AI Decision Guides 8 min read

Public AI vs Private AI for Australian Businesses

Security, compliance, and control differences between public AI services and private AI deployments. Includes a decision framework.

Best for: IT leaders, compliance officers Practical guide for business decision-makers

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

Send data to public AI or keep it in house

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

Public AI is fine when your data is not sensitive

  • 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

Keep AI private when the data is personal or regulated

  • 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
AI StrategyData PrivacyRAGCompliance

Meet the person

Written by the person who does the work

This guide comes from real projects. If it raises a question about your own system, you can ask the founder directly.

HELLO PEOPLE designs, builds and looks after AI, software, app and data solutions for Australian businesses, with senior expertise on every project and a scope agreed before work starts. For AI, that means working on your own data, with a person able to check every answer.

Since 2007, HELLO PEOPLE has delivered more than 100 projects from Perth for small and medium businesses across Australia: custom software and apps, system integrations, data migrations, reporting and dashboards, and AI that works inside the systems a business already runs.

I lead every engagement myself. I trained in accounting before moving into IT, hold accounting and IT professional qualifications and an MBA, and bring more than 20 years of experience across sales, service delivery, inventory and compliance. I am also a PhD candidate in AI at Curtin University, researching retrieval-augmented generation (RAG), so the technology is always judged by what it does for the business.

  • An old-fashioned service

    Small and boutique. The person who scopes your AI project is the person who builds it, and the same person checks its answers before your team relies on them.

  • Quick responses

    No ticket queue and no account manager in between. You hear back within one business day, usually sooner.

  • A long-term partner

    The first AI project is the start, not the end. When you need the next system, integration or report, you call the same person, who already knows your business.

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Kasun Wijayamanna, Founder Kasun Wijayamanna
Founder, replies within one business day

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  • No offshore development. Everything is written by our own team in Australia. Nothing is subcontracted overseas.
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