Can You Estimate AI Costs Before Going to Production?

The discussion quickly moved away from model pricing. Experienced participants stressed that token price is only one part of the equation. The real challenge is understanding how requests move through an AI architecture.

Recurring questions included: should every request reach the model, which steps can be deterministic, when is a small model sufficient, which prompts can be cached, how often does retrieval happen, which requests require reasoning, and what percentage of users actually invoke the AI.

Several participants noted that estimating "cost per prompt" is almost meaningless. Instead, they recommend estimating cost per business scenario. That shifts the question from a pricing exercise to an architecture exercise.

Microsoft's documentation focuses less on token prices and more on architecture. For Microsoft Foundry this includes prompt caching, model routing, evaluations, tracing and observability, the choice between serverless and provisioned throughput, and the trade-off between small models and reasoning models.

These concepts have direct cost impact. Prompt caching reduces latency and cost for prompts with an identical beginning: cache reads are billed at a discount on the input token price for standard deployments, and up to a full hundred percent discount on input tokens for provisioned deployments. The deployment type determines the billing model: a standard deployment bills pay-per-token (ideal for variable or unpredictable traffic), while provisioned throughput is billed per PTU per hour and only pays off at predictable, high volume, especially combined with a reservation.

For Microsoft 365 Copilot the model looks different. Organizations generally purchase Copilot through licensing (the Microsoft 365 Copilot add-on per user) rather than paying directly per token. But custom extensibility, Graph connectors and agents can still introduce costs depending on the architecture. Agents grounded only in instructions or public data incur no extra cost. Agents that touch tenant data (SharePoint, Copilot connectors) generate usage-based billing in Copilot Credits through Copilot Studio, and a custom engine agent you host yourself adds hosting and model costs in Azure AI Foundry on top of that.

One of the biggest mistakes is estimating AI costs from a single prompt. Real enterprise AI follows many different execution paths.

A typical Foundry agent performs retrieval, calls multiple tools, uses several models, executes deterministic logic, caches reusable context and returns citations. Not every request follows the same route. That turns cost estimation into an architecture exercise rather than a pricing exercise.

A second recurring recommendation was to split workflows into independent components, such as retrieval, classification, summarization, reasoning and tool calls. Each step can use a different model. Using a GPT-level reasoning model for every task often increases cost dramatically without improving business value. Classification and summarization can run comfortably on a small model, while you reserve reasoning for the steps that genuinely need it.

"This is not a pricing discussion. It is an architecture fit discussion."

The practical consequence: model every agent and every business scenario separately, add up the steps in the execution path, and estimate how often each path is taken and by how many users. Only then is a monthly figure grounded rather than a guess.