Reduce LLM API Costs by 80% with Semantic Caching

The Hidden Cost of LLM APIs

At $0.03-$0.06 per 1K tokens (GPT-4), costs add up quickly:

• 100K daily requests × 500 tokens avg = 50M tokens/day
• Monthly cost: ~$45,000-$90,000

Many of these requests are semantically identical — users asking the same questions in slightly different ways.

How Semantic Cache Works

SafeLLM’s L0 layer intercepts requests before they reach the LLM:

User Request → SHA256 Hash → Redis Lookup
                              ↓
                         Cache HIT? → Return cached response (<0.1ms)
                              ↓
                         Cache MISS → Continue to security layers → LLM

Cache Key Strategy

cache_key = hashlib.sha256(prompt.encode()).hexdigest()

For semantic similarity (Enterprise), we use embedding-based matching.

Real-World Results

Configuration

# Enable cache layer
export ENABLE_CACHE=true

# Cache TTL (default: 1 hour)
export CACHE_TTL=3600

# Redis connection
export REDIS_URL=redis://localhost:6379

Enterprise: Redis Sentinel HA

For production deployments:

export REDIS_SENTINEL_ENABLED=true
export REDIS_SENTINEL_HOSTS=sentinel-1:26379,sentinel-2:26379
export REDIS_SENTINEL_MASTER=mymaster

Automatic failover ensures cache availability even during Redis failures.

ROI Calculator

Use our Token ROI Dashboard (Enterprise) to visualize:

• Requests served from cache vs LLM
• Cost savings in real-time
• Cache hit rate trends

Start saving today: GitHub OSS | Enterprise Demo