When building applications on top of Large Language Models (LLMs), latency and API costs are often the biggest operational challenges. As the use of extensive system prompts, document analyses, and Retrieval-Augmented Generation (RAG) grows, the number of input tokens sent with every API call increases. Context caching is a technique developed to drastically reduce these repeated processing costs and wait times.
What Is Context Caching?
When an LLM processes a request, the model computes the so-called Key-Value (KV) cache for the entire input text. This process—computing the attention matrices (attention mechanism)—requires a relatively large amount of GPU compute. With traditional API calls, this computation is performed again on every interaction, even if 95% of the prompt is identical to the previous call.
Context caching makes it possible to store the already-computed KV states on the provider's servers. When a subsequent request arrives that starts with the same sequence of tokens (the prefix), the provider does not need to process this data again. The model simply resumes the computation from the point where the unique input begins.
How Does Context Caching Differ per Provider?
Although the basic principle is the same everywhere, the major AI providers use different strategies and pricing structures to support caching.
1. Anthropic (Claude)
Anthropic offers explicit Prompt Caching. Developers can mark specific blocks in the API payload with a cache_control header. This is especially well-suited for static system prompts, documents, or tool definitions.
- Minimum length: Typically 1,024 to 2,048 tokens (depending on the specific model).
- Lifetime: 5 minutes by default (Time-To-Live), with each repeated call renewing the TTL.
- Cost savings: Cached input tokens typically cost only 10% of the regular input rate. Writing to the cache costs roughly 25% extra on the first call.
2. Google (Gemini)
Google offers both automatic caching and explicitly managed context caches through the Vertex AI and Gemini APIs. You can create an explicit cache object with a fixed lifetime (for example, 1 hour or 24 hours).
- Minimum length: Often from 32,768 tokens (aimed at very large documents and videos).
- Lifetime: Configurable by the user, with storage costs per hour per gigabyte or per thousand tokens.
- Cost savings: High discounts on input tokens, but a small fee applies for keeping the cache in memory.
3. OpenAI
OpenAI uses Automatic Prompt Caching. No explicit API parameters are needed to enable caching; the infrastructure automatically recognizes when a request shares a longer common prefix with previous requests.
- Minimum length: From 1,024 tokens.
- Lifetime: Managed dynamically by OpenAI (usually a few minutes of inactivity).
- Cost savings: Automatic 50% discount on cached input tokens without additional storage costs.
When Does Context Caching Save Money?
Context caching does not provide direct savings in every scenario. The cost benefit depends on three factors: the size of the context, the frequency of reuse, and the provider's storage or write rate.
| Scenario | Caching Suitable? | Reason |
|---|---|---|
| RAG with a fixed knowledge base | Yes | A large document set that is included with every query quickly achieves a high reuse frequency. |
| Extensive system prompts / agent tools | Yes | If the instructions and JSON schemas exceed 1,000 tokens and are repeated continuously. |
| One-off long analysis | No | Without repeated calls within the TTL, the write overhead does not outweigh the benefits. |
| Short chat conversations | No | Often does not exceed the minimum threshold (e.g., 1,024 tokens). |
In projects where the total cost of ownership (TCO) of infrastructure is closely monitored, caching can reduce variable input costs by 50% to 90%. Read our article on TCO of open versus closed source models for a broader analysis of infrastructure costs.
Practical Considerations and Best Practices
To get the most out of context caching without unexpected errors or costs, developers should take the following technical aspects into account:
- Use a consistent order (prefix matching): Caching works through exact prefix matching. Make sure that static elements (system prompts, uploaded files, examples) are always at the very front of the prompt. Dynamic variables (such as the user's question or the current date) should only be added at the end.
- Monitor the hit rate: Use API telemetry to verify how many tokens are actually loaded from the cache. For detailed information on setting up monitoring and rate limits, see the API Optimization Documentation on api.llmnet.nl.
- Account for cache warmup: The very first API call that populates a cache has higher latency and sometimes a higher write rate. In production environments, it can be useful to pre-warm the cache ("pre-warming").
- Model selection and caching support: Not every model in a provider's portfolio supports caching. When making a model choice, check whether the specific version (and the chosen subdomain) offers the feature.
Conclusion
Context caching is an essential optimization step for production-ready LLM applications. By cleverly using prefix matching and the specific caching mechanisms of providers such as Anthropic, OpenAI, and Google, developers can significantly reduce both response time and the monthly API bill.
