Two platforms, two paradigms

ChatGPT Enterprise, powered by OpenAI's GPT-5.2, serves 5 million paying business users with virtually unlimited access to the world's most capable commercial language model. It offers SOC 2 Type 2 certification, optional HIPAA BAA, data residency across 10 regions, and 60+ application integrations. It is a cloud-first SaaS product optimized for broad enterprise adoption.

Zylon takes the opposite approach. Built by the creators of PrivateGPT — the open-source private AI project with 57,000+ GitHub stars — Zylon is a turnkey AI infrastructure platform that deploys on-premise, in private cloud, or in fully air-gapped environments. It runs open-source LLMs (Llama, Mistral, DeepSeek) locally, offers fixed-cost pricing with no per-token fees, and is designed from the ground up for organizations in finance, healthcare, defense, and government where data sovereignty is non-negotiable.

The right choice depends on your threat model, regulatory obligations, and whether you can tolerate any third-party data processing.

→ Learn more about Zylon's architecture | → Read the Zylon documentation

What is Zylon? Private AI built for regulated enterprises

Zylon is a complete, self-contained AI infrastructure — not a wrapper around a cloud API. Founded in 2023 by Iván Martínez Toro and Daniel Gallego Vico, the company emerged from PrivateGPT, which reached #1 across all GitHub categories twice in 2023 and has been adopted by organizations including Google, Meta, and J.P. Morgan. Zylon raised a $3.2 million pre-seed round led by Felicis Ventures and announced a strategic partnership with Telefónica Tech in May 2025 to expand secure on-premise AI globally.

The platform consists of three layers: the AI Core (local LLMs, vector databases, GPU orchestration, and document ingestion), an API Gateway with OpenAI-compatible endpoints, and a Workspace providing a collaborative interface for non-technical users. It deploys with a single command and reaches production readiness in under one week. Critically, Zylon runs on a single GPU — eliminating the assumption that on-premise AI requires massive infrastructure investment.

→ Explore Zylon's platform for financial services | → See Zylon's deployment options

What is ChatGPT Enterprise? OpenAI's cloud AI for business

ChatGPT Enterprise is OpenAI's flagship business offering, providing access to GPT-5.2 with a 128K token context window, advanced data analysis, custom GPTs, shared projects, AI agents, and deep research capabilities. It includes enterprise-grade admin controls: SAML SSO, SCIM provisioning, role-based access control, domain verification, and a compliance API with audit logging.

OpenAI does not use enterprise customer data for model training by default. Enterprise customers get configurable data retention (minimum 90 days), AES-256 encryption at rest, TLS 1.2+ in transit, and optional Enterprise Key Management. In January 2026, OpenAI launched ChatGPT for Healthcare with dedicated HIPAA BAA support, and in February 2026 introduced Lockdown Mode to limit data exfiltration risk for high-risk users.

Deployment models compared: on-premise vs. SaaS cloud AI

The deployment model is the foundational difference between these platforms and shapes every downstream consideration — from compliance posture to cost structure.

For regulated industries, the deployment model determines whether your AI usage falls under your existing security perimeter or introduces a new third-party data processor. Zylon's air-gapped capability is particularly relevant for defense contractors and agencies handling classified information — the platform has been described as capable of operating in "submarine-grade disconnected environments."

Data privacy and sovereignty: the core differentiator

Data sovereignty is where these platforms diverge most sharply. With Zylon, no data ever touches external servers — the LLM, vector database, and all processing run within customer infrastructure. There are no third-party data processing agreements to negotiate because no third party ever sees the data.

ChatGPT Enterprise takes a responsible but fundamentally different approach. OpenAI commits to not training on enterprise data and offers configurable retention periods. However, data is still processed on OpenAI's infrastructure, creating a data processing relationship subject to OpenAI's privacy practices, applicable law, and potential legal discovery. This is not theoretical: in January 2026, a federal court ordered OpenAI to produce 20 million de-identified ChatGPT logs as part of the New York Times litigation. While Enterprise workspaces were excluded from that specific order, the precedent underscores that cloud-hosted data exists within a legal jurisdiction you do not control.

