The Real Cost of Running Claude in Production

I track every penny of Claude API spend across my projects. Here's the breakdown of what costs what, and where the savings are.

I run Claude in production for several clients. Here's what costs add up.

Cost components

For a typical RAG-style chat product:

• System prompt: 2-4K tokens, mostly cached
• Retrieved context: 5-30K tokens, partially cached
• User message: 50-300 tokens, never cached
• Response: 200-2K tokens, never cached

Without caching, every request pays for the full input. With caching, only the new bits.

The caching multiplier

Anthropic's prompt caching cuts cached-token cost by ~10x. The trick: keep the prefix of your prompt stable. A single timestamp in the cached prefix invalidates the cache.

For a chat product with stable system prompt + stable retrieval scaffolding + variable user messages, caching saves 50-80% on token cost.

My standard cost model

For a customer-support chat agent on Claude Sonnet 4.6:

• 8K-token system prompt (cached)
• 12K-token retrieved context (cached for the session)
• 200-token user message
• 600-token response

Per turn (with cache): ~$0.004. Per 1000 turns: $4.

Compared to a naively-implemented version (no caching): $0.024 per turn. 6x more expensive.

Where the surprises are

• Long context degrades cost. Filling 1M tokens is expensive even with caching. Use it only when it earns its keep.
• Tool use multiplies turns. Each tool call is a round-trip. An agent that uses 5 tools per task is 5x the turns of a single-shot generation.
• Streaming doesn't save cost. Same token count, different delivery.

What I tell clients

Budget for AI cost like you budget for AWS. Track it daily. Set alerts at 50%, 80%, 100% of budget. Make a person responsible for the bill.

Also: token cost will probably halve again in 12 months. Don't over-engineer for token efficiency at the cost of code quality. Sometimes "expensive" is the right answer for now.

References

• Anthropic pricing
• Prompt caching docs