AI You Own vs AI You Rent: What’s Better for Business?
AI is changing how we work—but at what cost? Shadow AI,
unclear data ownership, rising subscription fees, and unsafe practices are putting businesses at risk.
Local AI offers an alternative: full control, lower
long-term costs, and better data sovereignty.
Beware of Shadow AI
Most people are familiar with Shadow IT—when employees install unauthorised tools or bypass company policy. Cloud services like SaaS make this easy: anyone can sign up with an email.
Shadow AI is a new form of Shadow IT, with far greater risk.
Shadow AI is the unauthorised use of AI tools by employees—without the organisation’s knowledge or
approval.
The
biggest risk? Staff entering confidential information into AI tools hosted in the cloud.
Studies show that nearly 40%
of workers already do this. Other reports suggest it could be as high as 60%. These figures should concern any business leader.
You can reduce the risk with staff training and clear guidelines—but it won’t eliminate it. If a single mistake leaks sensitive data, the
fallout could include legal action, reputational harm, and financial loss.
AI and Data Sovereignty
AI adoption is widespread—over 65% of organisations use it openly. The real number is likely higher when you include Shadow AI.
As usage grows, so do concerns about how providers handle sensitive data. Past breaches and misuse have shown how fragile data ownership can be.
Data sovereignty means your organisation owns and protects its data—by keeping it under its control.
For organisations in regulated or sensitive industries, this matters.
Even with policies in place, people still take shortcuts. If AI tools make work easier, many will upload data—regardless of the rules. In fact, even with a Responsible Use of AI Policy, around 50% of workers admit to sharing sensitive or restricted information when they shouldn't.
Large AI providers like ChatGPT have policies (Usage, Privacy) that outline how data is used. But the fine print matters.
- Personal data may be shared under certain conditions.
- It may be aggregated or de-identified—but what rights do you retain after that?
- And worse, de-identified data can often be re-identified. That’s not just a technical issue—it’s a business risk.
If your data matters, handing it to the cloud without guarantees is a risk you can’t ignore.
Avoid AI SaaS Fees
SaaS fees are the new business tax.
AI is just the latest line item—stacked on top of dozens of others.
User-based and consumption-based pricing models are everywhere.
They work well for providers, but they’re becoming a major burden for businesses.
As AI adoption grows, so do the costs.
For many, it's already unsustainable.
Before we explore how Local AI can reduce this pressure, let’s look at what typical AI pricing looks
like—using OpenAI as an example.
The table below outlines estimated user costs by organisation size.
ChatGPT Pricing Table (AUD with Totals)
| Organisation Size | People | Plan Type | Monthly Cost (AUD) | Annual Cost (AUD) | Notes |
| Solo Consultant | 1 |
Plus | ~$30 | $360 | USD $20/month. No team features. |
| Power User | 1 |
Pro |
~$300 | $3,600 | USD $200/month. High usage limits. |
| Small Business | 5 |
Team |
~$38/user × 5 = $190 | $2,280 | USD $25/user/month (annual billing). |
| Growing Team |
20 |
Team |
~$38/user × 20 = $760 | $9,120 | Max size for Team plan. Shared workspace, admin console. |
| Medium Org | 150 |
Enterprise |
~$72–$81/user × 150 = $10,800–$12,150 |
$129,600–$145,800 |
Estimated USD $60/user/month. 10–20% discount. |
| Large Org | 500 | Enterprise |
~$67.5–$76.5/user × 500 = $33,750–$38,250 | $405,000–$459,000 | Estimated 15–25% discount for volume. |
| Enterprise-Level | 1,000 |
Enterprise | ~$67.5–$76.5/user × 1,000 = $67,500–$76,500 | $810,000–$918,000 | Includes premium features, SSO, audit logs, analytics, custom terms. |
Assumptions: Exchange Rate: 1 USD = 1.5
AUD, Team Plan: USD $25/month = ~AUD $38/user/month, Enterprise Plan: USD $60/month = ~AUD $90/user/month before discounts, Discounts:
Medium Org: 10–20%, Large Org: 15–25%, Minimum Enterprise Contract: 150 seats (source below)
Sources: OpenAI official pricing: https://openai.com/chatgpt/pricing, TechCrunch: ChatGPT pricing deep dive, FirmSuggest: Enterprise pricing update, Exploding Topics: ChatGPT Enterprise seat minimums, SciTke: OpenAI pricing summary.
User licences aren’t the only cost.
Most AI providers also charge for API access—used to power internal tools, customer apps, and backend workflows.
These costs grow fast with usage.