For organizations bound by banking secrecy laws, HIPAA's minimum necessary standard, or government classification requirements, the distinction between "we promise not to look at your data" and "your data physically cannot leave your building" is the difference between contractual assurance and architectural guarantee.

Italy's €15 million GDPR fine against OpenAI in 2025 further highlights the regulatory risk of cloud-based AI data processing in European jurisdictions.

→ Read about Zylon's data sovereignty approach

Compliance and governance under GDPR, HIPAA, SOC 2, and the EU AI Act

The EU AI Act's primary compliance deadline of August 2, 2026 creates urgent obligations for AI systems used in credit scoring, fraud detection, clinical decision support, and critical infrastructure. Non-compliance carries penalties of up to 7% of global annual revenue.

The EU AI Act requires providers of high-risk systems to maintain technical documentation, demonstrate data provenance, and enable regulatory audit access. On-premise deployment inherently simplifies these obligations because the deploying organization maintains complete control over the AI system's data pipeline, model behavior, and audit infrastructure. The ECB has already signaled "significant increase" in supervisory attention to AI in banking, with targeted reviews of credit scoring and fraud detection systems.

For healthcare organizations, 82% of AI implementations have encountered HIPAA-related barriers. Zylon's architecture eliminates the most fundamental barrier — the need to transmit protected health information to a third-party cloud provider.

Cost model comparison: tokens vs. fixed infrastructure

The pricing philosophies reflect fundamentally different economic models. ChatGPT Enterprise charges approximately $60 per user per month with a minimum of roughly 150 seats, creating a floor of approximately $108,000 annually before advanced feature credits. Costs scale linearly with headcount and usage of premium features.

Zylon uses a fixed-cost model with no per-token pricing and no usage limits. After the initial infrastructure investment (the platform runs on a single GPU), the marginal cost of additional queries is effectively zero.

Recent Lenovo research (2026 edition) quantifies the on-premise advantage for sustained AI workloads: self-hosted infrastructure yields an 8× cost advantage per million tokens versus cloud IaaS and up to 18× versus frontier Model-as-a-Service APIs. On-premise achieves breakeven in under 4 months at high utilization against on-demand cloud pricing. Over a standard five-year hardware lifecycle, savings per server can exceed $5 million compared to equivalent cloud deployments.

For a 500-person organization using ChatGPT Enterprise, the annual cost reaches approximately $360,000 — and grows with every new user. Zylon's fixed-cost model becomes increasingly advantageous as adoption scales across the organization.

Security posture and threat model differences

The enterprise AI threat landscape has shifted decisively. The LayerX 2025 report found that AI is now the #1 uncontrolled channel for corporate data exfiltration, with 77% of employees pasting company data into AI tools and 40% of uploads containing PII or payment card data. CrowdStrike's 2026 Global Threat Report documented attackers injecting malicious prompts into GenAI tools at more than 90 organizations.

Zylon's security model is fundamentally different from any cloud-based AI. Because the platform runs entirely within the customer's security perimeter, the attack surface excludes all network-based data exfiltration vectors. There is no API call to intercept, no data in transit to a third party, and no cloud infrastructure to compromise. The threat model reduces to the same physical and network security that already protects the organization's other sensitive systems.

ChatGPT Enterprise has invested heavily in security — AES-256 encryption, Enterprise Key Management, Lockdown Mode (February 2026), and a compliance logging platform. These are meaningful controls. However, the fundamental architecture requires data to traverse a network to OpenAI's infrastructure, creating exposure to supply chain attacks, legal discovery, and the residual risk inherent in any third-party data processing relationship.

