Anthropic Prompt Caching Cuts AI API Costs 90%

Anthropic expanded its prompt caching feature this month to support up to 5 million tokens of cached context, slashing API costs by 90% and latency by 85% for developers building context-heavy AI applications. The feature allows applications to cache large codebases, documentation, or conversation histories once, then query them hundreds of times for a fraction of the normal cost. One developer reported going from $8,000 per month to $800 by implementing caching in their RAG system.

This isn’t just optimization—it’s transforming what’s economically viable to build with AI. Developers can now create context-rich applications like full-repository code analysis or unlimited documentation Q&A without massive API bills.

The Economics: 90% Savings That Matter

Cached input tokens cost $0.30 per million tokens versus $3.00 for regular input tokens on Claude 3.5 Sonnet. For a RAG application with 100,000 tokens of cached documentation, the first request costs $375 to create the cache. However, each subsequent query costs just $30 instead of $300—a 90% reduction.

Over 12 queries, costs drop from $3,600 without caching to $705 with caching. That’s 80% total savings. The breakeven point is 1.67 cache hits, meaning the feature pays for itself after the second query. Furthermore, latency improvements are equally dramatic—average response times drop from 3.2 seconds to 0.5 seconds for large contexts.

This economic shift enables business models that were previously untenable. Developers can now offer “unlimited queries” on cached datasets without bankruptcy risk.

How Anthropic’s Prompt Caching Works

Implementation requires adding a single cache_control parameter to your API request. The first request creates the cache at a 25% premium over normal costs. Subsequently, requests within 5 minutes read from cache at 90% discount. The cache holds up to 5 million tokens for 5 minutes, expiring automatically afterward.

Here’s a minimal example:

system=[{
    "type": "text",
    "text": "Large codebase or docs here...",
    "cache_control": {"type": "ephemeral"}
}]

The response includes cache_creation_input_tokens on first request or cache_read_input_tokens on subsequent requests, confirming caching is working. No complex cache management required—the system handles everything automatically.

However, there’s a catch: cached content must be at least 1024 tokens (2048 for Claude 3.5 Sonnet). Smaller contexts can’t be cached. Additionally, the cache requires exact matching—even whitespace changes break it.

Caching Wars: Anthropic vs OpenAI vs Google

OpenAI launched automatic prompt caching in late 2024 with 50% cost reduction and 1-hour time-to-live, compared to Anthropic’s 90% savings and 5-minute TTL. Google Vertex AI offers context caching with flexible TTLs from hours to weeks using storage-based pricing. This is a pricing war, and each provider optimizes for different use cases.

Anthropic wins on cost savings but has the shortest cache lifetime. Meanwhile, OpenAI offers simplicity with automatic caching and longer sessions. Google targets very long-lived caches spanning days or weeks.

The strategic choice matters: Anthropic suits high-volume batched workloads, OpenAI fits longer interactive sessions, and Google handles persistent multi-day contexts. Your caching strategy increasingly determines which AI provider makes economic sense.

Related: Anthropic Computer Use API: AI Agents Control Your PC

The Catch: When Prompt Caching Costs More

The 5-minute TTL is too short for many workflows. If queries arrive more than 5 minutes apart, the cache expires and must be recreated at the 25% premium each time. One developer noted: “Perfect for batched workflows, annoying for sporadic queries.”

Moreover, the cache write premium means you must get at least two cache hits to break even. One-off queries actually cost more with caching enabled. The exact matching requirement creates additional pain—any change to cached content, including model version updates or parameter tweaks, invalidates the entire cache.

Caching isn’t universally beneficial. Calculate your breakeven point before implementing. Low-volume applications, one-off queries, or workflows with large time gaps between requests will see increased costs, not savings. Don’t blindly adopt caching—do the math first.

What This Enables

Prompt caching makes previously uneconomical use cases viable. Code analysis tools can cache entire repositories and answer dozens of questions for pennies. Documentation assistants cache technical docs once and serve unlimited queries within active sessions. Customer support bots cache product knowledge bases and handle high-volume queries profitably.

The feature particularly benefits agentic workflows making multiple API calls with consistent context. AI agents can cache tool definitions and system instructions across dozens of sequential requests, reducing per-call costs from dollars to cents.

However, caching works best with static, repetitive context in high-volume scenarios. Applications with unique context per request or low query volumes should skip it. The ideal use case has large static context (10K+ tokens), multiple queries within minutes, and predictable access patterns.

Key Takeaways

• Anthropic’s prompt caching delivers 90% cost reduction on cached tokens, transforming AI application economics and enabling previously unviable use cases like full codebase analysis
• Breakeven occurs at just two cache hits—worth implementing for any workflow with repeated large context within 5-minute windows
• The 5-minute TTL suits batched workflows but fails for sporadic queries; calculate your query patterns before adopting to avoid increased costs
• Competition is heating up: Anthropic offers deepest discounts (90%), OpenAI provides longer TTL (1 hour), Google targets multi-day caches—choose based on your workload characteristics
• Cache write premium and exact matching requirements mean caching can backfire; low-volume apps and one-off queries will lose money—do the math first

The caching wars signal that cost optimization is becoming as important as model quality in the AI provider competition. Consequently, developers building at scale need to factor caching strategies into their architecture decisions from day one.

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