Your Models, Anywhere
Why enterprise AI needs to connect to models wherever they live—cloud, on-premises, or air-gapped.
The enterprise reality
Most AI interfaces assume you'll use their model, through their API, with your data leaving your network. That works fine for consumer applications. It doesn't work for many enterprises.
Banks can't send customer data to external APIs. Healthcare systems have HIPAA constraints. Defense contractors operate in classified environments. Many organizations have legitimate reasons to run AI on their own infrastructure.
At the same time, organizations don't want to be locked into a single model provider. They want the flexibility to use the best model for each task—whether that's Claude for analysis, GPT for generation, or a local model for sensitive queries.
The interface problem
Every model has a different interface. Cloud APIs work one way. Local models work another. Your organization's private fine-tuned model works yet another way.
Users shouldn't need to care about this. They have questions; they want answers. The plumbing should be invisible.
What users actually want
- ✓ One interface for all models
- ✓ Ability to compare responses across models
- ✓ Control over which models see which data
- ✓ Same experience on web, desktop, and mobile
Where models live
Cloud APIs
The standard approach. OpenAI, Anthropic, Google, xAI—managed services with simple API access.
Private cloud
Models deployed in your VPC. Still cloud-hosted, but within your security boundary. Azure OpenAI, AWS Bedrock, GCP Vertex.
On-premises
Models running in your data center. Full control over hardware, data, and access. Ollama, vLLM, TGI.
Air-gapped
Completely isolated networks with no internet connectivity. Models installed locally, no external communication.
Desktop local
Models running directly on user workstations. Apple Silicon, NVIDIA GPUs, or even CPU-only for smaller models.
Custom fine-tuned
Your own models trained on proprietary data. Domain-specific, organization-specific, deployed wherever makes sense.
The universal connector approach
Rather than building different interfaces for different model sources, we built a universal connector that abstracts the differences:
From the user's perspective, they just pick a model and ask a question. The connector handles authentication, API differences, error handling, and response formatting.
Real deployment scenarios
Bank compliance team
Uses cloud models for general research, on-premises fine-tuned models for regulatory analysis, with automatic routing based on query sensitivity.
Healthcare analytics
All patient-related queries stay on-premises. Administrative queries can use cloud models for better performance.
Defense contractor
Entirely air-gapped deployment. Desktop app with local models only. No network connectivity required.
Research team
Multi-model deliberation. Query five models simultaneously, synthesize results. Best of all worlds.
Same experience everywhere
Enterprise users work across devices. The interface should follow them:
Web
Zero install, works anywhere with a browser. Cloud models only.
Desktop (macOS, Windows, Linux)
Native apps with local model support. Global hotkeys, menu bar access, works offline.
iOS
Mobile access to cloud models. Quick queries on the go.
Android
Same mobile experience. Cloud models with secure authentication.
Security considerations
API keys stay local
Your API credentials are stored locally on your device. They're never sent to us. We're just an interface, not a proxy.
Data routing control
Configure which models can see which types of queries. Sensitive data can be restricted to on-premises models only.
Audit logging
Full audit trail of which users queried which models with what. Exportable for compliance purposes.
SSO integration
SAML/OIDC support for enterprise identity providers. Okta, Azure AD, whatever you use.
The takeaway
Enterprise AI isn't about picking one model or one deployment pattern. Different use cases need different solutions. The interface should handle that complexity invisibly.
Your models, wherever they live. Your data, under your control. One interface to access it all.
Deploy multi-AI anywhere
Onyx Legion supports cloud, on-prem, and air-gapped deployment.