OWASP ranks prompt injection as the #1 LLM vulnerability (LLM01:2025). Both platforms face this risk, but the consequences differ: a prompt injection against a cloud-hosted service could potentially exfiltrate data to the internet, while the same attack against an air-gapped Zylon deployment has no external network path to exploit.

Performance, customizability, and model flexibility

ChatGPT Enterprise offers access to GPT-5.2, currently among the most capable commercial language models, with a 128K token context window and specialized capabilities in reasoning, code generation, and research. For raw model performance on general tasks, GPT-5.2 likely outperforms the open-source models available through Zylon.

However, Zylon offers something ChatGPT cannot: complete model flexibility and technological independence. Organizations can deploy Llama, Mistral, DeepSeek, or any compatible open-source model and swap models as the rapidly evolving open-source ecosystem advances — without vendor dependency. The platform stays "always up-to-date" with the latest open-source LLMs. For domain-specific tasks in finance or healthcare, fine-tuned open-source models running locally can match or exceed general-purpose commercial models while maintaining full data control.

Zylon's OpenAI-compatible API enables organizations to build custom AI applications, integrate with workflow automation tools like n8n and LangChain, and extend functionality through MCP (Model Context Protocol) — all without exposing data to external services.

→ Learn about Zylon's API and integrations

Integration and extensibility for enterprise workflows

Both platforms offer integration capabilities, but with different philosophies. ChatGPT Enterprise provides 60+ pre-built connectors (Slack, SharePoint, GitHub, Atlassian, HubSpot) and shared projects for team collaboration. These integrations are powerful but route data through OpenAI's infrastructure.

Zylon integrates with SharePoint, Confluence, Claromentis, and file systems for knowledge base ingestion, supports n8n for workflow automation and LangChain for custom application development, and exposes an OpenAI-compatible API that serves as a drop-in replacement for existing OpenAI integrations. This means organizations can migrate existing AI workflows to Zylon's private infrastructure without rewriting application code.

Enterprise use cases across regulated industries

Orsa Credit Union, a credit union working with Zylon, stated: "Partnering with Zylon allows us to bring [our members] smarter, more secure banking, as privacy is paramount." A defense contractor using the platform reported reducing document review time by 64% while maintaining security classification compliance.

Strengths and limitations of each platform

ChatGPT Enterprise strengths: Superior raw model performance (GPT-5.2), massive ecosystem of 60+ integrations, 800 million weekly users driving rapid product iteration, comprehensive admin console, broad compliance certification portfolio, and extensive deep research and agent capabilities.

ChatGPT Enterprise limitations: Requires data to leave the organization, multi-tenant cloud architecture, minimum ~$108K annual commitment that scales with headcount, FedRAMP authorization incomplete, GDPR enforcement risk (€15M fine precedent), and dependency on OpenAI's continued operations and pricing decisions.

Zylon strengths: True data sovereignty with air-gapped capability, fixed-cost pricing with no per-token fees, open-source model flexibility eliminating vendor lock-in, single-GPU deployment simplicity, production readiness in under one week, and architectural (not just contractual) privacy guarantees.

Zylon limitations: Open-source models may lag frontier commercial models on general benchmarks, smaller integration ecosystem than ChatGPT, earlier-stage company with a smaller team, and requires organizations to maintain their own GPU infrastructure.

When ChatGPT Enterprise makes sense

ChatGPT Enterprise is the right choice for organizations that need the most powerful commercial language model available, operate in industries without strict data sovereignty requirements, have large distributed teams that benefit from SaaS simplicity, and can accept contractual data protection assurances. It excels for general knowledge work, content creation, code assistance, and research across non-regulated business functions.

When Zylon is the strategic choice for private AI

Zylon is the strategic choice when data cannot leave your infrastructure — period. This includes banks and credit unions bound by financial secrecy regulations, healthcare organizations processing PHI that cannot accept the residual risk of cloud data processing, government agencies and defense contractors requiring air-gapped operation, any organization preparing for EU AI Act high-risk compliance by August 2026 that needs full control over audit trails and data governance, and enterprises seeking predictable AI costs that do not scale linearly with headcount.

