Prompt Caching Compared: Pricing, TTL, and Integration Guide Across Seven AI Providers

Why Prompt Caching Suddenly Became So Popular

When model context starts at 100K tokens and up, the cost of repeatedly sending the same long prompt becomes glaringly obvious. Prompt Caching lets the server remember prefixes it has already processed, so subsequent requests can skip the redundant computation. According to a cross-provider empirical comparison, reusing a 100K-token prefix can cut costs by 71% to 93%.

What does that reduction mean? Take high-frequency use cases like customer service bots, coding assistants, or document analysis: system prompts, few-shot examples, and knowledge base snippets are often fixed and unchanging. Caching is essentially paying off these "sunk costs" once and then continuing to use them at a discounted rate.

Currently, OpenAI, Anthropic, Google Gemini, DeepSeek^Ad, Mistral, xAI, and Qwen all offer Prompt Caching capabilities in different forms. The differences mainly come down to three things: pricing discounts, cache time-to-live (TTL), and integration method.

Prefix here refers to the continuous, unchanging portion of a prompt starting from the beginning—for example, system prompts, few-shot examples, or fixed knowledge base snippets. During LLM inference, attention is computed token by token in order; once the prefix has been computed, its internal key-value pairs (KV Cache) can be reused, which is why caching typically works at the "prefix" level. This also explains why caching only works for content that is "identical from the very beginning"—once a single token in the middle changes, everything after it fails to hit the existing cache. Understanding this is crucial for designing prompt structure (put stable content first, variable content later).

Pricing Logic: Discount Depth and Billing Granularity

Anthropic bills cache writes and cache reads separately: write pricing is typically higher than regular input, while read pricing is significantly lower than regular input, creating a "pay first, save later" model. This design suits scenarios where the prefix will be hit repeatedly many times; if you only reuse it once or twice, the write cost may not be worthwhile.

OpenAI's cache discount is reflected directly on input tokens—the cached portion is automatically discounted with no need to explicitly create a cache object. According to public data, its cached input price is roughly a fraction of standard input, making it the lowest-barrier option to adopt.

Gemini offers explicit context caching (Context Caching), billed by storage duration and token volume, suited for mounting very large documents and reusing them over extended periods.

DeepSeek is known for aggressive low pricing, with cache-hit prices among the lowest of the providers—clearly attractive for cost-sensitive batch tasks. Mistral, xAI, and Qwen each offer caching or similar prefix-reuse mechanisms, with varying discount levels and trigger conditions.

It's worth noting that each provider's "cheap" is relative to a different baseline: some compare against their own standard input price, others distinguish between writes and reads. For a fair cross-comparison, you must convert everything to the same real-bill basis.

TTL Differences: How Long Caches Live

TTL (Time To Live) determines the survival window of a cached prefix and directly determines the hit rate.

Explicit caches typically have TTLs measured in minutes to hours, with some providers offering renewable options where each hit refreshes the lifetime. Implicit caches (automatic hits) often have shorter TTLs, and providers don't commit to exact values.

The practical criterion is simple: if your business requests are consistently spaced within the TTL window, the cache will almost certainly hit; if requests are sparse and irregular, even the highest discount is just a number on paper. For batch tasks, batching requests with the same prefix into a single time window will significantly boost the hit rate.

KV Cache (Key-Value Cache) is an inference acceleration mechanism used during autoregressive generation in LLMs. When a Transformer generates each new token, it needs the Key and Value vectors of all previous tokens; storing these vectors means subsequent steps don't need to recompute them, significantly reducing latency and compute cost. Prompt caching is essentially persisting the KV Cache corresponding to a given prefix on the server side, so subsequent requests with the same prefix can load it directly. Because KV Cache consumes GPU memory or storage space, servers need to set expiration times (TTL) and may charge storage fees—this is the underlying reason for differences in TTL and billing across providers.

Integration Methods: Explicit Declaration or Automatic Hits

The two integration paths are clearly distinct.

Explicit caching requires marking cacheable prefixes in requests; some providers require calling a cache-creation endpoint first, then referencing the cache ID in subsequent requests. The advantage is precise control—you can specify cache content and lifetime; the cost is an extra management step, and writes themselves have a cost.

Automatic caching has the server identify repeated prefixes on its own, so developers get discounts without changing code—ideal for existing applications already in production. The downside is that hit timing isn't fully controllable, and discount trigger conditions vary by provider.

From a migration-cost perspective, automatic caching requires almost zero changes; from a cost-optimization ceiling perspective, explicit caching combined with a sensible TTL renewal strategy wins out. Mixed use is also common: long documents go into explicit caches, while system prompts rely on automatic hits.

Hit Rate refers to the proportion of requests that actually reuse the cache, and it's the core metric for measuring cache benefit. It's affected by two factors: whether the prefix is fully identical, and whether the interval between requests falls within the TTL window. Even with a high discount rate, overall savings remain limited if the hit rate is low. Real-bill savings can be roughly understood as "the token share of hit portions × the discount depth for that portion," so improving the hit rate (e.g., standardizing prompt structure, concentrating request timing) is often more effective than chasing higher discounts.

The Hidden Variables of Geography and Compliance

Where cached data is stored and how long it's retained can be a compliance-sensitive matter in some industries. Explicit caching stores data relatively transparently, making it easier to document in data processing agreements; automatic caching's internal mechanisms disclose fewer details externally. For regulated industries like finance and healthcare, this is worth factoring into your evaluation.

Practical Recommendations

Measure first, then tune. Analyze the prefix repetition rate and request interval distribution of your production traffic, then match them against each provider's discounts and TTLs. For scenarios with highly stable prefixes and dense calls, prioritize explicit caching; for scenarios with variable prefixes and scattered calls, the zero-modification advantage of automatic caching is more practical.

Finally, run an A/B comparison on real bills. Discount rates are marketing claims; the bill is the truth.

One-line Summary

Prompt caching can cut the cost of reusing long prefixes by 71%–93%; the key to choosing a provider lies in discounts, TTL, and integration method.

Paragraph Summary

The article notes that as model context reaches the 100K-token level, the cost of repeatedly processing the same long prompt has become increasingly significant; Prompt caching skips redundant computation by having the server remember already-processed prefixes, cutting costs by 71%–93% when reusing a 100K-token prefix. OpenAI, Anthropic, Gemini, DeepSeek, Mistral, xAI, and Qwen all offer this capability, with differences concentrated in three areas: pricing discounts, cache time-to-live (TTL), and integration method. On pricing, there are two models: explicit caching (separate billing for writes and reads) and automatic caching (discounts applied directly to hit portions); TTL determines hit rate, and how well request intervals align with TTL matters more than the discount rate; on integration, explicit caching offers precise control but requires management, while automatic caching requires zero changes but leaves hit timing uncontrollable. The article recommends first measuring prefix repetition rates and request intervals, then running an A/B comparison on real bills, and reminds readers to pay attention to geography and compliance issues around cache storage.