Managing Client LLM Costs

LLM costs can quickly spiral out of control if not managed properly. This guide covers strategies for keeping costs predictable while maintaining automation quality.

Understanding LLM Pricing

Token-Based Pricing

Most LLMs charge per token (roughly 4 characters or 0.75 words):

Model	Input Cost (1M tokens)	Output Cost (1M tokens)
GPT-4o	$2.50	$10.00
GPT-4o mini	$0.15	$0.60
Claude 3.5 Sonnet	$3.00	$15.00
Claude 3 Haiku	$0.25	$1.25

Factors That Increase Costs

• Long prompts (system prompts, few-shot examples)
• Long outputs (detailed responses, JSON structures)
• Retry logic on failures
• Redundant processing

Cost Optimization Strategies

1. Choose the Right Model for Each Task

Not every task needs GPT-4:

• Classification/routing: Use GPT-4o mini or Claude Haiku
• Simple extraction: Use faster, cheaper models
• Complex reasoning: Use GPT-4o or Claude Sonnet
• Long documents: Consider Claude for larger context windows

Rule of thumb: Start with the cheapest model that works, upgrade only if quality suffers.

2. Optimize Your Prompts

Shorter prompts = lower costs:

Before (423 tokens):

You are an expert email classifier. Your job is to read emails
and classify them into categories. The categories are: support,
sales, billing, spam. Please analyze the following email carefully
and determine which category it belongs to. Consider the subject,
sender, and body content. Return only the category name.


After (67 tokens):
```
Classify this email as: support, sales, billing, or spam.
Return only the category name.

Email:
```

3. Cache Repeated Requests

If you process similar content frequently:

• Hash the input content
• Check cache before API call
• Store results with TTL
• Track cache hit rates

Caching can reduce costs by 30-60% for repetitive workflows.

4. Batch Processing

Instead of one request per item:

Process these 10 items:
1. [item 1]
2. [item 2]
...

Reduces API overhead and can improve consistency.

5. Implement Cost Alerts

Set up monitoring for:

• Daily spend per client
• Cost per workflow execution
• Unusual spikes in usage
• Token usage trends

Client Billing Strategies

Fixed Monthly Fee

• Easiest to sell and manage
• You absorb cost variance
• Build in 20-30% buffer

Cost Plus Markup

• Pass through LLM costs + markup (typically 30-50%)
• More transparent
• Requires accurate tracking

Usage Tiers

• Base fee includes X executions
• Overage charged per execution
• Good balance of predictability and fairness

Monitoring Tools

Track these metrics per client:

• Total tokens used (input/output separately)
• Cost per workflow type
• Average cost per execution
• Model usage distribution
• Cache hit rate

Communicating Costs to Clients

Be transparent about:

• What drives costs
• How you optimize
• What would increase costs
• Monthly cost reports

Frame it as partnership in efficiency, not a surprise expense.

When to Absorb vs. Pass Through

Absorb costs when:
- On fixed-fee retainers
- During pilot phases
- For small, predictable workloads

Pass through costs when:
- High-volume workflows
- Client controls input volume
- Enterprise agreements

Cost Reduction Checklist

• [ ] Using cheapest effective model?
• [ ] Prompts optimized for length?
• [ ] Caching implemented?
• [ ] Batching where possible?
• [ ] Cost alerts configured?
• [ ] Monthly tracking in place?