Mobius AI - AI Cost Optimization Platform

Overview

Smart caching is one of the most effective cost optimization techniques for AI applications. By intelligently storing and reusing responses, you can achieve 10-20% cost reductions while improving response times and reducing API rate limiting issues.

Real Impact

Enterprise Case Study: A SaaS company reduced their monthly OpenAI costs from $15,000 to $12,200 (18.6% savings) by implementing semantic caching for their customer support chatbot.

Caching Strategies

1. Exact Match Caching

The simplest approach: cache responses for identical prompts. Best for FAQ systems and repeated queries.

Implementation Example

import hashlib
import redis
import json

class ExactMatchCache:
    def __init__(self, redis_client):
        self.redis = redis_client
        self.ttl = 86400  # 24 hours
    
    def get_cache_key(self, prompt: str, model: str) -> str:
        content = f"{model}:{prompt}"
        return f"ai_cache:{hashlib.md5(content.encode()).hexdigest()}"
    
    def get(self, prompt: str, model: str):
        key = self.get_cache_key(prompt, model)
        cached = self.redis.get(key)
        if cached:
            return json.loads(cached)
        return None
    
    def set(self, prompt: str, model: str, response: dict):
        key = self.get_cache_key(prompt, model)
        self.redis.setex(key, self.ttl, json.dumps(response))

# Usage
cache = ExactMatchCache(redis.Redis())
response = cache.get(prompt, "gpt-4")
if not response:
    response = openai_client.chat.completions.create(...)
    cache.set(prompt, "gpt-4", response)

Expected Savings: 5-8% for applications with repeated queries

2. Semantic Similarity Caching

More sophisticated approach using embeddings to find similar prompts. Higher implementation complexity but better cache hit rates.

Semantic Cache Architecture

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
import openai

class SemanticCache:
    def __init__(self, similarity_threshold=0.95):
        self.cache = {}
        self.embeddings = {}
        self.threshold = similarity_threshold
    
    def get_embedding(self, text: str):
        response = openai.Embedding.create(
            model="text-embedding-ada-002",
            input=text
        )
        return response['data'][0]['embedding']
    
    def find_similar(self, prompt: str):
        if not self.embeddings:
            return None
            
        query_embedding = self.get_embedding(prompt)
        
        for cached_prompt, cached_embedding in self.embeddings.items():
            similarity = cosine_similarity(
                [query_embedding], 
                [cached_embedding]
            )[0][0]
            
            if similarity >= self.threshold:
                return self.cache[cached_prompt]
        
        return None
    
    def store(self, prompt: str, response: dict):
        embedding = self.get_embedding(prompt)
        self.embeddings[prompt] = embedding
        self.cache[prompt] = response

# Usage with similarity matching
semantic_cache = SemanticCache(similarity_threshold=0.95)
similar_response = semantic_cache.find_similar(new_prompt)
if similar_response:
    # Use cached response (costs $0)
    return similar_response
else:
    # Make new API call and cache
    response = openai_client.chat.completions.create(...)
    semantic_cache.store(new_prompt, response)

Expected Savings: 12-20% for applications with similar but not identical queries

3. Time-based Cache Invalidation

Implement smart TTL policies based on content type and freshness requirements.

Static Content

• Technical documentation: 7 days
• Code explanations: 30 days
• General knowledge: 90 days

Dynamic Content

• Market data analysis: 1 hour
• News summaries: 6 hours
• User-specific content: 24 hours

Implementation Best Practices

Monitor Cache Hit Rates: Aim for 15-25% hit rate for optimal ROI. Track daily to identify trends.

Implement Cache Warming: Pre-populate cache with common queries during low-traffic periods.

Use Distributed Caching: Redis Cluster or similar for high-availability production systems.

Gradual Rollout: Start with 10% traffic, monitor quality, then scale to 100%.

Common Pitfalls

Over-aggressive Caching: Similarity threshold too low can return irrelevant results.

Ignoring Cache Costs: Embedding generation for semantic cache has costs too - factor into ROI.

No Cache Validation: Always include mechanism to validate cached response quality.

Measuring Success

Key Metrics to Track

Cost Metrics

• Monthly API cost reduction
• Cost per request
• Cache infrastructure costs
• Total cost of ownership

Performance Metrics

• Cache hit rate percentage
• Average response time
• API rate limit utilization
• User satisfaction scores

Ready to Implement Smart Caching?

Get monitoring and optimization tools to track your caching implementation success and maximize cost savings.

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Smart Caching for AI: 12% Cost Reduction