Your Launch Budget Starts With Realistic Conversations

A useful forecast begins with user activity

Before launching an AI feature, you need a spending estimate that connects product usage to the bill. A model’s price alone cannot provide that estimate. Monthly cost depends on the number of people using the feature, their activity, the information each request carries, and the amount of text the model generates.

Start with three assumptions: monthly active users, sessions per user, and requests per session. For example, 1,000 users having four sessions each, with three model requests per session, produce 12,000 requests. Treat this as a planning scenario, not a prediction. Build quieter and busier scenarios alongside it.

Each request carries more than the latest message

Tokens are pieces of text processed by a model. They are not exactly words, and the relationship varies across languages and content types. Input tokens cover material sent to the model; output tokens cover its generated response. These categories often have different prices.

Your input estimate should include instructions, conversation history, retrieved documents, and tool results—not just the user’s latest sentence. In a conversation, earlier messages may be sent again with each new request. A short exchange can therefore become progressively more expensive as its history grows.

Some features also make several model calls behind one visible action. An assistant might search, inspect results, and then answer. Count those calls separately. Check the provider’s billing rules for reasoning tokens and non-text inputs rather than assuming that visible text explains every charge.

A small sample makes the estimate concrete

Run a representative set of tasks before launch and record the usage reported for each request. Include brief questions, ordinary conversations, and demanding tasks. Group them by workflow, then estimate their expected shares of traffic. A simple average from easy demonstrations can hide the cost of your busiest workflows.

Suppose your sample suggests an average of 2,000 input tokens and 500 output tokens per request. Across 12,000 requests, that becomes 24 million input tokens and six million output tokens. Using purely illustrative rates of $2 per million input tokens and $8 per million output tokens, the monthly token bill would be $96.

Those rates are not a quote for any model or service. Replace them with the current prices for your selected model, including any applicable pricing tiers. Keep input and output calculations separate so that longer answers do not disappear inside a misleading blended rate.

Cache savings need their own billing assumptions

Prompt caching can reduce the cost of repeatedly processing eligible input, but repeated text does not automatically guarantee a cache hit. Eligibility, minimum sizes, expiration periods, and pricing depend on the provider. Cache writes may also carry a different charge from ordinary input.

Split your forecast into uncached input, cache reads, cache writes, and output. Apply the relevant rate to each category without counting the same tokens twice. Until testing demonstrates a dependable cache-hit rate, use a conservative assumption. Budgeting as though every reusable instruction will always be cached can leave a substantial gap.

Launch uncertainty belongs inside the spending range

Create low, expected, and high estimates by varying traffic, conversation length, and output length. Add an explicit allowance for retries and failed requests that still incur charges. Keep subscriptions, infrastructure, taxes, and other non-token expenses separate so the token forecast remains understandable.

This article is published by BestRelay, a quality-first API relay that forwards each request to the official model named in it, without substitution. It covers Claude, GPT, Gemini, DeepSeek, Kimi, GLM, Qwen, Grok, and more, and works with Claude Code, Codex, Gemini CLI, and any OpenAI SDK.

BestRelay bills pay-as-you-go using provider-reported token counts, with cache reads and writes priced separately. Create a BestRelay account to test your launch assumptions. Members can also earn referral commission when invited users top up; keep that income separate from your usage forecast.

After launch, compare actual usage with your assumptions regularly. Set usage alerts and application-level limits before traffic arrives. The strongest budget is not an exact guess: it is a transparent estimate you can revise as real behavior replaces assumptions.