Calculate API costs, compute usage, technical capacity, and engineering project requirements.
Technology and engineering calculators translate units, usage volumes, service pricing, and system constraints into practical planning numbers. They document the exact rate basis and exclusions so a quick estimate can still be audited against a provider invoice or technical specification.
Estimating language-model spend across providers: use the LLM API Cost Calculator for uncached input, cached input, cache writes, output, request volume, and custom rates. Provider-specific and broader engineering calculators will be added here as the rollout advances.
Technical costs are most useful when inputs come from production traces or a representative test set. Average input and output tokens, cache-hit rate, request volume, and active days can vary by orders of magnitude between prototypes and production. Start with measured values whenever possible, then test low, expected, and high scenarios.
API prices, model names, context thresholds, and processing tiers change frequently. These calculators label embedded values with a verification date, link to first-party documentation, and allow custom inputs. Treat a preset as a documented starting point, not a substitute for the current provider quote or invoice.
Technical estimates fail surprisingly often because the units are implicit. Token prices are usually quoted per one million tokens, storage may be per token-hour, throughput may be requests per minute, and infrastructure may be billed per second or per month. A useful estimate states the unit beside every input, converts once in a pure formula, and preserves enough precision for small per-request costs without displaying meaningless precision on the monthly total.
Prompt caching is not a single discount. Providers may bill the initial cache write, later cache reads, refreshes, and storage differently, with time-to-live rules that determine whether content is actually reusable. Measure cache-hit behavior from the workload rather than assuming every repeated prefix is discounted. Separate cached input from cache writes and fixed storage cost so the estimate still works when one provider's caching policy differs from another's.
Production traffic, retries, output length, reasoning tokens, and tool calls rarely match a prototype exactly. Build at least three cases: a low-volume baseline, an expected workload from measured averages, and a high case using realistic peak traffic and longer outputs. Compare monthly spend and cost per successful task. The high case is useful for budget guardrails; the expected case is useful for model comparison; the low case reveals whether fixed costs dominate early usage.