The Four Options to implement AI for the enterprise
Simplify, accelerate, and transform with one connected private AI ecosystem.
Option 1: Cloud AI Services (ChatGPT, Claude, Copilot)
What it is
AI tools accessed via browser or API, fully hosted in the vendor’s cloud.
Best for
Startups and teams experimenting with non-sensitive data.
Key trade-offs
Fast and easy to start, but company data leaves your infrastructure, governance is limited, pricing scales unpredictably, and compliance can be difficult to prove.
Option 2: “Secure” SaaS AI (Vendor-Hosted Private Instances)
What it is
Enterprise SaaS plans with dedicated or isolated cloud environments.
Best for
Organizations with moderate compliance needs that accept vendor-hosted data.
Key trade-offs
More security features than standard SaaS, but data still resides in the vendor’s cloud, customization is limited, recurring costs are higher, and full audit or sovereignty control isn’t guaranteed.
Option 3: Build Your Own AI Platform
What it is
A fully custom AI stack built in-house using open-source models and infrastructure.
Best for
Large tech-driven organizations with dedicated AI engineering teams.
Key trade-offs
Maximum control and flexibility, but requires 12–18 months, significant engineering resources, ongoing maintenance, and high long-term costs.
Option 4: Zylon (Private AI Platform)
What it is
A complete AI platform deployed on your own infrastructure, ready for enterprise use.
Best for
Regulated industries and organizations requiring full data sovereignty.
Key trade-offs
Deploys in hours with built-in governance and unlimited usage, but requires GPU infrastructure and upfront hardware investment.
Speed of Cloud, Control of On-Premise
Zylon solves the impossible tradeoff: you get deployment speed similar to cloud AI (3 hours vs. instant) with the data sovereignty of building in-house—without the 12-18 month engineering project.
How we do it:
Pre-integrated full stack (AI Core + Workspace + API)
Single-command deployment
Automatic GPU optimization and model configuration
Built-in monitoring, HA, and governance
Enterprise support from day one
Cost comparison
Cloud AI
$30/user/month = $15,000/month = $180,000/year
Plus API usage fees (variable, can be 10x the subscription cost)
Total: $200K-$500K+/year (depending on usage)
Build In-House:
Total: $500K-$1.2M+/year ongoing
Zylon
Unlimited usage, no per-token fees
White Box, Not Black Box
Unlike cloud AI or secure SaaS, Zylon gives you complete transparency.
What you can access:
All tech layers configurations
Complete audit logs and data flows
GPU and infrastructure controls
Model parameters and behavior
Security architecture and encryption
Why this matters:
Your security team can audit everything
Your compliance team can prove data sovereignty
Your engineering team can customize when needed
No vendor black boxes when regulators ask questions
Private AI Built on Battle-Tested Foundation
Zylon is built by the creators of PrivateGPT—the open-source project with 57,000+ GitHub stars used by Google, Meta, J.P. Morgan, and thousands of developers worldwide. We took that foundation and made it enterprise-ready.
What this means:
Years of production usage and refinement
Community-tested and validated
Enterprise-hardened by real-world deployment
Not a startup experiment—proven infrastructure
Across the most regulated industries
Financial Services - Credit unions, banks, and investment firms protecting member and client data
Defense & Critical Infrastructure - Defense contractors, energy companies, and manufacturers securing classified information and proprietary IP
Government & Public Sector - Government agencies and public institutions maintaining citizen data sovereignty
Healthcare - Healthcare networks protecting patient information under HIPAA
Compliant with SOC 2, GLBA, FINRA, and NCUA requirements.
Questions We Hear Often
Common questions about Zylon and enterprise AI governance