Category Guide
Six proven strategies to cut LLM spending without sacrificing output quality — from model selection to per-customer cost tracking.
The model you choose has the single largest impact on cost. Pricing varies by 40x or more between models with comparable benchmark scores. Most teams default to the frontier model they used during prototyping and never revisit the decision. That default is almost always more expensive than necessary.
Use intelligence-per-dollar, not sticker price. A model that costs $0.15 per million input tokens and scores 74 on MMLU-Pro delivers far more value per dollar than one that costs $2.50 and scores 78. The 4-point difference on the benchmark is often imperceptible in production, but the 16x price difference compounds with every request. Public benchmarks like MMLU-Pro, GPQA, and AIME provide standardized quality comparisons across providers.
Match model capability to task complexity. Classification, extraction, and simple Q&A do not require a frontier model. Chat, summarization, and code generation might. The optimization is not about using the cheapest model everywhere — it is about not using the most expensive model where a cheaper one performs equally well.
A cost simulator makes this practical. Instead of running A/B tests for every model-task combination, you reprice your actual token usage against every available model, filter out any that would drop more than 10% on benchmarks or cannot handle your context window, and see the projected savings immediately. MarginDash's cost simulator does exactly this across 100+ models from OpenAI, Anthropic, Google, AWS Bedrock, Azure, and Groq.
Every token in your prompt is billed. Shorter, more precise prompts cost less and often produce better results. The most common waste is in system prompts — lengthy instructions that get sent with every request but could be condensed without losing meaning.
Audit your system prompts. Many system prompts contain redundant instructions, examples that could be removed, or formatting directives the model already follows by default. A system prompt that shrinks from 800 tokens to 300 tokens saves 500 input tokens on every single request. At scale, that adds up fast.
Use few-shot examples sparingly. Including five examples when two would suffice triples the prompt overhead. If your task is simple enough for a well-written zero-shot prompt, skip the examples entirely. Reserve few-shot prompting for tasks where the model genuinely needs the guidance.
Prompt optimization pairs well with model selection. A well-optimized prompt on a cheaper model often outperforms a bloated prompt on an expensive one — at a fraction of the cost. Track token counts per feature using AI cost management tools to identify which prompts are the most expensive before you start optimizing.
If you are sending the same or similar prompts to the API repeatedly, you are paying for the same work multiple times. Caching eliminates redundant calls entirely.
Exact-match caching is the simplest form. Hash the prompt and cache the response. If an identical prompt arrives, return the cached response without making an API call. This works well for classification tasks, FAQ responses, and any feature where the same input produces the same output. Even a modest cache hit rate of 20% reduces costs by 20%.
Semantic caching extends this to similar but not identical prompts. Using embeddings to measure similarity, you can serve cached responses for prompts that are close enough to a previous query. This is more complex to implement but captures a larger share of redundant requests, especially in customer support and search use cases where users phrase the same question differently.
Provider-level prompt caching is a newer option. Anthropic and OpenAI both offer forms of prompt caching where repeated prefixes (like system prompts) are cached on the provider side at a reduced rate. If your system prompt is 1,000 tokens and you send it with every request, provider-level caching can cut the cost of those tokens significantly without any changes to your application logic.
Not every request requires the same model. Request routing — sometimes called model routing or LLM gateway routing — sends simple requests to cheap models and complex requests to expensive models. The result is lower average cost per request without a blanket quality reduction.
Route by task type. Classification, sentiment analysis, and entity extraction rarely need a frontier model. Chat with complex reasoning, code generation, and multi-step analysis often do. If you tag your API calls by feature or task type, you can route each category to the most cost-effective model for that complexity level.
Route by customer tier. Free-tier customers might get responses from a balanced-tier model. Paying customers get the frontier model. This aligns cost with revenue and prevents free users from consuming disproportionate API spend. Per-customer cost tracking makes this visible — without it, you are guessing which customers are expensive.
Implementing request routing requires knowing which models perform well enough for each task. This is where benchmark data and a cost simulator become essential. You need to verify that the cheaper model actually handles the task before routing production traffic to it. LLM monitoring helps you track quality after the switch to confirm the routing rules are working.
Output tokens are the most expensive part of most LLM calls. Controlling how much the model generates is a direct lever on cost.
Set max_tokens explicitly. If your feature only needs a one-sentence answer, do not let the model generate a five-paragraph response. Setting max_tokens caps the output length and prevents the model from being unnecessarily verbose. This is especially important for features like classification, extraction, or yes/no decisions where a short response is the correct response.
Use structured outputs. JSON mode, function calling, and structured output schemas force the model to return data in a predictable format. This eliminates the filler text, hedging language, and unnecessary explanations that inflate output token counts. A JSON response with three fields is far cheaper than a natural language response that contains the same three pieces of information wrapped in two paragraphs.
Combine output control with prompt optimization for compounding savings. A shorter prompt that requests a structured output produces less input cost and less output cost on every call. Across thousands of daily requests, this combination alone can reduce costs by 30-40%.
The strategies above reduce cost per request. Per-customer cost tracking tells you where to apply them. Without it, you are optimizing blind — reducing costs on average without knowing which customers, features, or workflows are actually driving the spend.
Identify who is expensive. In most SaaS products that resell AI features, a small percentage of customers account for the majority of API costs. One customer running long-context requests through a frontier model can cost more than dozens of customers combined. Per-customer tracking surfaces these outliers so you can address them specifically — through usage limits, pricing adjustments, or model optimization for their workflow.
Connect cost to revenue. Knowing that a customer costs $45/month in API calls is only useful if you also know they pay $49/month. That is a 8% margin. Another customer might cost $3/month and pay the same $49 — a 94% margin. Flat pricing hides these differences. Connecting cost data to revenue data via Stripe or direct revenue tracking turns cost tracking into margin tracking, which is what pricing decisions actually depend on.
Set budget alerts before costs surprise you. A threshold per customer, per feature, or across the entire organization that triggers an email before spending exceeds the limit. Budget alerts turn cost tracking from a retrospective report into an early warning system. You find out about cost spikes when they are small, not when the monthly invoice arrives.
Each strategy above is more effective when you have data. The cost simulator approach combines all of them into a single workflow: capture your actual usage, reprice it against every available model, filter by quality benchmarks, and show you exactly where the savings are.
MarginDash's cost simulator takes your real token usage — actual input and output counts from actual requests — and reprices every event against 100+ models. It ranks alternatives by intelligence-per-dollar using MMLU-Pro, GPQA, and AIME scores, and filters out any model that drops more than 10% on benchmarks or cannot handle your context window size. The result is a list of model swaps with projected dollar savings and the quality tradeoff for each.
This matters because the cost-quality landscape changes constantly. New models are released every month, pricing changes without notice, and what was the best value last quarter might not be today. A pricing database that covers 100+ models across OpenAI, Anthropic, Google, AWS Bedrock, Azure, and Groq — and updates daily — keeps the simulator's recommendations current without you maintaining a spreadsheet.
The workflow is: install the SDK, let it collect usage data for a few days, open the cost simulator, and see your savings opportunities. No A/B tests, no manual pricing research, no spreadsheet maintenance. The simulator does the comparison; you make the decision.
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