claude-api-cost-optimizer

name	claude-api-cost-optimizer
description	Minimize Anthropic Claude API costs through model selection, prompt caching, batching, and cost tracking. Trigger phrases: reduce API costs, optimize Claude spending, save on API calls, Claude cost optimization, cheaper Claude models.
metadata	{"clawdbot":{"emoji":"💰","requires":{"bins":[]},"env":["ANTHROPIC_API_KEY"],"os":["linux","darwin","win32"],"homepage":"https://clawhub.com/djc00p/claude-api-cost-optimizer"}}

Claude API Cost Optimizer

Cut Claude API costs by 70–90% using intelligent model selection, caching, and batching.

Quick Start

1. Audit your current API calls — identify which tasks use Opus or Sonnet that could use Haiku. Model selection alone saves 10–18x on simple tasks.
2. Pick the cheapest model tier for each task: Haiku (cheapest) → Sonnet (mid) → Opus (most expensive, use sparingly). See references/pricing.md for current rates.
3. Enable prompt caching for repeated context (system prompts, codebases) by adding "cache_control": {"type": "ephemeral"} to message blocks
4. Implement cost reporting — track input_tokens, output_tokens, and cache metrics from API responses

Key Concepts

• Model selection — Haiku for simple tasks (formatting, comments) — cheapest tier. Sonnet for medium (refactoring, debugging) — mid tier. Opus for complex only (architecture, security) — most expensive, use sparingly. See references/pricing.md for current rates.
• Prompt caching — Cache large static content (system prompts, codebase context). Cache reads cost 90% less; writes pay off after 1–2 reuses.
• Batching — Combine multiple requests into one API call to eliminate per-request overhead. 80% fewer calls ≈ 80% lower cost.
• Local caching — Cache identical responses locally to skip redundant API calls entirely.
• Context extraction — Send only relevant snippets, not whole files. Smaller inputs = lower costs.
• max_tokens discipline — Set realistic limits; unused token budget is wasted money.

Common Usage

Code examples are in Python but concepts apply to any language or SDK.

Model selection pattern:

def select_model(task_type: str) -> str:
    simple_tasks = ["formatting", "comments", "explanation", "rename"]
    complex_tasks = ["architecture", "algorithm", "security_audit"]
    return ("claude-haiku-4-5-20251001" if task_type in simple_tasks else
            "claude-opus-4-6" if task_type in complex_tasks else
            "claude-sonnet-4-6")

Prompt caching:

response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    system=[{
        "type": "text",
        "text": system_prompt,
        "cache_control": {"type": "ephemeral"}
    }],
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": f"Code:\n{source_code}", 
             "cache_control": {"type": "ephemeral"}},
            {"type": "text", "text": query}
        ]
    }]
)

Cost tracking:

usage = response.usage
cost = (usage.input_tokens * INPUT_RATE +
        usage.cache_creation_input_tokens * CACHE_WRITE_RATE +
        usage.cache_read_input_tokens * CACHE_READ_RATE +
        usage.output_tokens * OUTPUT_RATE)

References

• references/implementation.md — Full implementation patterns, model routing, caching setup, batching, retry logic, and anti-patterns
• references/pricing.md — Current pricing, cache cost math, savings calculations, and batch API details