A marketing team does not only buy access to a model. It buys a workflow that depends on that model remaining available, affordable and suitable for the work. That distinction matters when drafting, reporting or creative production becomes part of a daily publishing operation.
AI vendor evaluation should therefore examine the cost of switching and the consequences of failure alongside output quality. Our perspective is that the best time to design an exit is before a tool becomes indispensable. An exit plan is not a prediction that the vendor will fail; it is a way to keep ordinary business changes from becoming emergencies.
Our article on AI, jobs and customer demand considers the wider business question. Here, the focus is the smaller decision a marketing team can control: choosing and operating a supplier.
This is an operational buying guide, not a comparison of current vendor prices or a legal assessment of any provider’s terms. Use it to frame the questions your team and appropriate advisers need to resolve.
Buy for an approved outcome
Describe the work in terms that remain meaningful if the model name changes: prepare a source-supported draft for an editor, classify enquiries for human review, or summarise a report without altering its figures. Record the required format, quality threshold and who can approve the result.
The AI writing-tool evaluation guide explains how to test representative assignments. Keep that test separate from the sales demonstration. A demonstration chosen by the vendor can be useful, but it does not establish performance on your material, language mix or exception cases.
Decide what happens when the system cannot complete the task. A visible failure with a saved brief can be preferable to an apparently finished output containing unsupported claims. For publishing, the fallback may simply be a manual editorial draft rather than a second model generating unchecked copy.
Understand the three layers of dependence
The first layer is data: source documents, assets, prompts and outputs. Can you export them in useful formats with appropriate permissions? A downloadable transcript is not the same as an export that preserves source references, status and structured fields.
The second layer is workflow: approvals, queues, integrations and business rules. If these exist only inside a vendor interface, moving to another model can mean rebuilding the whole process. Keep a written map of which parts belong to your organisation and which parts the supplier controls.
The third layer is evaluation: the examples and criteria that tell you whether the system still works. Without these, a replacement may be technically connected but editorially worse. Preserve failed cases as well as successful ones so a new system is tested against real difficulties.
These layers are a proposed procurement framework. They are not a claim that every supplier permits the same exports or that every workflow can be moved unchanged.
Price the operation, not just the token
A unit price describes one part of a bill. Total operating cost can also include input length, output length, repeated attempts, retrieval, storage, hosting, human review and integration maintenance. Ask what is metered and which limits stop or slow work.
Google Cloud’s guidance on generative AI cost and performance discusses trade-offs between infrastructure options, latency and cost. It is a vendor’s guidance, not an independent ranking. The practical implication for a marketing buyer is to compare the whole task under realistic demand rather than choose on a single headline rate.
For a fictional monthly plan, suppose 600 assignments require an average of 1.5 attempts. That is 900 attempts before exceptional retries, not 600. If a longer source pack doubles the input per attempt, a cost estimate based only on output length misses an important change. Use your own measured usage and the provider’s current billing rules for the actual forecast.
