a lifecycle discipline)

Zylon’s security posture—based on its docs—leans on standard enterprise controls (SSO, RBAC, audit), configuration-managed features (audit log enablement), and observability that can be toggled.

A notable nuance for enterprise architects: some connectors can intentionally “flatten” permissions at the project boundary. For example, Zylon’s SharePoint connector documentation warns that everyone with access to a Project will have access to the connected SharePoint files, independent of original SharePoint permissions. For regulated industries, that means you must model Projects as security domains and govern project membership accordingly.

Performance and customizability

Zylon positions itself as a platform that can be built upon, not only a workspace, and highlights automation and developer integration via API Gateway.

Performance in on-prem AI is strongly tied to GPU choice, memory, and workload shape. Zylon’s hardware requirements list VRAM guidance and include GPU examples and approximate price ranges that help frame performance/capacity planning.

In-house builds can be maximally customizable (you control every component) but that flexibility must be balanced against the governance obligations of high-risk AI systems (risk management, data governance, logging, oversight) and the operational resilience expectations in financial services (DORA).

Integration and extensibility

Zylon provides:

• API Gateway with OpenAI-compatible endpoints and built-in auth/logging/rate limiting/observability

• Workspace connectors for systems like SharePoint, Confluence, file systems (Samba), and Claromentis

• Documented SSO integration with Google and Microsoft Entra

An in-house approach can integrate with anything—but every integration expands the attack surface and increases governance scope. NIS2 explicitly broadens cyber risk management expectations across critical sectors and emphasizes reporting and accountability, which makes integration sprawl a regulatory problem, not just a technical one.

Enterprise Use Cases in Regulated Industries

This section focuses on practical “AI for the enterprise” scenarios where private/on-prem architecture is most defensible.

Private AI for banking and financial services

Zylon positions itself for financial institutions that require data privacy and compliance, emphasizing secure on-prem deployment, encryption (in transit/at rest), audit logging, and role-based access control.

In financial services, the compliance conversation increasingly includes DORA for ICT risk management, third-party risk, and exit planning expectations.

Common enterprise use cases include:

• Policy and procedure Q&A over internal corpora (RAG)

• KYC/AML support workflows (summarization, checklist generation; always with human oversight)

• Internal audit acceleration (evidence retrieval, control narrative drafting)

• Secure developer APIs for internal product teams (private AI assistants in apps)

Credit unions

Zylon’s “regulated industries” positioning explicitly includes credit unions as part of financial services, being Zylon’s AI Platform purposely built for Credit Unions, like Orsa Credit Union. Orsa CEO, Tansley Stearns said about Zylon: At Community Financial (now Orsa) , our members are at the heart of everything we do. Partnering with Zylon allows us to bring them smarter, more secure banking, as privacy is paramount.

Credit unions often face a similar “build vs buy AI platform” decision but with tighter staffing constraints than global banks—making platform operational complexity a first-order decision variable.

AI for healthcare

Zylon explicitly calls out healthcare networks protecting patient information under HIPAA as a target domain.

In healthcare, key private AI use cases include:

• Clinical policy and protocol retrieval (RAG-based assistance)

• Compliance documentation drafting support (while protecting PHI/ePHI)

• Revenue cycle support (secure summarization, document classification)

The governance baseline is shaped by HIPAA Security Rule safeguards (administrative/physical/technical) and the need for auditability of access and actions.

Private AI for the public sector

Zylon’s public sector approach provides:

• “All data stays on your servers”

• “No cloud exposure”

• “Air-gap capable”

• “Every AI interaction logged for accountability”

• “Fixed cost regardless of scale / no per-user fees”

Government use cases named on that page include application processing/case management, audit/compliance review, and policy development/research.

NIS2 matters here because public administration is explicitly included in the directive’s broadened scope, and it raises cybersecurity risk management and incident reporting expectations at the sectoral level.

Defense and critical infrastructure

Zylon’s defense/critical infrastructure approach emphasizes air-gapped/SCIF deployment models, ITAR/EAR framing, audit logs, and project-level segregation.

For this segment, “private AI” often means:

• disconnected operations,

• strict segmentation by program/classification,

• and minimization of external dependencies.

Platform governance and audit logs become not just “compliance,” but operational security controls.

Regulated industry suitability comparison table

Sector pressures and Zylon segment positioning are supported by Zylon industry resources and EU/National regulatory sources.

Strengths and Limitations of Each

Where building in-house provides superior flexibility

Building in-house can be strategically correct when:

• you need deep customization of model orchestration and routing,

• you’re performing experimental research (novel retrievers, custom training regimes),

• you want full control over data pipelines and model evaluation infrastructure,

• and you can sustain the long-term “platform as product” ownership model.

Where building in-house introduces disproportionate risk

In regulated industries, “build” often fails not because the enterprise cannot build—many can—but because:

• governance evidence becomes fragmented across tools,

• audit logging is incomplete or hard to prove tamper-resistant,

• security baselines drift over time across microservices, and

• regulatory requirements evolve (EU AI Act phased application timelines; NIS2 ongoing amendments; DORA technical standards).

