Predict per-request and monthly spend before deployment accurately. Tune token assumptions, margins, and retry rates. Download clear CSV and PDF summaries for stakeholders today.
| Scenario | Input tokens | Output tokens | Input price / 1K | Output price / 1K | Requests/day | Retry % | Buffer % |
|---|---|---|---|---|---|---|---|
| Support chatbot | 900 | 250 | $0.40 | $1.20 | 1,000 | 3 | 10 |
| Content drafting | 1,800 | 900 | $0.60 | $2.00 | 250 | 6 | 15 |
| Agent workflow | 2,600 | 700 | $0.75 | $2.50 | 400 | 10 | 20 |
1) Input tokens: input + system + tool.
2) Cache split: cached_tokens = input_tokens × cache_hit_rate. live_tokens = input_tokens − cached_tokens.
3) Base cost per request:
4) Expected cost per request: base_cost × (1 + retry%) × (1 + overhead%).
5) Budgeted cost per request: expected_cost × (1 + buffer%). Daily and monthly costs multiply by request volume.
Token pricing is typically quoted per 1,000 tokens, with different rates for input and output. Because every request contains user text plus system instructions and tool-routing context, the “hidden” input share can be material. For example, adding a 250‑token safety prefix to a 900‑token user prompt increases billable input by 28%. This is why the calculator asks for user, system, and tool tokens separately.
The estimator converts tokens to currency using: cost = (tokens ÷ 1000) × price. It sums input, output, and optional image components to create a base cost per request. When caching is enabled, it splits input tokens by the cache hit rate, applying an optional discounted cached-input price. This helps teams model prompt‑caching programs and quantify savings.
Production traffic includes retries from timeouts, rate limits, and downstream tool errors. A 6% retry rate implies 1.06 expected calls per successful user action, so costs rise even if average tokens stay constant. Operational overhead captures extra prompts for moderation, logging, routing, or evaluation. If overhead is 8%, the calculator multiplies base cost by 1.08 before applying buffers.
Per‑request expected cost becomes daily and monthly totals by multiplying by requests per day and active days. This is useful for planning a pilot versus a full launch. If volume doubles, spend doubles; if outputs grow by 30%, spend rises roughly in proportion to output pricing. The “budgeted” number adds a buffer after reliability and overhead to cover variance and peak events.
Start by reducing unnecessary output tokens using stricter formats, word limits, and stop sequences. Normalize system prompts and tool schemas to improve cache hit rates and reduce repeated context. Use validation and fallbacks to cut retries, and monitor cost per million tokens to compare scenarios across models. Update assumptions monthly, using real telemetry, to keep forecasts credible and budgets stable. Segment usage by feature, region, and customer tier to assign internal chargebacks, and run best‑case and worst‑case scenarios so leadership understands sensitivity to tokens, price changes, and adoption over time, too.
Use averages for baseline forecasting, then add a buffer or run a second scenario using p90 outputs. Percentiles matter most when output length varies widely across users or tasks.
They represent orchestration text, policies, and tool traces that are billed as input. Excluding them can understate spend, especially for agents with multi-step tool calls.
Retries increase expected calls per user action. A 10% retry rate approximates a 1.10× multiplier on base costs, even if token counts stay the same.
Use caching when large, repeated prompt prefixes stay stable. Enter a hit rate and optional cached input price to quantify savings relative to fully non-cached input tokens.
Overhead includes extra processing like routing, moderation, evaluation prompts, logging, and safety checks. It’s modeled as a percentage multiplier applied before the buffer.
Reduce output length with structured formats, tighten instructions, and use stop conditions. Improve reliability to cut retries, and standardize prompts to raise cache hit rates.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.