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 with a 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, with the ability to 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 work best for most businesses: public AI for general tasks, private AI for sensitive data