How to Budget LLM API Spend

Most AI cost surprises come from the same root cause: the input numbers were guesses. Here is the workflow that produces realistic forecasts within ~10% of actual spend.

Three rules

Get a representative prompt + response sample. Use freetokencounter.app on a real prompt — not a guess — to count actual tokens. The numbers are usually higher than people expect, especially when system prompts, few-shot examples, or RAG context get added.
Multiply by realistic monthly volume, not best-case. A 10× safety factor is reasonable when you're new to the space. A 2× factor is reasonable when you have real production data. Don't use the optimistic number from your pitch deck.
Compare across models, not just providers. The same workload can vary 60× between GPT-5 mini and Claude Opus 4.7. Picking the right model for the task matters more than picking the right provider.

The workflow

Open freetokencounter.app. Paste a representative prompt (with system prompt, few-shot examples, and any RAG context if applicable). Note input token count.
Open freeprompttester.app. Run the same prompt against the 2-3 models you're considering. Note output token counts and quality.
Open freeaicostcalculator.app. Enter requests/month × 10× safety, your real input tokens, your real output tokens. Pick the candidate models. Read the bar chart.
Toggle the prompt-cache option to see how the picture changes if you implement caching (50% input discount is the conservative estimate).
Compare against flat plans in the break-even card. If a flat plan is cheaper for your usage and you don't need programmatic access, use the plan.

What to budget extra for

Retries and failures — assume 5-10% extra spend on retried calls.
Output length variance — average and 95th-percentile output lengths can differ by 3×. Budget for the higher number.
System prompt growth — production system prompts tend to grow over time as you add edge-case instructions. Add 30% headroom.
RAG context — if you're doing retrieval augmented generation, the context size dominates the bill. Cache aggressively.
Streaming and partial responses — abandoned streams still consume input tokens (output stops at the abort point). For chatbot UIs with high abandonment, model this.

When to revisit the budget

Monthly. Pricing changes (typically downward), models get released, and your usage patterns shift. The calculator is fast enough to re-run in 60 seconds — make it part of a monthly cost review.

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Frequently Asked Questions

How wrong is my initial cost estimate likely to be?

Without real token counts, expect 2-5× under-estimation. With real counts and reasonable output assumptions, expect ~10-20% error.

Should I budget for the 95th-percentile case or average?

95th-percentile if cost overruns are damaging. Average if cost overruns are tolerable. For most products, somewhere between (e.g., average × 1.5) is the right working number.

How much can prompt caching save?

Up to 90% on the cached portion of input. In practice, 30-60% reduction on total monthly bill for input-heavy workloads (long system prompts, RAG). The calculator's 50% toggle is a working middle estimate.

Should I use the cheapest model that works?

Usually yes, but watch quality. A 60× cheaper model that produces wrong answers 20% more often may not save money — you spend it on retries, support tickets, or worse, broken user experience. Run side-by-side comparisons in freeprompttester.app to validate.

What about reasoning models — they cost more per call?

Yes, but they often need fewer back-and-forth turns. For multi-step problems, a single GPT-5 or o4-mini call may replace 3-5 cheaper calls. Model end-to-end task cost, not per-call cost.

Do I need to budget for OpenAI's new pricing tiers?

Tiered pricing (cached vs uncached, different context windows) does change the math. The calculator's 50% cache toggle approximates this. For precision, run the calculator twice with different multipliers.