For enterprises in regulated industries, the choice between Zylon and Gemini is fundamentally a choice between infrastructure control and cloud convenience. Zylon delivers a fully private, on-premise AI platform purpose-built for organizations that cannot allow sensitive data to leave their perimeter — banks, healthcare providers, government agencies, and defense organizations operating under strict data sovereignty mandates. Gemini, Google's multimodal foundation model family, offers frontier AI capabilities primarily through cloud-based delivery via Vertex AI and Google Workspace, with a newer on-premise path through Google Distributed Cloud (GDC).

Both platforms serve enterprise AI needs, but they approach the problem from opposite directions. Zylon starts with data sovereignty and governance as foundational constraints, then builds AI capability within those boundaries. Gemini starts with maximum model capability in the cloud, then layers enterprise controls on top. This architectural difference shapes every downstream decision — from compliance posture to cost predictability to threat model exposure.

For CTOs and CISOs in regulated industries evaluating private AI for banking, healthcare, or the public sector, this distinction is not academic. It determines whether your organization retains full ownership of its AI infrastructure or depends on a third-party cloud provider's contractual commitments for data protection.

What is Zylon? Private AI built for regulated industries

Zylon is an enterprise AI platform delivering private generative AI and on-premise AI software, enabling secure deployment inside enterprise infrastructure without external cloud dependencies. Built by the creators of PrivateGPT — the open-source AI framework with over 57,000 GitHub stars

The platform operates on a three-layer architecture. Zylon AI Core provides self-contained AI infrastructure including local large language models, vector databases (Qdrant), and GPU orchestration. Zylon Workspace delivers the team-facing interface — an AI assistant, document creation, knowledge base access, collaborative projects, and data connectors. Zylon API Gateway serves as the extensibility layer with OpenAI-compatible endpoints, built-in authentication, logging, rate limiting, and observability.

Zylon supports three deployment environments: private cloud (VPC), on-premise (bare metal data centers), and fully air-gapped (no internet connection required). The platform reaches production readiness in under a week via single-command deployment Zylon through zylon-cli. Its air-gapped mode is configured through a simple YAML flag (airgap: offline_operation: true), enabling operation in environments with zero internet connectivity — from submarine operations to classified government facilities.

The platform supports multi-model and multimodal operation, running several AI models in parallel within a single instance. Organizations can plug in their own fine-tuned models without losing platform features.Currently, Zylon ships with models like Qwen 2.5 14B as a baselineand its own GPT-OSS model in 24GB and 48GB configurations, with full GPU orchestration built into the AI Core layer.

What is Gemini? Google's multimodal foundation model family

Gemini is Google DeepMind's family of multimodal large language models, first launched in December 2023 and now in its third generation. The platform natively processes text, code, images, audio, video, and documents, with context windows reaching 1 million tokens.As of February 2026, the Gemini 3 family represents Google's most capable models, Google with variants including Gemini 3 Pro and Gemini 3 Flash alongside the stable Gemini 2.5 line.

Gemini is delivered primarily through three channels: the Gemini Developer API (via Google AI Studio) for developers, Vertex AI on Google Cloud for enterprise deployments, and Google Workspace for productivity integration across Gmail, Docs, Sheets, Slides, and Meet. Each channel has different pricing, controls, and compliance characteristics.

Google has introduced an on-premise deployment path through Google Distributed Cloud (GDC), including an air-gapped variant authorized for U.S. government Secret and Top Secret missions. GDC Connected requires constant internet for remote attestation via Intel SGX, while GDC Air-Gapped operates with no connectivity to Google Cloud or the public internet. GDC requires Google-certified hardware and NVIDIA Blackwell GPU-based appliances, with a minimum deployment of four racks.

How deployment models differ between on-premise AI and cloud AI

The deployment model is where Zylon and Gemini diverge most sharply.

Zylon's deployment model is designed for organizations that need AI governance and audit controls without cloud dependency. The platform runs on standard NVIDIA GPU hardware that the customer already owns or procures independently. This means no vendor dependency on proprietary appliances and no minimum rack requirements.

