LLM Cost Optimization: Cut Your AI Bill by 60%

Your LLM bill is killing your margins. Here's how to cut costs without sacrificing quality.

The LLM Cost Problem

Real example: A SaaS startup processing 1M requests/month:

Before optimization:

Model: GPT-4 Turbo
Avg tokens/request: 2,000 input + 500 output
Cost: $0.03/request
Monthly bill: $30,000

After optimization:

Reduced to $12,000/month (60% savings)
Same quality, better latency

Here's how they did it...

Strategy #1: Prompt Optimization (Save 20-30%)

Before (500 tokens)

You are a helpful customer service assistant for Acme Corp. We sell software products. Our mission is to help customers succeed. We value transparency, innovation, and customer satisfaction.

Customer context: [200 tokens of history]

Answer this question: "What is your return policy?"

After (150 tokens)

Customer service bot. Answer from docs:
{docs}

Q: "What is your return policy?"

Savings: 70% fewer input tokens

Optimization Tactics

Remove fluff ("we value...", "our mission...")
Use abbreviations where safe
Only include relevant context (not full history)
Compress JSON (remove whitespace)

Strategy #2: Caching (Save 30-50%)

The problem: Sending the same system prompt 1M times/month.

Solution: Use OpenAI's prompt caching (50% discount on cached tokens).

System prompt: 1,000 tokens (cached)

User query: 50 tokens (not cached)

Total cost:

Without cache: $0.01 input + $0.03 output = $0.04
With cache: $0.005 input + $0.03 output = $0.035
Savings: 12.5%

Multiply by 1M requests → $5K/month saved

What to Cache

System prompts
RAG context (if same docs fetched often)
Few-shot examples
Long instructions

Strategy #3: Model Selection (Save 40-80%)

Not every task needs GPT-4. Use the right model for the job:

Task	Wrong Model	Right Model	Savings
Classification	GPT-4	GPT-3.5	85%
Simple Q&A	GPT-4	GPT-4o-mini	75%
Summarization	GPT-4	Claude Haiku	90%
Complex reasoning	-	GPT-4 (necessary)	0%

Model Routing Strategy

if task == "classification":
  use_model("gpt-3.5-turbo")
elif task == "simple_qa":
  use_model("gpt-4o-mini")
else:
  use_model("gpt-4-turbo")

Result: 60% of queries routed to cheaper models → 40% cost reduction

Strategy #4: Output Length Control (Save 10-20%)

You're charged for output tokens. Control them:

Add to prompt:

"Answer in 2-3 sentences max."
"Be concise. No fluff."
"Respond in <50 words."

# Or set max_tokens explicitly
response = openai.chat.completions.create(
  model="gpt-4",
  messages=messages,
  max_tokens=100 # Cap at 100 tokens
)

Strategy #5: Batching (Save 15-25%)

Process multiple requests in one API call:

Before: 10 separate API calls

10 API calls × 2,000 tokens = 20,000 tokens
Cost: $0.20

After: 1 batched API call

1 API call × 15,000 tokens (shared system prompt)
Cost: $0.15
Savings: 25%

Strategy #6: Streaming for Better UX (Not cost savings, but important)

While not cheaper, streaming improves perceived performance:

Users see responses faster
Can stop generation early (saves tokens)
Better UX = lower churn = better ROI

Strategy #7: Fallback to Fine-Tuned Models (Advanced)

For repetitive tasks, fine-tune a smaller model:

Example: Customer support classification
Before: GPT-4 ($0.03/request)
After: Fine-tuned GPT-3.5 ($0.0015/request)
Savings: 95%

⚠️ Trade-off: Upfront fine-tuning cost + maintenance

Real Case Study: 60% Cost Reduction

Company: B2B SaaS (1M requests/month)

Before: $30K/month

Changes made:

Prompt optimization → 25% savings
Caching → 15% savings
Model routing (60% to GPT-3.5) → 20% savings
Output length control → 5% savings

After: $12K/month

Annual savings: $216K

Cost Monitoring Best Practices

Track cost per request: Identify expensive queries
Set budgets: Alert when approaching limits
A/B test optimizations: Verify quality doesn't degrade
Review monthly: Look for anomalies

Quick Wins (This Week)

Day 1: Audit your prompts—remove fluff
Day 2: Enable prompt caching
Day 3: Route simple tasks to GPT-3.5
Day 4: Add output length limits
Day 5: Measure savings

Expected savings: 30-40% in Week 1

Conclusion

LLM costs can be optimized significantly without sacrificing quality. Start with prompt optimization and caching—those alone can save 30-50%.

Tools to help:

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