LLM prompt caching: How to cut API costs 40–90% with prefix caching

How KV cache reuse eliminates redundant compute

Every LLM API call processes input tokens through the attention mechanism, building a Key-Value (KV) cache that represents each token's relationship to every other token in the context. For a 10,000-token system prompt, the GPU must compute this cache from scratch on every call — even when the system prompt is identical across 100,000 requests.

Prompt caching solves this by persisting the KV cache for repeated input prefixes. When your next request begins with an identical prefix, the provider serves the cached KV representation rather than recomputing it, charging a fraction of the standard input rate.

For the full AI cost optimization landscape, see our sovereign tech and AI computing cost framework.

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Provider-specific caching implementations

Anthropic cache writes: Writing a new cache entry costs $3.75 per 1M tokens (25% above standard). This write cost is amortized over subsequent reads.

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Break-even analysis for cache investment

Scenario: 8,000-token system prompt, 200-token user query, 400-token response.

Anthropic Claude 3.5 — No caching:

• Per call cost: (8,200 × $3.00 + 400 × $15.00) / 1M = $0.0306/call

With caching (after first cache write):

• Cache write (first call): (8,000 × $3.75 + 200 × $3.00 + 400 × $15.00) / 1M = $0.0366
• Subsequent calls: (8,000 × $0.30 + 200 × $3.00 + 400 × $15.00) / 1M = $0.00900/call

Savings per call after breakeven: $0.0216 (70.6% reduction)

Cache write pays for itself after: $0.0366 / $0.0216 = 1.69 calls — essentially the very first reuse is profitable.

At 100,000 calls/month: $306/month without caching → $900/month first call + $89,999 × $0.009 = $900 total → $906/month ← Wait, recalculate clean:

Without caching: 100,000 × $0.0306 = $3,060/month With caching (1 write + 99,999 reads): $0.0366 + (99,999 × $0.009) = $900.03/month Monthly savings: $2,159.97 (70.6% reduction)

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Implementation requirements for effective caching

1. Stable prefix structure: The system prompt must appear at the beginning of the messages array, before any dynamic content. Any modification to the cached prefix — even a single character — invalidates the cache entry.

2. Freshness window: Anthropic caches expire after 5 minutes without use. Ensure your application traffic rate is sufficient to keep the cache warm between requests.

3. Explicit cache_control markers (Anthropic): You must mark prefix boundaries with "cache_control": {"type": "ephemeral"} in your API request. Without this, caching is not applied.

4. Temperature and sampling parameters: Caching applies to the input side only — sampled outputs remain stochastic. Cached prefixes do not affect output quality or determinism.

Use the LLM Token Cost Calculator to model your system prompt length and call volume to calculate your specific monthly caching savings.