Gemini's GDC Air-Gapped option does provide a genuine on-premise path, but it requires significant infrastructure investment in Google-certified hardware and maintains a dependency on Google's software stack. The GDC Connected variant requires persistent internet for remote attestation which disqualifies it from true air-gapped environments. For organizations already operating Google Cloud, Vertex AI provides the most frictionless path to Gemini's capabilities— but data processing occurs within Google's infrastructure under a shared responsibility model.

Data privacy and sovereignty: where your data lives matters

Data sovereignty is the single most consequential differentiator for regulated enterprises evaluating private AI vs cloud AI.

Zylon enforces data sovereignty architecturally. All AI models run entirely on customer premises. Data never touches external servers, and no information leaves the customer's infrastructure perimeter. This is not a contractual promise — it is a technical constraint of the deployment model. There are no data residency configurations to manage because data residency is inherent to on-premise deployment.

Gemini's data sovereignty depends on configuration, tier, and feature. On Vertex AI, Google states that customer data is not used to train foundational models, and customers can control data-at-rest regions using regional or multi-regional APIs. Gemini Enterprise supports data residency in U.S. and EU multi-regions only. Customer-Managed Encryption Keys (CMEK) and VPC Service Controls are available but carry important caveats: enabling Grounding with Google Search disables Data Residency, CMEK, VPC Service Controls, and Access Transparency controls simultaneously.

For EU sovereignty specifically, Google partners with Thales (France, via S3NS) and T-Systems (Germany) for sovereign cloud operations, where a European partner holds operational authority over sensitive data. This layered approach is designed to meet requirements like France's SecNumCloud certification. Zylon sidesteps this complexity entirely — EU organizations deploy within their own EU-based data centers with no third-party operational authority required.

On the free tier of the Gemini Developer API, content is used to improve Google products. This is explicitly stated in Google's documentation. Only paid API tiers and enterprise deployments exclude customer data from model training.

Compliance and governance across regulatory frameworks

Regulated industries require demonstrable compliance, not just capability claims. The table below maps both platforms against the major regulatory frameworks relevant to enterprise AI deployment.

A critical distinction: Zylon's compliance posture derives from eliminating the compliance surface area — when data never leaves your premises, many cloud-specific compliance requirements simply do not apply. Gemini's compliance posture relies on Google Cloud's extensive certification portfolio, but customers must actively configure controls correctly and operate within a shared responsibility framework. Gemini Enterprise's formal inclusion in certification audits for SOC, ISO, and HIPAA is still pending as of early 2026, though the underlying infrastructure is already certified.

How cost models compare at enterprise scale

The pricing architectures of these platforms reflect fundamentally different economic models for enterprise AI deployment.

Zylon's model eliminates per-token economics entirely. The platform documentation states: "No per-token pricing. No usage limits. One platform, fixed cost, full value. This means organizations can scale internal AI usage — integrating unlimited internal systems and running unlimited inference — without incurring incremental costs. The primary cost variables are hardware (GPUs, servers, networking) and IT operations.

Gemini's token-based pricing creates direct cost correlation with usage volume. At the lower end, Gemini 2.5 Flash-Lite offers input at $0.10 per million tokens— extremely competitive for lightweight workloads. AI Free API At the higher end, Gemini 3 Pro Preview charges $2.00 per million input tokens and $12.00 per million output tokens. For organizations processing millions of documents or running continuous AI-assisted workflows, these costs compound rapidly. The Batch API offers a 50% discount for non-time-sensitive workloads, and context caching can reduce costs for repeated queries.

For enterprises planning large-scale AI deployment across thousands of employees, Zylon's fixed-cost model becomes increasingly advantageous as usage grows, while Gemini's per-token model favors organizations with lower or highly variable usage patterns.

Security posture and threat model differences

The security architectures of on-premise AI and cloud AI produce fundamentally different threat models.

