Private AI Infrastructure for Business
When your business relies on AI for daily operations, where that AI runs matters as much as what it does. Private AI infrastructure gives you control over your data, your costs, and your compliance position—without giving up the capabilities you need.
Why businesses are rethinking public AI
Most AI inference today happens on shared infrastructure owned by a handful of large providers. Your prompts, your documents, and your outputs pass through systems you don't control, in jurisdictions you may not have chosen.
For many use cases, this is acceptable. For others, it's a risk.
Regulated industries, client confidentiality requirements, and internal data policies often conflict with the default model of sending everything to a third party. The question isn't whether public AI works—it's whether it works for your specific constraints.
What "private AI" actually means
Private AI refers to inference that runs on dedicated infrastructure, isolated from other users, with data that doesn't leave a defined boundary.
It's not about building your own models. It's about choosing where existing models run—and ensuring your inputs and outputs stay under your control.
Private doesn't mean disconnected. It means inference happens on infrastructure that answers to you, not to a shared cloud provider's terms.
How private AI fits into existing systems
If your applications already call OpenAI-compatible endpoints, switching to private infrastructure typically requires changing a single configuration value: the endpoint URL.
The request format stays the same. The response format stays the same. Your existing code, integrations, and workflows continue to work—just against a different backend.
This compatibility matters because it eliminates the need to rebuild. You're not adopting a new paradigm. You're redirecting where inference happens.
When private AI is the right choice
Private AI infrastructure makes sense when:
- Your data policies prohibit sending certain content to third parties
- You need to demonstrate data residency for compliance or contractual reasons
- Cost predictability matters more than pay-per-token flexibility
- You want to avoid vendor lock-in at the inference layer
It may not be necessary if your workloads aren't sensitive, your scale is unpredictable, or your compliance requirements are minimal.
The decision is situational, not ideological.
Why EU-based inference matters
For businesses operating in Europe, or serving European customers, data residency is often non-negotiable.
Running inference within the EU means:
- Data subject to GDPR jurisdiction
- No transatlantic data transfers for inference workloads
- Clearer compliance position for audits and client assurances
This is particularly relevant for legal, healthcare, finance, and consulting work—where demonstrating that data stayed within a defined boundary is part of the value you deliver.
How Juice Factory supports private AI
Juice Factory provides dedicated inference infrastructure located in the EU. Your data goes in, gets processed, and comes out—without being stored, logged, or shared.
The interface is OpenAI-compatible. The runtime is private. Data residency is guaranteed.
This isn't a managed service in the traditional sense. It's infrastructure that behaves like a utility: you connect, you use it, you control what happens to your data.
Need dedicated capacity?
For organizations with high-volume inference requirements or specific isolation needs, we offer dedicated infrastructure as an extension of our managed AI inference service.
This provides private compute capacity exclusively for your workloads—same API, same compliance guarantees, with infrastructure reserved for you.
Next steps
If you're evaluating whether private AI infrastructure fits your requirements, we can help you understand the technical and compliance implications.
Request access to learn more about how Juice Factory works, what integration looks like, and whether it fits your use case.