The table below shows estimated API costs across different organisation sizes.
| Organisation Size | People | Usage Type | Monthly Cost (AUD) | Annual API Cost (AUD) | Assumptions |
| Solo Consultant | 1 |
Light (Dev/Test) |
$20 | $240 | ~100K tokens/day. GPT-4o @ ~$5/million input & ~$15/million output tokens. |
| Startup | 5 | Light-Moderate | $100–$200 | $1,200–$2,400 | 0.5–1M tokens/day across team. |
| Small Business | 20 | Moderate-Heavy | $400–$800 | $4,800–$9,600 | 2–4M tokens/day total. |
| Medium Org | 50 |
Heavy Internal Use | $1,250–$2,500 | $15,000–$30,000 | 6–12M tokens/day total. |
| Large Org | 250 |
Mixed Use (Apps + Dev) | $6,250–$12,500 | $75,000–$150,000 | 25–50M tokens/day. Internal tools + customer-facing integration. |
| Enterprise-Level | 1,000 |
Production-Scale | $25,000–$50,000 | $300,000–$600,000 | 100–200M tokens/day. Could involve product integration, chat support, and analytics pipelines. |
Source: Pricing | OpenAI
The table below shows the total cost of ownership (TCO) for AI across different organisation sizes.
It combines annual licence fees and API costs to give a full view of what AI adoption really costs at scale.
| Organisation Size | People | Plan Type | Annual Licence Cost (AUD) | Annual API Cost (AUD) | Total Annual Cost (AUD) | Notes |
| Solo Consultant | 1 |
Plus | ~$360 | $240 | $600 | Basic use. No team features. Light dev/test API use (~3M tokens/year). |
| Power User | 1 |
Pro |
~$3,600 | $240–$480 | $3,840–$4,080 | High personal usage. Still low API volume. |
| Startup | 5 | Team | ~$2,280 | $1,200–$2,400 | $3,480–$4,680 | Light–moderate API usage (15–30M tokens/year). |
| Small Business | 20 | Team |
~$9,120 | $4,800–$9,600 | $13,920–$18,720 | Moderate–heavy API usage (60–120M tokens/year). |
| Medium Org | 150 |
Enterprise |
$129,600–$145,800 | $15,000–$30,000 | $144,600–$175,800 | 180–360M tokens/year. Multiple use cases: apps, internal tools, etc. |
| Large Org | 500 | Enterprise |
$405,000–$459,000 | $75,000–$150,000 | $480,000–$609,000 | 750M–1.5B tokens/year. Significant internal + customer-facing AI. |
| Enterprise-Level | 1,000 |
Enterprise | $810,000–$918,000 | $300,000–$600,000 | $1,110,000–$1,518,000 | Full-scale integration, 3–6B tokens/year. SLA, SSO, analytics, APIs. |
No matter your size, you need to manage AI’s total cost of ownership if you want to stay competitive.
One way to do that is by self-hosting your AI models.
At WorkingMouse, we help organisations plan and execute this transition.
While it requires upfront investment in infrastructure, it can dramatically reduce long-term costs by cutting out SaaS fees.
The table below outlines estimated hardware costs for different organisation sizes.
| Organisation Size | People | Usage Type | Suggested Hardware Setup | Estimated Upfront Cost (AUD) | Notes |
| Solo Consultant | 1 |
Light (Dev/Test) | 1× Mac Mini | $1,000–$1,500 | Suitable for lightweight local inference and prototyping. |
| Startup | 5 | Light–Moderate | 1× Mac Studio (M2 Ultra) or entry GPU workstation | $2,000–$2,500 | Ideal for experimentation and small-scale API replacement. |
| Small Business | 20 | Moderate–Heavy | 1× Workstation with RTX 4090 or dual RTX 4080 | $6,000–$10,000 | Supports sustained internal inference workloads. |
| Medium Org | 50 | Heavy Internal Use | 1× Server with 2× RTX 4090 + 128GB RAM | $12,000–$20,000 | Suitable for concurrent use and batch AI processing. |
| Large Org | 250 |
Mixed Use (Apps + Dev) | 2–4 Servers with A100 or H100 (Enterprise GPUs) | $150,000–$300,000 | Supports internal tools, production apps, and scaling needs. |
| Enterprise-Level | 1,000 |
Production-Scale | Cluster with 8+ H100 GPUs + orchestration stack | $500,000–$1,000,000 | High-availability AI infrastructure including networking and cooling. |
If you have an internal IT team, you may choose to manage Local AI in-house.
If not, WorkingMouse can help.
- We offer end-to-end AI enablement services:
- Infrastructure setup and installation
- Model selection, training, and fine-tuning
- Integration with your internal data and document systems
- Ongoing monitoring and support
- Strategic roadmap aligned to your business goals
Let’s talk.
We’ll help you take control of your AI—and bring costs back under control.
We acknowledge the use of AI in the construction of this paper. AI-assisted in checking spelling, grammar, and refining certain sections for
clarity and coherence. However, all content remains original, and this article has been developed in alignment with our
Human Oversight Policy.
