Why Cloud-Only AI Is Not Enough

Three Problems That Hit Every Organisation at Scale

AI is essential — but cloud-only has real limits. At 1,000 users, the costs become uncomfortable. At 10,000 users, they become unacceptable. At any scale, the privacy and latency problems never go away.

Problem 1 — Data Privacy & Compliance

Every prompt you send to a cloud API is potentially logged, stored, and used for model training. Legal contracts, financial models, source code — all sent to a third party.

• Every prompt you send is potentially logged, stored, and used for model training
• Legal contracts, financial models, source code — all sent to a third party
• Competitive intelligence in your prompts can be exposed
• Provider terms of service can change without notice
• No guarantee that your data is isolated from other customers

Problem 2 — Latency

300ms delay is perceptible — users notice and abandon AI features. Coding assistants feel sluggish. Real-time chatbots feel broken with 500ms+ response starts.

• Latency compounds — a 5-turn conversation = 5 network round trips
• Latency varies — fast at 2am, slow at 2pm on a Tuesday
• No SLA guarantee on first-token response time
• Peak load on shared infrastructure affects your application
• Impossible to guarantee a consistent experience to your users

Problem 3 — Uncapped Cost

Every token costs money — input, output, and system prompt. Adding document context multiplies token usage 3–10×. Success punishes you — more users = immediate cost spike.

• You can be rate-limited and billed simultaneously
• Providers change pricing — GPT-4 pricing changed 3 times in 18 months
• No ceiling — a bug in your app can send millions of accidental tokens
• Vendor lock-in: migrating away costs engineering time and risk
• Deprecations force rewrites on the provider's schedule, not yours

The Solution Architecture

Each of these three problems has a direct, architectural solution in Foundry Local — not a policy workaround, an architecture that eliminates the problem by design.