Monthly AI Spend Estimator

Per-call costs feel tiny — fractions of a cent — until you multiply by thousands of requests a day across a whole month. This tool turns your daily usage pattern into a monthly and annual bill so you can budget, set spend limits, and decide whether a cheaper model or a caching layer pays for itself.

How it works

You provide four numbers: requests per day, days per month the workload runs, average input tokens, and average output tokens. The model preset supplies the input and output prices per million tokens. The formula is:

cost per request = (avg_input / 1,000,000 × input_price)
                 + (avg_output / 1,000,000 × output_price)
monthly cost     = cost_per_request × requests_per_day × days_per_month
annual cost      = monthly cost × 12

The estimator also breaks down how much of the bill comes from input versus output, so you can see which side is worth optimising.

The output-token trap

The most common budget surprise is output cost dominance. Most LLM providers price completion tokens at 3–5× the input rate. A model that generates verbose 500-token responses on a task that needed 100 words is silently inflating your bill. For example, at a typical pricing ratio:

• Cutting average output from 500 to 200 tokens saves ~60% of output cost.
• Switching from a premium model to a mini/fast tier on routine classification or extraction tasks can cut per-call cost by 10–20×.

Practical cost-reduction checklist

Prompt side (input):

• Share a system prompt once per session, not per request.
• Use prompt caching if your provider supports it — repeated context is billed at a reduced rate.
• Strip whitespace and redundant context from programmatic prompts.

Output side (completion):

• Set max_tokens explicitly. Open-ended generation is the fastest route to a large bill.
• Ask the model to “be concise” or “reply in under 3 sentences” when you do not need a long answer.
• For structured tasks (classification, extraction, JSON), instruct the model to output only the result.

Architecture level:

• Cache responses to identical or near-identical inputs (semantic deduplication).
• Batch non-urgent requests where the provider offers a batch discount (often 50% off).
• Route simple queries to a cheap fast model and only escalate to expensive models when quality matters.

Worked example

A customer support bot making 2,000 requests per day, 25 days per month, with 400 average input tokens and 300 average output tokens:

• Monthly requests: 50,000
• At a mid-tier model: cost per request ≈ a fraction of a cent
• Even small per-call prices scale: at $0.003/request the monthly bill is ~$150; at $0.01/request it is ~$500.

The key is to run this estimator for each model tier you are considering, then decide whether the quality difference justifies the cost gap.

Always set a hard spend limit in your provider console. An estimate is a planning number, not a guardrail — unexpected traffic spikes, prompt injection attacks, or runaway loops can send real spend far above the forecast.