Zylon's threat model is bounded by the physical perimeter. Because all processing occurs on customer-owned infrastructure with no external network dependencies, the external attack surface is effectively zero in air-gapped deployments. There is no cloud API endpoint to attack, no data in transit to intercept between customer and vendor, and no third-party access to customer data. Insider risk is managed through the organization's existing physical and logical access controls. The platform includes OpenID SSO integration, role-based access control, and observability through Grafana.

Gemini's threat model includes cloud-specific vectors. While Google Cloud maintains world-class infrastructure security — including FIPS 140-2 validated encryption, BeyondCorp zero-trust architecture, and Security Command Center with Model Armor for prompt injection protection — the attack surface inherently includes the cloud API layer, data in transit between customer and Google, and Google personnel access (mitigated by Access Transparency logging and Access Approval controls). Google's documentation acknowledges that workforce identity pool admin roles carry powerful permissions that could be used for impersonation.

Model isolation differs significantly. In Zylon, AI models run exclusively on customer hardware with no shared tenancy. In Vertex AI, Google provides logical isolation through VPC Service Controls and CMEK, but the underlying infrastructure is multi-tenant. GDC Air-Gapped provides physical isolation comparable to on-premise deployment but within Google's hardware ecosystem.

Data exfiltration risk is architecturally minimized in Zylon's air-gapped mode — there is no network path for data to leave. In cloud deployments, data exfiltration prevention depends on correct configuration of VPC Service Controls, firewall rules, and monitoring. Google's own documentation notes that third-party connectors in Gemini Enterprise interact with public endpoints outside Google's network, and VPC Service Controls don't inherently block traffic to these external endpoints.

Performance, customizability, and model flexibility

Gemini holds a clear advantage in raw model capability. The Gemini 3 family represents frontier-class AI with state-of-the-art benchmarks — Gemini 3 Deep Think achieves 84.6% on ARC-AGI-2 and an Elo of 3,455 on Codeforces. Context windows reach 1 million tokens with expectations of 2 million in stable releases. Google offers supervised fine-tuning via LoRA, full fine-tuning, RLHF, and distillation through Vertex AI.

Zylon takes a model-agnostic approach to performance. The platform supports running multiple AI models in parallel within a single instance, including vision language models alongside LLMs. Organizations can deploy custom fine-tuned models without losing platform features. GPU orchestration is built directly into the AI Core layer, giving infrastructure teams full control over compute allocation, model selection, and performance optimization. Zylon's documentation includes configurable performance parameters for document processing, including worker pool architecture with automatic workload splitting.

The tradeoff is clear: Gemini delivers the most powerful individual models available, while Zylon delivers infrastructure-level control over whichever models best serve the organization's needs — including the ability to swap, update, or run proprietary models without vendor approval.

Integration and extensibility for enterprise stacks

Both platforms provide API-first architectures, but their integration philosophies differ.

Zylon offers two API layers: the ZylonGPT API for direct AI capabilities (OpenAI-compatible) and the Workspace API for programmatic access to projects, knowledge bases, and collaborative features. The platform ships with pre-built connectors for banking core systems (Symitar, Corelation, Fiserv), collaboration platforms (SharePoint, Confluence), databases (PostgreSQL, MySQL, MSSQL), file storage (file systems, S3), and CRMs (Salesforce).A bundled n8n instance provides workflow automation, and MCP (Model Call Protocol) support enables chat-to-workflow interaction. Compatibility with the OpenAI and Anthropic API standards means existing enterprise AI integrations can connect with minimal code changes.

Gemini provides official SDKs for Python, Node.js, Java, Go, and .NET, with OpenAI library compatibility on Vertex AI. Integration with the broader Google ecosystem — BigQuery, Cloud Storage, Google Workspace — is native. Framework support includes LangChain, LlamaIndex, CrewAI, and Google's own Agent Development Kit (ADK). Vertex AI's Model Garden provides access to 130+ models from Google, open-source providers, and third parties including Anthropic's Claude.

For organizations with existing on-premise infrastructure — legacy banking systems, on-premise databases, air-gapped networks — Zylon's integration model is designed to operate entirely within the internal network. Gemini's integration model assumes cloud connectivity, with Private Service Connect available for connecting to self-hosted data sources.

