Cost Optimization

AI is expensive. A single GPT-4 request can cost $0.03. If you have 10,000 users, that's $300/day.

Strategies

1. Model Routing (The 80/20 Rule)

80% of user queries are simple ("Hi", "Thanks", "Summarize this short text"). Don't use GPT-4 for everything.

const isComplex = await classifier.classify(prompt); // Cheap BERT model
const model = isComplex ? 'gpt-4o' : 'gpt-4o-mini';

Savings: 20x cheaper.

2. Semantic Caching

If User A asks "Who is the President?", and User B asks "Who is the current President?", they should get the same cached answer. Use Redis or a specialized cache (GPTCache) to store (embedding(prompt), response).

Savings: 100% (Free).

3. Prompt Compression

Shorter prompts = Lower cost.

• Remove polite phrases ("Please", "Thank you").
• Use specialized syntax instead of verbose English.

4. Self-Hosting (for high volume)

If you spend > $5k/month, consider hosting Llama 3 on your own GPU server (AWS EC2 / RunPod).

Budgeting

Formula: Cost = (Input Tokens * Price_In) + (Output Tokens * Price_Out)

Rule of Thumb:

• 1,000 tokens ≈ 750 words.
• Output is usually 3x more expensive than Input.
• RAG apps have Huge Inputs (Context) and Small Outputs.

Alerts

Set a Hard Limit in OpenAI. If you don't, a while(true) loop in your code could cost you $10,000 overnight.