With the EU AI Act deadline approaching and AI-related data exfiltration becoming the #1 enterprise security concern, the strategic question is increasingly not "which AI is smarter" but "which AI architecture can we actually deploy within our regulatory and security constraints."

→ Schedule a Zylon demo | → Read the Zylon technical documentation

Final recommendation for enterprise decision-makers

The Zylon vs. ChatGPT Enterprise decision is ultimately about architectural philosophy. If your organization's regulatory environment, security posture, or data governance policies permit third-party cloud processing of sensitive data, ChatGPT Enterprise offers a powerful, feature-rich platform backed by the world's leading AI company. If they do not — or if you anticipate they will not once the EU AI Act's August 2026 deadline arrives — Zylon provides the only self-contained, ready-to-deploy AI platform that keeps everything within your walls.

For regulated industries, the trend is clear. The ECB is increasing supervisory scrutiny of AI in banking. HIPAA's previously optional safeguards are becoming mandatory. The EU AI Act imposes penalties of up to 7% of global revenue. And AI tools have become the primary channel for corporate data exfiltration. In this environment, the architectural guarantee of on-premise deployment is not a technical preference — it is a governance imperative.

The bottom line: Evaluate ChatGPT Enterprise for general enterprise productivity. Evaluate Zylon when your data, your regulators, or your board demands that AI stays private.

Frequently asked questions

What is the difference between on-premise AI and cloud-based AI for enterprises? On-premise AI platforms like Zylon run entirely within your organization's own servers and infrastructure. Data never leaves your security perimeter. Cloud-based AI platforms like ChatGPT Enterprise process data on the provider's infrastructure, with contractual commitments governing data handling. The key difference for regulated industries is whether data sovereignty is guaranteed by architecture or by contract.

Is ChatGPT Enterprise HIPAA compliant? OpenAI offers a Business Associate Agreement (BAA) for ChatGPT Enterprise, ChatGPT for Healthcare (launched January 2026), and eligible API customers. However, HIPAA compliance is not automatic — organizations must properly configure the platform, implement access controls, train staff, and monitor audit logs. The BAA for API access only covers Zero Data Retention–eligible endpoints, excluding several advanced features.

How does the EU AI Act affect enterprise AI deployment decisions? The EU AI Act's primary compliance deadline for high-risk AI systems is August 2, 2026, with penalties up to 7% of global annual revenue. High-risk categories include AI used in credit scoring, fraud detection, clinical decision support, and critical infrastructure. The Act requires comprehensive documentation, data governance, and audit access — requirements that are inherently simpler to meet with on-premise deployments where the organization controls the entire data pipeline.

What does Zylon's on-premise AI platform cost compared to ChatGPT Enterprise? ChatGPT Enterprise costs approximately $60 per user per month with a minimum of roughly 150 seats, creating a floor around $108,000 annually. Zylon uses fixed-cost pricing with no per-token fees or usage limits. Industry research from Lenovo (2026) shows on-premise AI achieves an 8× cost advantage per million tokens versus cloud infrastructure and breaks even in under 4 months at high utilization.

Can Zylon operate in an air-gapped environment with no internet connectivity? Yes. Zylon supports fully air-gapped deployment with zero internet connectivity, making it suitable for classified government environments, defense contractors, and any organization requiring complete network isolation. The platform is entirely self-contained, running local LLMs, vector databases, and all processing without external dependencies.

Which AI platform is better for banking and financial services? For banks, credit unions, and financial institutions subject to banking secrecy laws, Basel requirements, and increasing ECB supervisory scrutiny, Zylon's on-premise deployment eliminates the regulatory risk of transmitting sensitive financial data to a third-party cloud provider. ChatGPT Enterprise can serve non-sensitive banking workflows, but core functions like AML detection, loan processing

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.