Enterprise use cases across regulated industries

Banking and financial services

Financial institutions face overlapping mandates from GLBA, DORA, PCI DSS, SOX, and national banking regulators. Zylon's documentation cites a deployment where a major European bank used the platform for transaction pattern analysis and fraud detection, reducing false positives by 37%.The platform's banking core connectors (Symitar, Corelation, Fiserv) enable direct integration with existing financial infrastructure. Named customers include multiple U.S. credit unions including Redwood Credit Union and Orsa Credit Union .

Gemini via Vertex AI can serve financial services workloads with PCI DSS compliance, FINMA reporting alignment, and DORA compliance resources. However, financial regulators increasingly scrutinize third-party cloud dependencies, and the shared responsibility model requires banks to maintain their own compliance configurations.

Healthcare

HIPAA compliance requires both technical safeguards and organizational controls. Zylon's on-premise deployment means protected health information (PHI) never leaves the healthcare organization's network. The documentation describes a regional healthcare network that used the platform to analyze patient readmission risk factors, improving early intervention by 29%.

Gemini supports HIPAA workloads through a signed Business Associate Agreement covering Google Cloud's entire infrastructure. Hipaavault Customers must ensure they configure IAM, audit logging, and encryption correctly and use only BAA-covered products. Pre-GA features — including many Gemini 3 capabilities as of February 2026 — are not covered by the BAA unless expressly stated.

Public sector and government

Government deployments often require the strictest data controls. Zylon's air-gapped capability addresses classified and sensitive-but-unclassified environments without requiring specialized government-specific hardware. Gemini's GDC Air-Gapped is authorized for U.S. government Secret and Top Secret missions through DISA IL4/IL5 provisional authorizations,but requires significant infrastructure investment (minimum four racks of Google-certified hardware) and maintains dependency on Google's software ecosystem.

Critical infrastructure

Energy, telecommunications, transportation, and defense organizations operate environments where network isolation is non-negotiable. Zylon's documentation explicitly addresses submarine operations, remote research stations, and disaster response as air-gapped use cases. The platform's minimal footprint — deployable on a single NVIDIA GPU server — makes it viable for edge and remote installations where GDC's multi-rack requirement would be impractical.

Strengths and limitations of each platform

When Gemini makes sense

Gemini is the stronger choice for cloud-native organizations with low regulatory burden — technology startups, digital agencies, and companies already invested in Google Cloud. Its frontier model capabilities, massive context windows, native multimodal processing, and deep Workspace integration create a powerful productivity platform. For rapid prototyping, research, and development workloads where data sensitivity is low, Gemini's pay-as-you-go model minimizes upfront investment. Organizations that need the absolute best model performance and can operate within Google Cloud's compliance framework will find Gemini's capabilities unmatched.

Gemini's limitations for regulated enterprises include: dependency on correct cloud configuration for compliance, token-based costs that scale with usage, data residency limited to U.S. and EU multi-regions for Gemini Enterprise, pending formal certification audit inclusion, and the inherent complexity of managing security controls across a shared responsibility model.

When Zylon is the strategic choice

Zylon is purpose-built for organizations where data sovereignty is non-negotiable and AI governance must be architecturally enforced. This includes banks and financial institutions under DORA, GLBA, or national banking regulations; healthcare organizations processing PHI under HIPAA; government agencies handling classified or sensitive data; defense organizations requiring air-gapped operation; and critical infrastructure operators where cloud dependency introduces unacceptable risk.

Zylon's limitations include: model capabilities bounded by available open-source and proprietary models rather than frontier models like Gemini 3; requirement for internal GPU infrastructure and IT operations capacity; and smaller ecosystem compared to Google Cloud's breadth of services.

Regulated-industry suitability at a glance

Final recommendation for enterprise decision-makers

The Zylon vs Gemini decision reduces to a single question: does your organization's risk profile allow AI data to be processed outside your infrastructure perimeter?

