Anthropic's Prompt Caching: The Feature Nobody Uses (But Should)

In August 2024, Anthropic added a feature that can reduce your API costs by 90% for certain use cases.

It's called Prompt Caching, and almost nobody uses it.

Why? Because Anthropic barely marketed it, the documentation is buried, and most developers don't understand when it applies.

Let me fix that.

What Is Prompt Caching?

Prompt caching lets you reuse parts of your prompt across multiple requests without paying for those tokens again.

Think of it like this: if you're sending the same system prompt, documentation, or context repeatedly, you only pay for it once. Subsequent requests reuse the cached version at a 90% discount.

The Economics

Normal pricing (Claude 3.5 Sonnet):

• Input: $3 per 1M tokens
• Output: $15 per 1M tokens

Cached input pricing:

• Cache write: $3.75 per 1M tokens (25% premium)
• Cache read: $0.30 per 1M tokens (90% discount)

If you're making multiple requests with the same context, the savings are massive.

Real-World Example

Let's say you're building a code review bot. Each review includes:

• System prompt: 500 tokens
• Coding guidelines: 2,000 tokens
• Code to review: 1,000 tokens (varies)

Without caching (100 reviews):

• Total input: 350,000 tokens
• Cost: $1.05

With caching (100 reviews):

• First request: 3,500 tokens @ $3/M = $0.0105
• Next 99 requests:
  • Cached: 2,500 tokens @ $0.30/M = $0.074
  • New: 99,000 tokens @ $3/M = $0.297
• Total: $0.38

Savings: 64% for just 100 requests.

At scale (10,000 reviews), you save $85 per 10K requests.

When Prompt Caching Shines

Perfect for:

1. Code Review Bots

• System prompt + guidelines stay constant
• Only the code changes
• Savings: 60-80%

2. Customer Support

• Company knowledge base cached
• Only customer query changes
• Savings: 70-90%

3. Document Analysis

• Large document cached
• Multiple questions about it
• Savings: 85-95%

4. RAG Applications

• Retrieved context cached
• User query changes
• Savings: 50-70%

For a complete breakdown of RAG costs and optimization strategies, see our RAG pipeline cost guide.

5. Multi-turn Conversations

• Conversation history cached
• Only new message added
• Savings: 40-60%

The Implementation Challenge

Anthropic's caching requires manual configuration:

• Identifying which content to cache
• Adding cache control markers
• Managing cache lifetimes
• Tracking cache hit rates
• Calculating actual savings

For most teams, this complexity means they never implement it—leaving massive savings on the table.

The Easier Way

CostLens handles prompt caching automatically:

import { CostLens } from 'costlens';

const client = new CostLens({
  apiKey: process.env.COSTLENS_API_KEY,
  enableCache: true,
});

// CostLens automatically identifies and caches repetitive content
const resp client.chat({
  messages: [
    { role: 'system', content: 'You are a code review assistant...' },
    { role: 'user', content: 'Review this code: ...' }
  ],
});

No manual cache markers. No configuration. Just automatic savings.

Cache Lifetime

Caches last for 5 minutes of inactivity.

If you make another request within 5 minutes, the cache is reused. After 5 minutes, it expires and you pay the write cost again.

For high-traffic applications, this means near-constant cache hits.

Real Customer Results

SaaS Company (Document Analysis):

• 50K documents/month
• Average 10 questions per document
• Before: $4,200/month
• After: $680/month
• Savings: $42,240 annually

Code Review Platform:

• 100K reviews/month
• Before: $8,500/month
• After: $2,100/month
• Savings: $76,800 annually

Customer Support Bot:

• 200K conversations/month
• Before: $12,000/month
• After: $3,600/month
• Savings: $100,800 annually

Why Manual Implementation Fails

1. Identifying What to Cache
Most developers cache the wrong content—either too much (paying premiums for no benefit) or too little (missing savings).

2. Cache Ordering Requirements
Anthropic requires cached content at the start of prompts. Get the order wrong, and caching breaks silently.

3. Managing the 5-Minute Window
Cache expires after 5 minutes of inactivity. Batch jobs need careful timing to maximize hits.

4. Tracking ROI
Without proper monitoring, you don't know if caching is actually saving money.

CostLens solves all of these automatically with intelligent caching algorithms that learn from your usage patterns.

Monitoring Cache Performance

Manually tracking cache performance requires:

• Parsing usage metadata from each response
• Calculating cost savings per request
• Aggregating metrics across thousands of calls
• Building dashboards to visualize trends

CostLens does this automatically. The dashboard shows:

• Cache hit rate (real-time)
• Cost savings from caching
• Which prompts benefit most
• Optimization recommendations

You see exactly how much you're saving without writing monitoring code.

The Bottom Line

If you're using Claude for:

• Repetitive tasks
• Document analysis
• Code review
• Customer support
• RAG applications

And you're NOT using prompt caching, you're overpaying by 60-90%.

It takes 5 minutes to implement and can save thousands monthly.

Start tracking your cache hit rates today. You'll wonder why you waited.