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There is no single best AI sourcing choice for a whole business. Buy a mature product for a common need; build or adapt when the requirement is distinct and you can support the system over time; consider open-weight models when deployment control matters enough to justify added operational responsibility. Compare each option on cost per successful outcome, not model price alone—and be prepared to use different approaches for different tasks.
Which AI approach fits your use case?
Start with the task and the organization’s ability to own the result, not with a preferred model or vendor. UK government guidance recommends considering whether a need is unique, whether commercial products are mature, how the solution will integrate, and whether the team can build and operate it. That is a useful decision framework for businesses too, though procurement rules differ by jurisdiction. UK guidance on assessing whether AI is the right solution
| Option | Consider it when | What you must evaluate or own |
|---|---|---|
| Buy a finished AI application | The task is common and a mature product covers it adequately. | Vendor terms and privacy; what users may enter; fit with your existing service; output review; who is accountable; and any customization or integration work. |
| Use a commercial model API or managed service | You need model capability inside your own product or workflow and want the provider to manage the model service. | What data is sent and retained; controls for prompts and outputs; provider and model changes; evaluation and monitoring; dependence on the provider; and total cost at expected usage. |
| Adapt a pre-trained or open-weight model | Domain fit, ability to modify the system, or deployment control is important enough to justify adaptation. | Exact model and dataset licenses; performance on task-specific tests; hosting and inference costs; security updates; specialist skills and maintenance; and the responsibilities of each provider and integrator. |
| Build a new model or substantial custom system | Existing products and models do not meet genuinely distinctive requirements, and sustained investment is justified. | Data rights and quality; research and engineering capacity; training and compute; evaluation and governance; and production maintenance. First test whether retrieval or adaptation of an existing model can meet the need. |
| Do not use AI for this task | A conventional workflow, rules, or non-AI software meets the need more safely or economically—or an AI proof of concept does not succeed. | Compare against a non-AI baseline and include error, human-review, and operational-complexity costs. |
Buying a product does not remove the work of integrating it into the complete service. Conversely, having people who can train or customize a model does not by itself establish that the organization can secure, operate, update, test, and monitor it in production.
How can you compare options on a real business task?
Use a small, controlled evaluation before committing to a vendor, a custom build, or private hosting. Define the task narrowly enough that different approaches can be tested against the same cases.
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- Write a success definition. Describe one task, the acceptable quality or error rate, and the circumstances in which a person must review, correct, or override the result.
- Set a baseline. Where practical, record how the task performs today without AI, using a conventional process, rules, or existing software.
- Test representative cases. Give each candidate approach the same examples, including difficult and sensitive cases. Begin with the smallest useful proof of concept rather than a broad rollout.
- Record the full result. Measure task quality, latency, failure modes, user acceptance, integration effort, staff review time, and the frequency of retries or corrections.
- Estimate cost at forecast usage. Include application or API fees, compute, storage, engineering, data preparation, integration, security, monitoring, retries, human review, incident response, and model upgrades.
- Make an ownership decision. Assign accountable people for data, model choice, application code, deployment, testing, monitoring, and incidents; continue evaluating performance after launch.
Compare cost per successful outcome: the total cost of delivering an acceptable result, including the time, retries, oversight, and errors required to get there. OpenAI uses this framing in a company-authored article, so it is a useful vendor perspective rather than an independent benchmark. OpenAI’s cost-of-outcome framing
Do not assume there is a general usage level at which building becomes cheaper than buying. A break-even point depends on the workload, quality requirements, usage, integration, and ongoing operating costs; estimate it from your own evaluation rather than applying a universal threshold.
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When does open-source AI make sense?
“Open-source AI” can refer to different degrees of access and permission. Downloadable model weights do not automatically mean that every part of a system is open source, or that its license permits every intended use. Check the terms for the exact model version and any relevant datasets before adapting or deploying it.
Open-weight models may be a fit when a business needs more control over deployment or modification and has the capacity to take on the associated work. Private hosting can keep data within an environment the organization owns, but then the organization is responsible for securing and updating the model, maintaining infrastructure, and providing specialist machine-learning operations. The UK Government AI Playbook notes that models runnable locally may not match the scale of public services and are not recommended for most production services. UK Government AI Playbook
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Open versus closed does not determine security on its own. Assess the specific model and release components, your threat model, the deployment, the data, and the maintenance practices. Downloadable weights are not inherently private if you use them through a hosted endpoint.
How do data handling and security affect the choice?
Map the actual data flow for the specific product or deployment rather than relying on labels such as “private,” “open,” or “enterprise.” Public applications, APIs, managed hosting, private hosting, local execution, and model training have different arrangements. An API may offer additional controls, but using one still sends data to the provider.
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- Classify the data involved and check applicable regional or residency requirements.
- Review provider access, retention, deletion, logging, and whether submitted data may be used for training.
- Check access controls and contractual commitments for the precise service, plan, and deployment region you intend to use.
- Decide how prompts and outputs will be controlled, reviewed, and monitored, including how incidents will be handled.
Provider terms and service capabilities change, so verify current official terms for the product and plan under consideration. Security and deployment guidance from OpenAI recommends publishing usage rules, enforcing them, evaluating model behavior, documenting known weaknesses, and gathering stakeholder input; it is provider-authored guidance, not a substitute for your own risk assessment. OpenAI’s language-model deployment guidance
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can a business use more than one approach?
Yes. The sourcing decision can be made per workflow: a commercial product may serve a routine need while a more controlled deployment is reserved for a critical, high-risk, or strategically important application. A 2026 paper on government LLM strategy describes this kind of pluralistic approach using dimensions including sovereignty, safety, cost, resource capability, cultural fit, and sustainability. It focuses on the public sector, so businesses should adapt its dimensions rather than treat them as a ready-made private-sector rule. 2026 paper on government LLM strategy
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With adapted or hosted models, responsibility may be distributed among cloud or compute providers, data providers, model providers, model hubs, adapters, application integrators, distribution platforms, and evaluation or MLOps providers. Identify who controls each part of the system and who responds when it fails; a model provider’s involvement does not automatically make that provider accountable for the complete business workflow. Partnership on AI’s map of the AI ecosystem
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