[Share] Dify + Claude with Prompt Caching: Monthly bill dropped from $35 to $4

[lei83314]

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Content

Sharing a real test result — switching Dify workflows to Claude with Prompt Caching enabled made a huge difference in costs.

Use Case

Fixed 5000-token system prompt (knowledge base + role definition), ~100 calls per day.

Cost Comparison

Setup	Monthly Cost
Without Cache	~$35
With Cache	~$4

89% reduction.

How to Enable Prompt Caching

Use the Anthropic native SDK and add cache_control to your system prompt:

from anthropic import Anthropic

client = Anthropic(
    api_key="your-key",
    base_url="https://your-claude-endpoint"
)

response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1000,
    system=[
        {
            "type": "text",
            "text": YOUR_LONG_SYSTEM_PROMPT,  # must be > 1024 tokens
            "cache_control": {"type": "ephemeral"}
        }
    ],
    messages=[{"role": "user", "content": user_input}]
)

# Verify cache hit in usage
print(f"cache write: {response.usage.cache_creation_input_tokens}")
print(f"cache hit:   {response.usage.cache_read_input_tokens}")

Notes:
- Cache TTL is 5 minutes — only effective for high-frequency calls
- Sonnet 4.6 minimum cacheable length: 1024 tokens
- Opus 4.7 / Haiku 4.5 minimum: 4096 tokens

Claude API Access (for users in China)

I'm using Feiyuan API (feiyuanapi.com) — a relay service running on Anthropic's official API, accessible from mainland China without VPN. OpenAI-compatible endpoint, plug directly into Dify.
Docs: https://feiyuanapi.com/docs/?utm_source=github&utm_medium=discussion&utm_campaign=feiyuan&utm_content=dify

Happy to discuss Cache optimization strategies with anyone running Claude on Dify.

Telegram group: https://t.me/feiyuanapi_group

[winchaos]

OK！

[e6o]

$35 → $4 is impressive. Prompt caching is genuinely one of the most underused cost optimization techniques.

For teams running Dify at scale, the compounding savings stack looks like:

1. Prompt caching (your approach) — 80-90% reduction on repeated system prompts. Huge for RAG pipelines.

2. Model tiering — route simpler tasks (extraction, formatting, classification) to smaller/cheaper models. In our testing, 60-70% of typical Dify workflow nodes can use a smaller model with no quality loss. This stacks on top of caching savings.

3. Caching + tiering combined — cache the expensive model's system prompt for complex tasks, and use cheap models for everything else. The $35 could potentially drop to $1-2/mo for light workloads.

The prompt caching approach you described is the right first move. Model tiering is the second move for teams that want to push further.

We track these combined savings at InferCut — cost-aware routing across models with built-in caching awareness. Works as a drop-in replacement for the OpenAI API endpoint in Dify's model configuration.