How to Cut Your AI API Costs by 80% Without Sacrificing Quality
Practical strategies for reducing LLM API costs in production — model routing, caching, prompt compression, batching, and more.
AI API costs can spiral fast. A product that costs £500/month at launch can hit £50,000/month at scale if you're not careful. Here's how to manage it.
The 80/20 of AI Cost Reduction
80% of your cost savings will come from two things:
Everything else is optimisation on the margins — still worth doing, but get these two right first.
1. Model Routing
Don't use your most powerful (expensive) model for everything. Route tasks to the cheapest model that can handle them.
```python
def choose_model(task_type: str) -> str:
routing = {
"classification": "claude-haiku-4-5", # Simple, fast, cheap
"summarisation": "claude-sonnet-4-6", # Medium complexity
"complex_reasoning": "claude-opus-4-6", # Needs best quality
"code_review": "claude-sonnet-4-6", # Usually Sonnet is enough
}
return routing.get(task_type, "claude-sonnet-4-6")
```
Typical savings: 60-80% on tasks currently using Opus that could use Sonnet or Haiku.
2. Prompt Caching
For prompts with large, repeated content (system prompts, documents, instructions), caching eliminates redundant processing.
Anthropic's prompt caching:
```typescript
const response = await anthropic.messages.create({
model: "claude-sonnet-4-6",
system: [{ type: "text", text: long_system_prompt, cache_control: { type: "ephemeral" } }],
messages: [{ role: "user", content: user_query }],
});
```
Typical savings: 40-90% on applications with large system prompts.
3. Semantic Caching
Cache LLM responses for semantically similar queries:
```python
from gptcache import cache
from gptcache.embedding import Onnx
from gptcache.similarity_evaluation.distance import SearchDistanceEvaluation
cache.init(
embedding_func=Onnx().to_embeddings,
similarity_evaluation=SearchDistanceEvaluation(),
)
# Now identical or similar queries hit the cache instead of the API
```
Typical savings: 30-60% for applications with repetitive queries.
4. Prompt Compression
Long prompts cost more. Remove unnecessary content:
Typical savings: 20-40% on RAG applications.
5. Batching
Instead of one API call per item, batch multiple items:
```python
# Instead of 100 individual calls:
for doc in documents:
result = llm.invoke(f"Classify: {doc}")
# One call with all documents:
prompt = "Classify each of the following:\n" + "\n".join(documents)
results = llm.invoke(prompt)
```
Typical savings: 40-60% on batch processing tasks.
6. Output Length Control
Shorter outputs cost less. Be explicit about length:
```
Return ONLY a JSON object. No explanation. No preamble.
Limit your response to 200 words maximum.
```
Monitoring
You can't optimise what you don't measure. Track:
Tools: LangSmith, Langfuse, custom dashboards.
Realistic Targets
Starting from a naive implementation:
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