Self-Hosted AI vs Cloud AI: A Compliance Decision Matrix
When does self-hosted AI make sense for defense manufacturers? A practical framework for choosing between local inference and cloud services.
Defense manufacturers evaluating AI adoption face a fundamental architecture decision: run models locally or use cloud APIs. This is not primarily a technology decision. It is a compliance decision. The right answer depends on what data your AI will touch.
The Decision Matrix
Map your AI use cases against two dimensions: data sensitivity and latency requirements.
Public data + low latency tolerance: Cloud AI is fine. Marketing content generation, public-facing chatbots, general research queries. Use GPT-4, Claude, or Gemini via API. No compliance issues.
Public data + real-time latency: Edge or self-hosted may be better for cost. Quality inspection cameras, real-time sensor analysis. Cloud round-trip adds 200-500ms per inference. Self-hosted GPU inference is 5-50ms.
CUI data + any latency: Self-hosted is required. Any AI processing that involves Controlled Unclassified Information must stay within your security boundary. Sending CUI to OpenAI, Anthropic, or Google violates NIST 800-171 Control 3.1.3 (CUI flow control) unless the provider has a FedRAMP authorization at the appropriate level.
CUI data + real-time: Self-hosted is the only option. Combining CUI sensitivity with latency requirements eliminates cloud entirely.
The Cost Reality
A single NVIDIA RTX 4090 ($1,600) can run a 30B parameter model at 20-40 tokens per second. That is sufficient for most manufacturing AI use cases: document analysis, quality reports, process optimization suggestions, compliance gap assessment.
Compare to cloud: GPT-4 costs $30-60 per million tokens. At moderate usage (100K tokens/day), that is $900-1,800 per month. The GPU pays for itself in 1-2 months.
The hidden cost of cloud AI for defense work is not the API bill. It is the compliance remediation when an auditor discovers CUI was sent to a non-FedRAMP cloud service.
The Governance Layer
Self-hosting the model is necessary but not sufficient. You also need:
Access control: Who can query the model? Role-based access prevents unauthorized use.
Audit logging: Every prompt and response must be logged for NIST 800-171 audit requirements.
Data classification: The system must know which queries contain CUI and enforce routing rules accordingly.
Output filtering: Prevent the model from including CUI in responses that might be shared externally.
This governance layer is what separates "we have a GPU running Ollama" from "we have a compliant AI platform." The infrastructure is commodity. The governance is the product.
Learn more about governed AI infrastructure at aegisos.ai.