Cost Optimization Strategies

Strategy 1: Be specific in queries

# ❌ Expensive - reads entire file
"Check auth.ts for issues"
# ~5K tokens if file is large

# ✅ Cheaper - targets specific location
"Check the login function in auth.ts:45-60"
# ~500 tokens

Strategy 2: Use /compact proactively

# Without /compact - conversation grows
Context: 10% → 30% → 50% → 70% → 90%
Cost per message increases as context grows

# With /compact at 70%
Context: 10% → 30% → 50% → 70% → [/compact] → 30% → 50%
Frees significant context space for subsequent messages

Strategy 3: Choose the right model

# Use Haiku for simple tasks (4x cheaper input, 3.75x cheaper output)
claude --model haiku "Fix this typo in README.md"

# Use Sonnet (default) for standard work
claude "Refactor this module"

# Use Opus only for critical/complex tasks
claude --model opus "Design the entire authentication system"

Strategy 4: Limit MCP servers

// ❌ Expensive - 5 MCP servers loaded
{
  "mcpServers": {
    "serena": {...},
    "context7": {...},
    "sequential": {...},
    "playwright": {...},
    "postgres": {...}
  }
}
// ~10K tokens overhead per session

// ✅ Cheaper - load only what you need
{
  "mcpServers": {
    "serena": {...}  // Only for this project
  }
}
// ~2K tokens overhead

Strategy 5: Batch operations

# ❌ Expensive - 5 separate prompts
"Read file1.ts"
"Read file2.ts"
"Read file3.ts"
"Read file4.ts"
"Read file5.ts"

# ✅ Cheaper - single batched request
"Read file1.ts, file2.ts, file3.ts, file4.ts, file5.ts and analyze them together"
# Shared context, single response

Strategy 6: Use prompt caching for repeated context (API)

If you call the Anthropic API directly (e.g., for custom agents or pipelines), prompt caching cuts costs by up to 90% on repeated prefixes.

# Mark stable sections with cache_control
response = client.messages.create(
    model="claude-sonnet-4-6-20250514",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": "<your large system prompt / codebase context>",
            "cache_control": {"type": "ephemeral"}  # Cache this prefix
        }
    ],
    messages=[{"role": "user", "content": "Fix the bug in auth.ts"}]
)

Prompt caching economics:

Operation	Cost multiplier	TTL
Cache write	1.25x base price	5 minutes (default)
Cache write (extended)	2x base price	1 hour
Cache read (hit)	0.1x base price	—
Latency reduction	Up to 85% for long prompts	—

Break-even: 2 cache hits with 5-minute TTL. After that, pure savings.

Rules:

• Max 4 cache breakpoints per request
• Cache key = exact prefix match (single character change = cache miss)
• Place breakpoints after large stable sections: system prompt, tool definitions, codebase context
• For Claude Code itself: caching is handled automatically by the CLI. This applies to API-based workflows you build on top of Claude

Docs: prompt caching