Who this is for
IT leaders, compliance officers, and business owners evaluating AI deployment options for data-sensitive environments.
Question this answers
Should we use public AI services (like ChatGPT, Azure OpenAI) or deploy AI privately on our own infrastructure?
What you'll leave with
- What public and private AI actually mean in practice
- Security and compliance implications of each approach
- Cost and capability tradeoffs
- When hybrid approaches make sense
The core difference
When we say "public AI" and "private AI," we're talking about where the AI runs and where your data goes — not whether the technology is open-source or proprietary.
Public AI: Your data is sent to a third-party provider's servers for processing. Examples: ChatGPT, Google Gemini, Azure OpenAI Service.
Private AI: The AI model runs on your infrastructure (or a dedicated cloud instance). Your data never leaves your control.
Public AI: what you get
Advantages:
- Access to the most powerful models (GPT-4o, Claude, Gemini)
- No infrastructure to manage
- Pay-per-use pricing — low barrier to entry
- Latest capabilities automatically available
- Enterprise plans include data processing agreements and security certifications
Concerns:
- Data is processed on third-party servers
- Data may be used for model training (free tiers) — enterprise plans usually opt out
- No control over model updates that might change behaviour
- Vendor lock-in risk
- May not comply with data residency requirements (Australian data staying in Australia)
Private AI: what you get
Advantages:
- Complete data control — nothing leaves your environment
- Compliance with strict data residency and privacy requirements
- No vendor dependency on model availability or pricing
- Customisable — fine-tune models to your specific domain
- Predictable cost at scale (no per-token pricing)
Concerns:
- Smaller models — open-source models are capable but not yet at GPT-4o level for all tasks
- Infrastructure cost and management overhead
- Requires technical expertise to deploy and maintain
- GPU compute costs for running large models can be significant
Public vs private AI deployment
| Criterion | Public AI | Private AI |
|---|---|---|
| Data location | Provider's servers | Your infrastructure |
| Model quality | Best available (GPT-4o, Claude) | Good and improving (Llama, Mistral) |
| Setup cost | Low ($0-$5K) | Higher ($15K-$50K) |
| Running cost | Per-token (scales with usage) | Fixed infrastructure (predictable) |
| Data privacy | Provider-dependent | Full control |
| Compliance | Enterprise plans offer compliance | Strongest compliance position |
| Maintenance | Provider handles it | You manage it (or your vendor does) |
| Customisation | Limited (prompting, fine-tuning via API) | Full (fine-tuning, custom training) |
Decision framework
Use public AI when
- Data is non-sensitive (public information, general queries)
- You need the best model quality available
- Volume is low to moderate (per-token pricing is affordable)
- Fast implementation is important
- No data residency requirements apply
Use private AI when
- Data includes personal, health, or financial information
- Regulatory compliance requires data to stay in your control
- Volume is high enough that per-token pricing is expensive
- You need full control over model behaviour and updates
- Data residency requirements apply (data must stay in Australia)
Hybrid options
Most businesses benefit from a hybrid approach:
- Public AI for general tasks: Content drafting, research, brainstorming, analysis of non-sensitive data
- Private AI for sensitive tasks: Customer data processing, internal knowledge Q&A, compliance-related workflows
Key takeaways
- Public AI sends your data to third-party servers — even with enterprise plans, data leaves your control
- Private AI keeps data on your infrastructure but costs more and requires more technical capability
- Most Australian businesses can use public AI for non-sensitive tasks with appropriate policies
- Regulated industries (healthcare, finance, government) should default to private AI for sensitive data
- Hybrid approaches — public AI for general tasks, private AI for sensitive data — are the most practical solution for most businesses