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LLM Inference Cost Calculator

Estimate the daily and monthly cost of running an LLM workload in production — across providers, with prompt caching and multi-model comparison.

Inputs

Input: $5.00/1M tokens  ·  Output: $20.00/1M tokens  ·  Cached: $1.25/1M

Average tokens in each prompt/context sent to the model

Average tokens in each model response

Total API calls your application makes daily

Used to compute cost per user

0%45%90%

Percentage of input tokens served from prompt cache (reduces input cost).

Configure your workload and click Calculate to estimate production costs.

Cost Reduction Tips

  • • Prompt caching is the single biggest lever — even 30% cache hit rate cuts input costs significantly.
  • • Use a smaller model for classification, routing, and simple generation steps.
  • • Trim system prompts aggressively — every 100 tokens saved × daily requests × 30 days adds up fast.
  • • Streaming does not reduce cost, but it improves perceived latency for end users.
  • • For agent workflows, multiply this estimate by your average number of LLM calls per task.

How to use LLM Inference Cost Calculator for AI Architects

1. What this calculator does

Projects daily and monthly inference spend under real traffic assumptions, exposing how model choice, token mix, caching, and retries alter cost at production scale.

2. When to use it

  • Before selecting a default model tier for high-volume workloads.
  • When finance and platform teams need spend forecasts tied to usage scenarios.
  • When optimization work is required to keep margin targets intact.

3. Inputs explained

  • Input/output token volume per request and request frequency.
  • Model price tiers and any provider-specific token pricing splits.
  • Cache hit assumptions and retry rates under operational conditions.
  • Workload distribution across endpoints and user segments.

4. Formula / decision logic

  • Cost = (input tokens x input price) + (output tokens x output price), aggregated over traffic horizon.
  • Scenario bands compare baseline, growth, and stress traffic patterns.
  • Optimization options are evaluated by token reduction and routing impact.
  • Decision threshold flags when expected spend breaches budget guardrails.

5. Example scenario

A customer support copilot operates well in pilot but monthly spend surges after rollout. The calculator reveals long response tails and retry inflation. Limiting output length and introducing model routing reduces projected run-rate significantly.

6. Architecture implications

  • Cost-aware routing should be part of control-plane design from day one.
  • Prompt and retrieval budgeting often outperforms model downgrade-only strategies.
  • Forecasting must include error and retry pathways, not just happy-path requests.
  • FinOps metrics should map directly to model and workflow change controls.

7. Common mistakes

  • Using single-request averages as production forecast proxies.
  • Ignoring output-token variance and long-tail generation behavior.
  • Skipping cache strategy analysis before escalating model costs.
  • Treating inference cost as static after launch.

8. Related calculators

Context Window Calculator Agent Cost Calculator GPU vs API Break-Even Calculator RAG Vector DB Cost Calculator All Calculators

9. FAQ

Why do pilot cost estimates usually miss production cost?

Pilot estimates often exclude retries, caching miss rates, output-length variance, and workflow orchestration overhead. Production costs are driven by complete task paths, not single request averages.

Should we optimize prompt length before changing model tier?

Usually yes. Prompt and retrieval payload optimization often delivers immediate savings with less quality risk than aggressive model downgrading.

How often should we recompute inference cost forecasts?

At least monthly, and immediately after major prompt, model, or routing changes. Token economics drift quickly in active products.

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