What shapes the cost of an AI product?

Begin with the workflow, not the model

A useful AI product starts with a repeatable job. Mapping inputs, decisions, exceptions and outputs reveals whether AI should generate, classify, retrieve, summarise, converse or simply stay out of the way.

The main cost drivers

Complexity grows when the product needs several data sources, real-time interaction, detailed permissions or high-confidence review paths.

• •Number of workflows and user roles
• •Quality and availability of source data
• •Real-time versus background processing
• •Existing systems that need to connect
• •Review, escalation and audit requirements
• •Interface and operational tooling

Prototype the uncertain part first

The highest-risk assumption may be model quality, response speed, data access or user trust. A focused prototype should test that assumption before the surrounding platform expands.

Budget for operation as well as build

AI products have ongoing usage, monitoring and improvement considerations. A clear scope should identify what the team will need to review, update and control after launch.

Studio89 starting points are not universal AI prices

A Product Blueprint starts from £1,500 to clarify the problem and scope. A focused MVP or first AI workflow starts from £5,000. Multi-workflow AI operations systems start from £15,000+. The final price depends on the agreed work; these are not industry averages or a guarantee that every AI idea fits one starting point.

A single workflow may still need careful engineering if its inputs are unreliable or a wrong output has serious consequences. Several workflows may need additional permissions, review screens and exception handling. The scope needs to account for those differences instead of pricing only the model connection.

Define what a useful result looks like

Write examples of acceptable outputs and examples that would need correction. Then decide how a person can inspect, edit, approve or reject a result. The review experience is part of the product, not a detail to add after the model appears to work.

For example, a workflow could draft a summary from supplied information and leave a person to approve it before use. That planning example has different requirements from a system that acts without review. Neither fluent text nor a successful demonstration establishes that outputs will be suitable in every real situation.

• •What source information should each result rely on?
• •What should happen when information is incomplete?
• •Who decides whether the result is ready to use?
• •What action must never happen without approval?

Data and integrations can change the estimate

Confirm where the information lives, whether you have permission to use it and how the product can access it. An available export and a real-time connection are not the same scope. Existing software may impose access limits, usage charges or restrictions that affect the design.

Do not assume that every tool can connect simply because it has an API. The required action, permissions and data format must be available. Studio89 checks feasibility during scoping rather than promising integrations before those constraints are understood.

Plan ongoing usage rather than a fixed model bill

Operating costs vary with the selected services, the amount of information processed and how often the workflow runs. A conversational experience, a scheduled background task and a document-processing tool can have different usage patterns. The project estimate should make its assumptions visible.

Include external subscriptions, hosting and any agreed ongoing review or improvement work in the operating plan. No usage-cost figure is reliable without those assumptions. Ask what you will pay directly, what is included in the engagement and what would trigger a later scope discussion.

When to choose a Blueprint or a focused build

A Blueprint is useful when the problem is clear but the right workflow is not, or when several stakeholders have different priorities. A focused build can be appropriate when the main journey, inputs and review decisions are already understood.

Bring a description of the current process and representative examples you are allowed to share. Avoid sending sensitive business or customer data before agreeing how it will be handled. The first conversation can establish the questions to resolve before committing to a build; it cannot guarantee an AI outcome before testing.