FAQ guide
How can I reduce AI token costs?
Quick answer
Reduce token costs by shortening prompts and completions, reusing stable prefixes eligible for caching, routing easy queries to smaller models, trimming chat history intelligently, and preventing accidental retries. Quantify each change with before-and-after token histograms rather than intuition. Avoid reckless cuts that harm safety, compliance, or user trust. Treat cost work as ongoing operations, not a one-time rewrite.
Introduction
The highest leverage wins usually come from product behavior, not micro-edits. Restricting when expensive models run, summarizing redundant context, and constraining output schemas routinely outperform squeezing a few adjectives from a marketing line.
Organizations that succeed pair engineering changes with governance: budgets per surface, alerts, and executive-level transparency. Otherwise optimizations erode as teams ship new features without token review.
Prompt-side savings
Remove duplicate instructions, compress verbose few-shot examples, and cite snippets instead of dumping entire documents when retrieval quality allows. Convert prose instructions into bullet constraints when readability remains acceptable.
Structure shared knowledge so caching can hit warm prefixes across many users rather than unique blobs per session.
Completion-side savings
Ask for JSON or key-value answers when downstream systems parse programmatically. Cap max tokens and tune temperatures to discourage rambling when appropriate.
Use eval harnesses to ensure shorter outputs still meet accuracy targets for regulated domains.
Illustrative impact
Cutting average completion length twenty percent while holding model tier constant reduces that portion of spend about twenty percent, ignoring compounding changes elsewhere.
Approximate savings ≈ baseline_tokens × (1 − factor_after_change) × blended price per token.
Optimization mistakes
- Deleting safety or disclosure text to save tokens without legal review.
- Assuming the largest model is cheapest after aggressive prompt cuts when routing would win.
- Rolling out compression without regression tests on multilingual content.
- Chasing tiny savings on rare admin pages while ignoring fat-tail customer flows that drive most spend.
- Disabling logging entirely to save money and then losing the data needed to diagnose regressions quickly.
Actionable tips
- Create a token budget field for every feature in the roadmap.
- Automate weekly reports ranking endpoints by cost per successful outcome.
- Implement progressive disclosure for tools so schemas appear only when needed.
- Train support staff to spot runaway customer threads early.
- Hold design reviews that include token estimates alongside UX mocks.
- Celebrate wins publicly so teams copy effective patterns.
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Core tools
Turn these ideas into concrete dollars
Compare models, simulate monthly traffic, and export shareable estimates in seconds. Numbers follow your config/models.php rates so you can mirror vendor tables before you commit to architecture.