Zylon
A private enterprise AI platform for regulated industries, deployable on the enterprise's own infrastructure or in a fully air-gapped environment.
About Zylon
Zylon is an enterprise AI platform for regulated industries that packages local large language models, a vector database, and GPU orchestration into self-contained AI infrastructure deployed in the enterprise's own environment: a cloud VPC, local servers, or a fully air-gapped environment. According to official documentation, data never leaves the enterprise's own environment, including the AI models, which all run locally; this makes it suitable for organizations with strict data sovereignty and compliance requirements. The platform provides low-level APIs (ZylonGPT) and high-level APIs (Workspace) for turning internal company knowledge into usable AI workflows. Unlike token-priced cloud services, the vendor says Zylon uses a fixed-cost, unlimited-usage model; the official comparison page says deployment to availability takes about 3 hours, but the enterprise must provide its own GPU infrastructure and bear upfront hardware costs. Platform license pricing is not public; contact the vendor for a quote.
Best for
Best use cases
Running generative AI in a fully air-gapped environment with no external cloud dependency
Turning internal enterprise documents and knowledge bases into a question-answering, searchable AI assistant
Industry applications that require compliance evidence for data retention, auditing, and AI governance
Compare before choosing
The official comparison page describes Zylon as a complete enterprise AI platform deployed on the organization's own infrastructure, suited to regulated industries and organizations needing full data sovereignty; the trade-off is deployment within hours, built-in governance, and unlimited usage, while requiring GPU infrastructure and upfront hardware costs.
The official comparison table states that, compared with ChatGPT/Copilot, Zylon keeps data local, supports air-gapped deployment, and offers predictable costs rather than token-based billing; compared with building in-house, deployment takes about 3 hours instead of 12-18 months, maintenance and updates are provided by the platform rather than an internal team, and users can use any model with local data connectors.
The official page states that Zylon differs from cloud AI assistants such as Claude by supporting fully private on-premises deployment.
Features
Self-contained AI infrastructure
The vendor describes Zylon AI Core as self-contained AI infrastructure that includes local large language models, a vector database, and GPU orchestration, deployable in a cloud VPC, on local servers, or in a fully air-gapped environment.
Data sovereignty
Official documentation states that data never leaves the enterprise's own environment and that everything, including the AI models, runs locally to meet compliance requirements.
Governance and auditing
Official documentation states that Zylon provides separate documentation for AI governance, retention, evidence collection, and control mapping, and requires reauthentication before sensitive actions such as deleting an organization or creating an API token.
Unlimited usage
Official documentation states that, unlike token-priced cloud services, Zylon has no token or inference-count limits, so scaling usage adds no extra cost.
Open APIs
Official documentation states that the platform provides low-level (ZylonGPT) and high-level (Workspace) APIs, all running within the enterprise's own secure infrastructure for integration with internal systems.
Rapid deployment
Official documentation states that deployment to any infrastructure takes 3 hours, with no vendor lock-in, and supports full customization and white-labeling.
Pros
- All data and AI models run in the enterprise's own environment; the vendor states that air-gapped deployment is supported for strict compliance requirements
- The vendor states that token and inference counts are unlimited, making costs fixed and predictable
- The vendor states that deployment to any infrastructure takes about 3 hours, with no vendor lock-in
- Built on experience from the open-source PrivateGPT project, with both low-level and high-level APIs
Cons
- The enterprise must prepare its own GPU infrastructure and bear upfront hardware costs.