anth-cost-tuning

by jeremylongshore

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SKILL.md About

---
name: anth-cost-tuning
description: 'Optimize Anthropic Claude API costs with model routing, prompt caching,

batching, and spend monitoring.

Use when analyzing Claude API billing, reducing costs,

or implementing cost controls and budget alerts.

Trigger with phrases like "anthropic cost", "claude billing",

"reduce claude spend", "anthropic budget", "claude pricing optimize".

'
allowed-tools: Read, Write, Edit, Grep
version: 1.0.0
license: MIT
author: Jeremy Longshore <jeremy@intentsolutions.io>
tags:
- saas
- ai
- anthropic
compatibility: Designed for Claude Code
---
# Anthropic Cost Tuning

## Overview

Optimize Claude API spend through model routing, prompt caching, the Message Batches API, and real-time cost tracking. The four biggest levers: model selection (4-19x), prompt caching (10x input), batches (2x), and `max_tokens` discipline.

## Pricing Reference (per million tokens)

| Model | Input | Output | Cache Read | Cache Write |
|-------|-------|--------|------------|-------------|
| Claude Haiku | $0.80 | $4.00 | $0.08 | $1.00 |
| Claude Sonnet | $3.00 | $15.00 | $0.30 | $3.75 |
| Claude Opus | $15.00 | $75.00 | $1.50 | $18.75 |

**Message Batches:** 50% off all model pricing for async processing.

## Cost Calculator

```python
def estimate_cost(
    input_tokens: int,
    output_tokens: int,
    model: str = "claude-sonnet-4-20250514",
    cached_input: int = 0,
    use_batch: bool = False
) -> float:
    pricing = {
        "claude-haiku-4-20250514": {"input": 0.80, "output": 4.00, "cache_read": 0.08},
        "claude-sonnet-4-20250514": {"input": 3.00, "output": 15.00, "cache_read": 0.30},
        "claude-opus-4-20250514": {"input": 15.00, "output": 75.00, "cache_read": 1.50},
    }
    rates = pricing[model]
    uncached_input = input_tokens - cached_input

cost = (
        uncached_input * rates["input"] +
        cached_input * rates["cache_read"] +
        output_tokens * rates["output"]
    ) / 1_000_000

if use_batch:
        cost *= 0.5

return cost

# Example: 10K requests/day, 500 input + 200 output tokens each
daily = estimate_cost(500, 200, "claude-sonnet-4-20250514") * 10_000
print(f"Daily: ${daily:.2f}")      # ~$0.045 * 10K = $450/day
print(f"Monthly: ${daily * 30:.2f}")  # ~$13,500/month

# Same with Haiku + batching
daily_optimized = estimate_cost(500, 200, "claude-haiku-4-20250514", use_batch=True) * 10_000
print(f"Optimized: ${daily_optimized:.2f}/day")  # ~$22/day (20x cheaper)
```

## Strategy 1: Model Routing

```python
def route_to_model(task: str, complexity: str) -> str:
    """Route tasks to cheapest adequate model."""
    # Haiku: classification, extraction, yes/no, routing ($0.80/$4)
    if task in ("classify", "extract", "route", "validate"):
        return "claude-haiku-4-20250514"

# Sonnet: general tasks, code, tool use ($3/$15)
    if complexity in ("low", "medium"):
        return "claude-sonnet-4-20250514"

# Opus: only for complex reasoning, research ($15/$75)
    return "claude-opus-4-20250514"
```

## Strategy 2: Prompt Caching

```python
# Cache system prompts and reference documents (90% input savings)
# Break-even: 2 requests with same cached content
message = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=256,
    system=[{
        "type": "text",
        "text": large_reference_document,  # 10K+ tokens
        "cache_control": {"type": "ephemeral"}
    }],
    messages=[{"role": "user", "content": user_question}]
)
```

## Strategy 3: Batches for Non-Real-Time

```python
# 50% cost reduction for anything that doesn't need immediate response
# Ideal for: summarization pipelines, data extraction, content generation
batch = client.messages.batches.create(requests=[...])  # Up to 100K requests
```

## Strategy 4: Spend Tracking

```python
import anthropic
from dataclasses import dataclass, field

@dataclass
class SpendTracker:
    budget_usd: float = 100.0
    spent_usd: float = 0.0
    requests: int = 0

def track(self, response):
        cost = estimate_cost(
            response.usage.input_tokens,
            response.usage.output_tokens,
            response.model,
            getattr(response.usage, "cache_read_input_tokens", 0)
        )
        self.spent_usd += cost
        self.requests += 1

if self.spent_usd > self.budget_usd * 0.8:
            print(f"WARNING: 80% budget used (${self.spent_usd:.2f}/${self.budget_usd})")
        if self.spent_usd > self.budget_usd:
            raise RuntimeError(f"Budget exceeded: ${self.spent_usd:.2f}")

tracker = SpendTracker(budget_usd=50.0)
```

## Cost Reduction Checklist

- [ ] Use Haiku for classification/extraction/routing tasks
- [ ] Enable prompt caching for repeated system prompts
- [ ] Use Message Batches for non-real-time processing
- [ ] Set `max_tokens` to realistic values (not maximum)
- [ ] Use prefill to reduce output preamble tokens
- [ ] Implement spend tracking and budget alerts
- [ ] Monitor via [Usage API](https://docs.anthropic.com/en/api/usage-cost-api)

## Resources

- [Pricing](https://docs.anthropic.com/en/docs/about-claude/pricing)
- [Prompt Caching](https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching)
- [Usage & Cost API](https://docs.anthropic.com/en/api/usage-cost-api)
- [Message Batches](https://docs.anthropic.com/en/api/creating-message-batches)

## Next Steps

For architecture patterns, see `anth-reference-architecture`.