Zylon’s SharePoint connector note is a good example of a tradeoff you still must manage even when buying: access boundaries must be modeled correctly (project membership becomes critical). That’s not a “Zylon flaw” so much as a reminder that enterprise governance design remains required.

Where Zylon reduces operational complexity

Zylon’s strategic value proposition for regulated enterprises is reducing the integration burden by providing one connected platform (AI Core + Workspace + API Gateway), with deployment modes designed for on-prem and air-gapped environments, and governance primitives such as audit logs, RBAC, and enterprise authentication integration already present in documentation.

When Building In-House Makes Sense

Building an enterprise AI platform internally is typically rational when most of the following are true:

• You have a very large AI/platform engineering team that can sustain a dedicated internal product over multiple years.

• Your use cases are highly specialized and cannot converge on a standard platform model.

• You operate in a lower-regulation environment (or your AI is strictly internal and low risk), reducing the governance evidence burden—though GDPR and cybersecurity frameworks can still apply depending on data type and sector.

• You can absorb 12–18 months of build time without losing stakeholder trust or momentum.

When Zylon Is the Strategic Choice

Zylon is most defensible as a “buy” decision when:

• You are in a regulated industry where private AI for regulated industries is a hard requirement.

• You need on-prem or air-gapped deployment as a baseline, not a future phase.

• Your AI initiative will be judged by auditability, evidence, and governance maturity (EU AI Act record-keeping/logs, GDPR enforcement risk, HIPAA safeguards, SOC 2 controls, NIS2 cyber risk management, DORA resilience and third-party risk).

• You need faster time-to-value than a 12–18 month internal platform build.

• Your operating model benefits from predictable economics (e.g., no per-user fees / fixed cost positioning).

Final Recommendation for Enterprise Decision-Makers

A useful way to decide is to treat this as a risk-adjusted infrastructure decision, not a tooling decision.

If your organization is deploying private AI in regulated environments, the dominant drivers are:

• Auditability and traceability (EU AI Act record-keeping/log retention; SOC 2 controls; HIPAA audit controls)

• Cyber risk management and reporting capability (NIS2)

• Operational resilience and third-party exit planning (DORA)

• Financial and legal exposure ceilings (GDPR and AI Act penalty ceilings can be existential at scale)

Under this lens:

• Building in-house is optimal when AI platform engineering is a core competency you are willing to fund and govern for years—and you can operationalize AI governance as a durable lifecycle capability.

• Zylon is optimal when you need a pre-integrated private AI platform that can be deployed on-prem/air-gapped, with governance and extensibility layers already built, so your team can focus on enterprise adoption, data governance, and control mapping rather than constructing the entire enterprise AI stack.

FAQ

Is it better to build or buy an enterprise AI platform?

It depends on whether you are buying speed and risk reduction or buying maximum flexibility. Zylon explicitly frames building in-house as a 12–18 month effort with significant ongoing maintenance, which is often incompatible with regulated-industry timelines and governance demands.

How much does it cost to build a private AI system?

Even a conservative model typically includes (1) GPU and server capex plus (2) multi-year staffing. Median wage benchmarks for key roles alone are in the six figures, and employer benefit loads add substantial overhead.

What are the compliance risks of building AI internally?

The main risk is not that compliance is impossible—it’s that evidence becomes fragmented or incomplete. Under the EU AI Act, high-risk systems require risk management, data governance, logging/record-keeping, technical documentation, and human oversight; penalties can reach €35M / 7% worldwide turnover. GDPR enforcement can reach €20M / 4% worldwide turnover.

Can AI be deployed fully on-premise?

Yes. Zylon’s platform overview explicitly describes deployment on-prem and in fully air-gapped environments, and its operator manual supports online, semi-airgap, and full-airgap installation modes.

How do banks deploy private AI securely?

Banks typically require: strict access control, audit logs, secure data handling, operational resilience, and third-party risk management. DORA reinforces resilience and ICT third-party risk management expectations, and Zylon positions itself for financial services with on-prem security controls and auditability.

What does enterprise AI governance require?

At minimum: role-based access control, audit trails, logging and monitoring, model/dataset governance, and documented oversight processes. Under the EU AI Act, governance is a lifecycle obligation that includes risk management (Art. 9), dataset governance (Art. 10), record-keeping/logging (Art. 19), and human oversight (Art. 14).

How does Zylon support integrations and extensibility?

Zylon’s API Gateway is described as OpenAI-compatible with built-in authentication/logging/rate limiting/observability, and the platform provides both low-level and workspace APIs that run inside company infrastructure. Workspace connectors include SharePoint and other enterprise systems, and SSO integrations are documented.

Author: Cristina Traba Deza, Product Designer at Zylon
Published: February 2026
Last updated: February 2026

Cristina designs secure, on-premise AI platforms for regulated industries, specializing in enterprise AI deployments for financial services, healthcare, and public sector organizations requiring full data control, governance, and compliance.