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A cheap AI API is one that completes your task at an acceptable total cost. A low input-token rate can hide expensive outputs, failed attempts, platform fees, or additional review. Compare the same workload and quality threshold before choosing a model or gateway. Start with a small usage sample: model ID, task type, input/output volume, cache categories, retries, and completed charges. Remove credentials and private content from any exported records. Separate production traffic from experiments so a temporary test does not distort your estimate.

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Normalize the price before comparing

Use the current Rock API catalog for model details. Follow pricing and billing to distinguish a model’s base price, the applicable consumption group multiplier, and the amount paid to receive prepaid balance. There is no universal discount across all models or requests.

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A hypothetical token calculation

The following numbers are assumptions for arithmetic only, not a Rock API quote or a provider comparison. Suppose ordinary input costs USD 2 per million tokens, output costs USD 6 per million, and the applicable group multiplier is 1.2. With 1 million input tokens and 200,000 output tokens, no caching, and no other billed items:
Apply the multiplier once. This flat token example does not apply to per-call or dynamic billing, and excludes quota conversion and rounding. Use normalized billable categories to avoid counting cached tokens twice. Reconcile real requests against their completed usage records and saved rates; current prices may differ from historical settlement.

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Evaluate alternatives on the same basis

For direct access, check official OpenAI pricing, Anthropic pricing, or Gemini API pricing for the exact model and conditions you need. For gateways, examine the selected route and service terms as well as token rates. OpenRouter documents prompt caching for supported providers and models. LiteLLM documents a self-hosted proxy; include its infrastructure and maintenance in your budget. For Portkey, verify the deployment and plan you intend to use. None of these facts establishes which service will be cheapest for your traffic.

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Reduce usage while protecting quality

  1. Establish an acceptance set with representative tasks, languages, output schemas, and difficult cases.
  2. Test a smaller model on routine work and retain an escalation route for tasks it cannot complete reliably.
  3. Trim repeated context and request the output length your product needs. Recheck correctness after prompt changes.
  4. Where supported, test stable prompt prefixes and measure cache reads separately from writes. Do not assume every request will hit cache.
  5. Bound retries and log their cause. Measure costs across the whole task rather than only the final successful call.
  6. Compare settled cost per accepted result, latency, and manual review before expanding traffic.

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Does prepaid balance guarantee a spending cap?

Prepaid balance and API-key quota checks help control access, but a temporary reservation can differ from settlement. Concurrent requests and asynchronous tasks also require usage review. Configure your own budget alerts and request limits; use the billing guide to understand reservation, settlement, and reversal rather than treating a balance screenshot as the final bill. Prepare a small evaluation with the quickstart, then revisit your usage sample after changing one cost driver at a time.