The 80/20 Rule of AI Costs
Most AI applications waste 40-60% of their token budget. Here are proven strategies used by companies processing billions of tokens per month.
Strategy 1: Model Routing
Not every request needs your most powerful model. A "router" sends simple questions to cheap models and complex ones to expensive models. In practice, 70-80% of requests can be handled by mini/flash models. If 75% of your traffic is simple, you save 60%+ on those requests.
Strategy 2: Prompt Caching
If you send the same system prompt every request, you're paying for those tokens every time. Many providers now offer prompt caching — stored server-side, you pay once. Anthropic's caching can reduce costs by up to 90% on the cached portion.
Strategy 3: Response Caching
Cache responses for similar questions. A simple hash of the input serves as a cache key. Even a 10% hit rate saves 10% of your budget with zero quality loss.
Strategy 4: Batch Processing
Most providers offer 50% discounts for batch (non-real-time) calls. If your workload doesn't need instant responses — nightly reports, bulk content, data processing — batch mode cuts costs in half.
Strategy 5: Prompt Engineering
Shorter prompts = fewer tokens = lower costs. Use structured output (JSON), set max_tokens, remove redundant instructions.
Strategy 6: Choose the Right Provider
DeepSeek-V3 costs $0.27/$1.10 while delivering quality comparable to models 5-10x the price. Open-source models via Together AI can be even cheaper.
💡 Combining strategies 1 + 2 + 4 typically yields 50-70% cost reductions. Start with model routing — it's the highest-impact change.