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Operations··8 min read

AI Cost Optimization in Production: Where the Money Actually Goes

How to cut LLM costs 40-70% without quality loss: caching, smaller models for sub-tasks, structured outputs, and the metrics to track.

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AI cost optimization for production is mostly about discipline, not exotic infrastructure. Seven moves get us 40-70% savings on most builds without quality loss.

The seven moves

  1. 01Prompt caching. Anthropic and OpenAI both support it; we use it on every system prompt that doesn't change per request.
  2. 02Smaller models for sub-tasks. Use Haiku / GPT-4o-mini for classification, summarization, structured extraction; reserve Sonnet / GPT-4o for the reasoning step.
  3. 03Structured outputs over free text. Schemas force shorter completions and cleaner downstream consumption.
  4. 04Retrieval before generation. If the answer is in 200 documents, retrieve the relevant 5 and stop sending the entire corpus.
  5. 05Cost ceiling per request and per cohort. Alerts when a cohort drifts.
  6. 06Streaming with early-stop. Don't pay for the rest of the completion if the user navigated away.
  7. 07On-device or smaller-class models for high-volume sync paths. Whisper on-device, small classifiers locally.

Realistic savings, by move

MoveTypical savings
Prompt caching on system prompts30-60%
Smaller model for sub-tasks20-50%
Structured outputs10-25% (also reliability)
Retrieval first, generation second30-70%
Cost ceilings + alertsPrevents 5-10× incident spikes

FAQ

Frequently asked.

How much does AI cost optimization typically save?

On a typical B2B SaaS feature, 40-70% cost reduction is realistic without quality loss. Most of it comes from prompt caching and using smaller models for the easy sub-tasks. The hard part is measuring it , you need cost telemetry per feature first.

Do open-weights save money?

At high volume yes, but not on day one. Operating a Llama 3 70B endpoint requires GPU infra, monitoring, scaling. Break-even vs cloud LLMs is usually somewhere between 10M and 100M tokens per month, depending on hardware.

What's the fastest cost win?

Turn on prompt caching for your system prompts. Anthropic and OpenAI both expose it. If your system prompt is large (>1000 tokens), caching alone saves 30-60% with one config change.

How do you track cost per feature?

We use Helicone or a custom layer that tags every LLM call with feature, tenant, and user. The dashboard surfaces cost-per-feature and per-tenant. Alerts fire when any cohort drifts.

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