- 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
- 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.
- Pick the cheapest model tier for each task: Haiku (cheapest) → Sonnet (mid) → Opus (most expensive, use sparingly). See
references/pricing.mdfor current rates. - Enable prompt caching for repeated context (system prompts, codebases) by adding
"cache_control": {"type": "ephemeral"}to message blocks - 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.mdfor 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:\
{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-patternsreferences/pricing.md— Current pricing, cache cost math, savings calculations, and batch API details