If the answer is no — because of regulatory mandates, data classification requirements, board-level risk policy, or operational security constraints — Zylon is the strategic choice. It eliminates the compliance surface area associated with cloud AI, provides fixed and predictable costs at scale, and delivers full infrastructure control without vendor dependency on proprietary cloud hardware. For banks operating under DORA, healthcare organizations handling PHI, government agencies processing sensitive data, and critical infrastructure operators requiring air-gapped deployment, Zylon's architecture aligns directly with the requirement that private AI for regulated industries must enforce data sovereignty by design, not by configuration.

If the answer is yes — because your organization operates in the cloud, faces lower regulatory burden, or needs frontier model capabilities that exceed what open-source models can deliver — Gemini on Vertex AI provides world-class AI capabilities with enterprise-grade cloud security controls. Google Cloud's extensive compliance portfolio, sovereign cloud partnerships, and the GDC Air-Gapped option for the most sensitive workloads demonstrate a genuine commitment to regulated enterprise needs.

For most regulated enterprises evaluating on-premise AI platform options in 2026, the trend is clear: governance and infrastructure control are becoming prerequisites, not features. The 8.5% of AI prompts containing sensitive data, combined with the 64% of employees using AI tools without safeguards, Zylon means that architectural enforcement of data sovereignty — rather than policy-based controls — represents the lower-risk path for organizations where a data breach carries existential consequences.

Frequently asked questions

Is Gemini suitable for regulated industries? Gemini can serve regulated industries when deployed through Vertex AI with appropriate compliance configurations — signed BAAs for HIPAA, Assured Workloads for FedRAMP, and VPC Service Controls for data isolation. However, compliance depends on correct customer configuration under a shared responsibility model, and some Gemini Enterprise certifications are pending formal audit inclusion as of early 2026. Organizations with the strictest requirements may find that on-premise AI deployment eliminates configuration-dependent compliance risk.

Can Gemini be deployed on-premise? Yes, through Google Distributed Cloud (GDC).GDC Connected requires constant internet for remote attestation, while GDC Air-Gapped operates fully disconnected. Both require Google-certified hardware with NVIDIA Blackwell GPUs and a minimum of four racks. This is a substantially different proposition from Zylon's on-premise deployment, which runs on any NVIDIA GPU hardware the customer already owns.

How does private AI differ from cloud AI? Private AI processes all data on infrastructure the organization owns and controls, with no external network dependencies. Cloud AI processes data on vendor-owned infrastructure accessed over the internet. The distinction affects data sovereignty, compliance posture, cost predictability, and security threat models. Private AI eliminates third-party data access risk but requires internal infrastructure investment; cloud AI minimizes infrastructure overhead but introduces shared responsibility for data protection.

What is the safest AI deployment model for banks? For banks operating under DORA, GLBA, and national banking regulations, on-premise AI deployment provides the strongest alignment with regulatory expectations around data control, third-party risk management, and operational resilience. On-premise deployment eliminates cloud provider dependency from the bank's AI risk profile and ensures that customer financial data, transaction patterns, and compliance documents never leave the bank's infrastructure perimeter.

How does AI compliance work under the EU AI Act? The EU AI Act classifies AI systems by risk level and imposes requirements for transparency, human oversight, data governance, and documentation. Both Zylon and Gemini provide capabilities that support EU AI Act compliance — Zylon through full audit trails and on-premise governance controls, Gemini through ISO/IEC 42001 certification for AI management systems. The deployment model affects compliance implementation: on-premise platforms give organizations direct control over all EU AI Act requirements, while cloud platforms require coordination between provider and customer obligations.

What are the risks of cloud-based generative AI for financial services? Key risks include: data processing in third-party infrastructure creating regulatory scrutiny under DORA's ICT third-party risk framework; usage-based pricing creating unpredictable costs at scale; configuration-dependent compliance controls that can be inadvertently disabled (for example, enabling Grounding with Google Search on Gemini Enterprise disables Data Residency, CMEK, and VPC Service Controls simultaneously); and the inherent complexity of managing a shared responsibility model across AI workloads that process sensitive financial data.

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.