How to Choose an AI Model for Your Business Use Case
A practical framework for choosing an AI model — matching capability, cost, latency, context and data requirements to the specific use case rather than defaulting to the best-known name.
Quick answer
Quick answer
Proprietary AI models are accessed through a provider's API and run on the provider's infrastructure; open-source — more precisely, open-weight — models can be downloaded and run on your own infrastructure. The trade-off is essentially control and data residency versus capability and operational ease. Proprietary models typically lead on frontier capability and require almost no operational burden, but your data goes to the provider and you depend on their terms. Open-weight models keep data and hosting under your control and can be cheaper at high volume, but you must run, secure and maintain them yourself. For most mid-market buyers the right answer is use-case-specific, and often a mix of both.
What this means
The distinction is not really "open versus closed" in the software sense; it is "run by the provider versus run by you." With a proprietary API, you send prompts to the provider and receive responses, paying per use, with the model and its operation entirely managed for you. With an open-weight model, you obtain the model itself and take responsibility for hosting, scaling, securing and updating it.
This is fundamentally an operating-model decision. One path trades data control for convenience; the other trades convenience for control. Capability differences exist but narrow continually, so for many tasks the deciding factors are control, cost at volume and operational capacity.
Why it matters for business
The choice has real consequences for cost structure, data governance and the skills your organisation needs. Proprietary APIs convert AI into a variable, per-use operating cost with no infrastructure to manage — attractive for most mid-market organisations that lack large platform teams. Open-weight self-hosting converts it into an infrastructure investment that can be cheaper at scale and keeps data in-house.
For Australian organisations with data residency or sovereignty requirements, open-weight self-hosting can be the route to keeping sensitive data entirely within their own environment. BCG's research shows leading adopters investing heavily in AI infrastructure; for some, that includes self-hosted models where control is paramount. For most others, the operational burden of self-hosting outweighs the benefit.
How it works technically
The two approaches differ across several dimensions:
| Dimension | Proprietary API | Open-weight self-hosted |
|---|---|---|
| Capability | Typically frontier-leading | Strong and closing the gap |
| Data control | Data goes to provider (under terms) | Stays in your environment |
| Cost model | Per-token operating cost | Infrastructure + operations |
| Operational burden | Minimal — provider manages | Significant — you run it |
| Customisation | Limited to provider's options | Full, including fine-tuning |
| Residency/sovereignty | Depends on provider | Fully under your control |
A pragmatic pattern is to use proprietary APIs for most tasks and reserve self-hosted open-weight models for the specific cases where data sensitivity, sovereignty or volume justify the operational investment.
Practical implementation considerations
Be honest about operational capacity. Running open-weight models well requires infrastructure, security and ongoing maintenance skills that many mid-market organisations do not have and may not want to build. The total cost of self-hosting includes that operational effort, not just compute.
Edison AI's implementation work helps organisations decide per use case, often landing on a hybrid: proprietary APIs for general tasks, self-hosted open-weight models only where control or economics clearly demand it. Keeping the architecture model-agnostic allows both to coexist behind a consistent interface.
The decision should be revisited as both options evolve — open-weight capability and proprietary data-residency offerings both change quickly, and what favoured one path last year may not this year.
Common mistakes
- Choosing open-weight for cost without counting operations. Self-hosting's true cost includes infrastructure and skilled maintenance.
- Assuming proprietary always means better. Leading open models are sufficient for many tasks, especially where control matters.
- Ignoring data residency. For sensitive or sovereign data, where the model runs can be decisive.
- All-or-nothing thinking. A mix of proprietary and open-weight is often optimal; it need not be one or the other.
- Underestimating maintenance. Self-hosted models need ongoing updating and securing, not a one-off setup.
What leaders should do next
Decide per use case, not by ideology. Use proprietary APIs where their capability and low operational burden fit, and reserve open-weight self-hosting for cases where data control, sovereignty or high volume justify the effort — and where you genuinely have the operational capacity. Keep your architecture model-agnostic so both can coexist. Reassess as the options evolve. The aim is to match each workload to the model approach that best balances capability, cost, control and the operational reality of your organisation.
An AI readiness audit maps the highest-return use cases before you commit to a model or platform.
Frequently asked
Questions, answered.
What is the difference between proprietary and open-source AI models?
Proprietary models are accessed via a provider's API and run on their infrastructure. Open-source (open-weight) models can be downloaded and run on your own infrastructure, giving more control over data and hosting but requiring you to operate them yourself.
Are open-source AI models as good as proprietary ones?
The best open-weight models are highly capable and close the gap continually, though the frontier is typically held by proprietary models. For many business tasks, leading open models are more than sufficient, especially where control and data residency matter.
When should a mid-market company use open-source models?
When data residency, sovereignty or control requirements favour self-hosting, when high volume makes per-token API costs unattractive, or when deep customisation is needed. For many others, proprietary APIs offer better capability with far less operational burden.
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Article: Proprietary vs Open-Source AI Models: Trade-offs for Mid-Market Buyers