How to Reduce LLM API Costs: Routing, Caching, Token Control

LLM API costs usually do not explode all at once. They creep up slowly: longer prompts, more users, bigger context windows, more retries, more premium-model calls, and more background automation.

The good news is that most teams can reduce LLM spend without making the product worse. The goal is not to use the cheapest model everywhere. The goal is to use the right model, context size, and routing rule for each task.

Start with visibility

Before optimizing cost, you need to know where cost comes from. At minimum, track:

model name
provider
user or team ID
request type
input tokens
output tokens
latency
retries
estimated cost
success or failure

Without this data, teams often optimize the wrong thing.

Use cheaper models for simple tasks

Not every request needs your strongest model. Many production tasks are simple:

classification
sentiment detection
short rewriting
title generation
tag extraction
routing decisions
FAQ matching
format cleanup

These tasks often work well on smaller, cheaper models. Reserve premium models for tasks where quality truly matters.

Route by workload

A model routing table can reduce cost while preserving quality.

| Task | Recommended routing |
|---|---|
| Simple extraction | Small fast model |
| Complex reasoning | Strong reasoning model |
| Long document Q&A | Long-context model |
| Code generation | Code-capable model |
| High-value enterprise user | Premium model |
| Free-tier user | Budget model |

Routing by workload is more effective than picking one default model for everything.

Shorten prompts

Prompt length is one of the easiest costs to overlook. Long system prompts, duplicated instructions, excessive examples, and oversized context blocks all increase cost.

Review your prompts for:

repeated instructions
examples that do not improve output
long policy text that could be summarized
retrieval chunks that are too large
hidden context that is rarely used

Small prompt savings compound quickly at scale.

Control output length

Output tokens can be more expensive than input tokens depending on the provider. If your app does not need long answers, set clear limits.

Use:

concise system instructions
max_tokens limits
structured output formats
shorter templates
summary-first responses

Do not pay for text your interface will hide.

Cache repeated work

Many AI products repeat the same or similar work:

generating summaries for unchanged documents
classifying the same records
embedding duplicate content
answering common support questions
processing repeated templates

Cache outputs when the input and model settings are stable. Even partial caching can reduce spend significantly.

Improve retrieval quality

RAG systems often waste tokens by sending too much irrelevant context to the model. Better retrieval reduces prompt size and improves answer quality.

Improve:

chunk size
metadata filters
reranking
query rewriting
deduplication
top-k limits

The cheapest token is the one you never send.

Limit retries

Retries are necessary, but uncontrolled retries multiply cost. Set clear retry rules:

retry only transient errors
use exponential backoff
cap retry count
fallback to another provider when appropriate
avoid retrying invalid requests

Track retry cost separately so it does not disappear into normal usage.

Set budgets and quotas

Cost controls should be built into the product, not handled manually after the invoice arrives.

Useful controls include:

per-user daily limits
per-team monthly limits
model access tiers
free-tier caps
alert thresholds
admin approval for premium models

For B2B products, these controls also make usage-based billing easier.

Final thoughts

LLM cost optimization is not one trick. It is a system: observability, routing, prompt discipline, caching, retrieval quality, and budget enforcement.

Start by measuring usage. Then move simple tasks to cheaper models, shorten prompts, control outputs, and route high-value requests to stronger models only when needed.