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Aparna Pradhan
fullstack dev specia... • 6h
🚀 How to Save 90% on Your AI Costs Here’s exactly how we cut AI costs from $500/month to $5/month: 1. Avoid LLMs When Possible Use rules, regex, or database lookups for simple tasks (60–80% of workflows). Example: A lead qualification bot using regex + DB lookup handles 90% of interactions with zero LLM costs. 2. Cache Responses Prompt Caching: Store system prompts/context to avoid reprocessing. Without caching: 1,000 questions × 5,100 tokens = $12.75/month. With caching: 1,000 questions × 100 tokens = $0.35/month. Savings: 97%. Semantic Caching: Reuse answers for similar questions. Example: "How do I reset my password?" asked 500 times → only pay for 1 LLM call. 3. Right-Size Your Models Tiny Models (1B–3B): Perfect for classification, sentiment analysis, and short content. Cost: $0.10–$0.20 per 1M tokens. Mid-Size Models (7B–14B): Ideal for chatbots and content generation. Cost: $0.20–$0.60 per 1M tokens. Large Models (70B+): Only for complex reasoning. Cost: $0.50–$10.00 per 1M tokens. 4. Batch Operations Combine multiple tasks into one API call. Example: Classify 10 emails in one call instead of 10 separate calls. 5. Intelligent Routing Use a small model to decide whether to use a larger model or no model at all. Example routing: 70% of tasks: No LLM (rules/DB). 25% of tasks: 7B model ($0.20–$0.60 per 1M tokens). 5% of tasks: 70B model ($0.50–$1.00 per 1M tokens). Savings: Up to 95% vs. always using large models. Real Cost Comparison (10,000 calls/month): Naive Approach (GPT-4o for everything): $250/month. Optimized Approach: 7,000 calls: No LLM ($0). 2,500 calls: Tiny Model ($0.50). 500 calls: Mid-Size Model ($1.50). Total: $2/month (99% savings). The key to cost savings? Smart architecture, not just technology. What’s your current setup? Share your use case, and I’ll show you the exact savings! read full blog here : https://lnkd.in/gXPxvPHs and : https://medium.com/@ap3617180/from-instagram-dms-to-closed-deals-building-a-vertical-ai-system-with-langgraph-and-graphiti-neo4j-0b8aa02509dc
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