What Is the Difference Between Cloud AI and On-Premise AI?

Cloud AI is the default for most businesses. On-premise is for specific cases. Here's how to choose.

Quick Comparison

Cloud AI Explained

Cloud AI means using APIs from providers:

• Vendors: OpenAI (ChatGPT), Anthropic (Claude), Google (Gemini)
• How it works: You send data to them, they process, send results back
• Pricing: Pay per token (roughly per word)
• Pros: Best models, easy setup, auto-updates, scales instantly
• Cons: Data leaves your control, ongoing costs, vendor dependency

On-Premise AI Explained

Running AI on your own hardware:

• Models: Open-source like Llama, Mistral, or proprietary
• Hardware: GPUs ($5k-100k depending on size)
• How it works: Everything runs locally, no data leaves
• Pros: Complete control, no per-use costs, data sovereignty
• Cons: Higher upfront cost, needs expertise, lower performance

When to Choose Cloud

Cloud is right for most businesses:

1. No regulatory requirement to keep data local
2. Want the best performing models
3. Need to scale quickly
4. Limited technical expertise in-house
5. Variable usage (pay only for what you use)

When to Choose On-Premise

On-premise when:

1. Contractual requirement for data sovereignty
2. Processing sensitive data (healthcare, defense)
3. Regulatory compliance requires it
4. High, consistent volume (cost-effective at scale)
5. Need to customize model deeply

Hybrid Approach

Many businesses use both:

• Sensitive data: On-premise or private cloud
• General tasks: Cloud AI
• Routing: Smart routing based on data sensitivity

Japanese Considerations

• APPI: Cross-border transfer requires safeguards
• Japanese cloud: Azure OpenAI in Japan region, local providers
• Government: Some agencies require on-premise

Greene Solutions Recommendation

We typically recommend:

• Start with cloud (unless you have a hard requirement)
• Use Japanese cloud regions when possible
• Implement hybrid if you have mixed data sensitivity