When Public Cloud AI Is Not Enough
For many organisations, cloud-based AI services work well. Some situations call for private deployment: • Data is too sensitive to leave your network (medical, legal, financial) • Regulatory or compliance requirements mandate on-premise processing • Management needs full visibility into how AI processes data • You need tight integration with internal systems behind a firewall
Cost Considerations
Private deployment often involves higher upfront infrastructure costs but may reduce ongoing per-query costs at scale. Consider: • Server hardware or private cloud infrastructure • Internal IT capacity for maintenance and monitoring • Model licensing and update costs • Total cost versus cloud AI at your expected volume
Operational Readiness
Before choosing private deployment, assess honestly: • Does your IT team have capacity to maintain AI infrastructure? • Do you have clear document structures and use cases? • Can you commit to ongoing model updates and document refreshes? • Is there executive sponsorship for the required investment?
A Practical Framework
Do not decide on trends alone. Evaluate: 1. Data sensitivity — how sensitive is the data AI will process? 2. Volume — how many queries or documents will the system handle? 3. Integration — how deeply must AI connect with internal systems? 4. Capacity — can your team maintain a private deployment? If sensitivity is high but volume is low, consider hybrid approaches. If both are high, private deployment may make sense.