Token Efficiency: Get More From AI APIs While Spending Less
Your API bill is too high. Cut it by 80% without losing quality using these five techniques.
How APIs Price Tokens
Every AI API call costs money based on tokens. 1 token ≈ 4 characters.
Input tokens: What you send to the model Output tokens: What the model generates
Pricing example (Claude): Input $3 per million tokens, Output $15 per million tokens.
The Math
You send 10,000 tokens. Model generates 2,000 tokens.
Cost: (10,000 * $3M) + (2,000 * $15M) = $0.03 + $0.03 = $0.06
Do this 1000 times/day: $60/day = $1,800/month.
That adds up.
Techniques to Reduce Tokens
1. Shorter Prompts
Instead of long verbose prompts, use short ones. Both work. Short is 70% fewer tokens.
2. Caching
If you always analyze the same document against different questions, cache the document.
Claude supports prompt caching: same prompt prefix = cheaper repeated calls.
3. Smaller Models
Claude 3 Haiku (cheaper) vs Claude 3.5 Sonnet (expensive).
For simple tasks, Haiku is 75% cheaper with only 5% lower quality.
4. Batch Processing
Some APIs have batch endpoints (10% cheaper) for non-urgent work.
5. Fine-Tuned Models
Cheaper per token once you pay the fine-tuning cost upfront.
Real Example: Customer Support Agent
Inefficient: Full customer profile (2000 tokens), Full ticket thread (3000 tokens), System prompt (500 tokens). Per ticket cost: $0.28. Processing 100 tickets: $28.
Efficient: Summary of profile (200 tokens) via cache, Relevant ticket section (300 tokens), Minimal system prompt (100 tokens). Per ticket cost: $0.018. Processing 100 tickets: $1.80.
Savings: 93%
The Trend
APIs are adding better caching and batching. Token usage will keep dropping as models get smarter (same result with fewer tokens).
By 2028, token efficiency matters more than model quality for most applications.