AI Inference Cost Optimization

The Cost Problem

AI inference costs can spiral quickly. A typical production app making 1M API calls/month can easily spend $5,000-50,000 on LLM APIs alone.

Strategy 1: Model Selection & Routing

Not every request needs GPT-4. Route simple queries to cheaper models:

python
def route_request(query, complexity_score):
    if complexity_score < 0.3:
        return "gpt-3.5-turbo"  # $0.002/1K tokens
    elif complexity_score < 0.7:
        return "gpt-4o-mini"    # $0.015/1K tokens
    else:
        return "gpt-4o"         # $0.05/1K tokens

Strategy 2: Semantic Caching

Cache responses for semantically similar queries:

python
from sentence_transformers import SentenceTransformer
import numpy as np
model = SentenceTransformer('all-MiniLM-L6-v2')
cache = {}
def cached_llm_call(query, threshold=0.95):
    embedding = model.encode(query)
    for cached_query, (cached_embedding, response) in cache.items():
        similarity = np.dot(embedding, cached_embedding)
        if similarity > threshold:
            return response  # Cache hit!
    response = call_llm(query)
    cache[query] = (embedding, response)
    return response

Strategy 3: Prompt Compression

Reduce token count without losing information:

Remove redundant instructions

Use shorter examples

Compress context with summarization

Strategy 4: Response Streaming

Stream responses to reduce perceived latency and allow early termination for long responses.

Strategy 5: Batch Processing

Group non-urgent requests for batch processing at lower rates.

Real-World Results

Companies implementing these strategies report 50-80% cost reduction while maintaining quality.