A private RAG pipeline is the only practical way to get secure retrieval augmented generation on an open weight model, grounding it in sensitive data without breaking the data leg of sovereignty. [1]. Where this picks up from the last post Last time I defined sovereign AI as three legs: your data stays in your

• Inference & Infrastructure
• Sovereign AI

Hoonify and Together AI both run open-weight models behind an OpenAI-compatible API. Change your base URL, keep your code, pay per token. For a lot of workloads, they’re interchangeable. The decision gets easy once you ask a different question than most comparison pages ask: after you send a prompt, who holds it, who could read

• Inference & Infrastructure
• Sovereign AI

Everything you need to move a workload without a rewrite. PDF + eval script, straight to your inbox.

• Cost & Pricing
• Inference & Infrastructure

Private AI infrastructure for enterprise teams. A self‑hosted LLM platform that runs on hardware you own.

• Inference & Infrastructure
• Sovereign AI

Running AI workloads in classified or export-controlled environments means your compute platform cannot depend on external networks, cloud APIs, or vendor-managed infrastructure. An air-gapped HPC platform operates in complete isolation, with every component required to schedule, execute, and monitor jobs packaged on-site. As a result, that constraint changes how you evaluate hardware, software stacks, and

• HPC & Defense
• Inference & Infrastructure
• Sovereign AI

From autocomplete to frontier intelligence Not that long ago, you were typing an email and the app suggested what you were going to type next. You may have wondered: “How did it do that?” At the time, very few people realized that the ability to make those suggestions would create the world we live in

• Cost & Pricing
• Inference & Infrastructure
• Sovereign AI