Context Caching for LLM APIs: How to Cut Repeated Input Costs by Nearly 90%

1. The Hidden Cost of Large Language Model Calls

As large language models become deeply integrated into production systems, enterprise users are discovering that the biggest budget risk is often not model inference itself, but repeated input that is easy to overlook.

In a typical conversational application, every request may include the full conversation history, system instructions, tool definitions, example interactions, and other context. If these tokens are sent repeatedly without being cached, costs can grow rapidly—even when the model’s response is very short.

Many workloads have naturally repetitive input:

• A customer service bot may resend the same knowledge base excerpts in every turn.
• A coding assistant may include the same project-level context with every request.
• A document analysis workflow may repeatedly process the same long document.

In production environments, input tokens can account for 60% to 80% of total token usage, and a significant portion of that input may be duplicated. Without an effective strategy for managing repetition, the marginal cost of an LLM application increases roughly with user volume, which can eventually undermine its commercial viability.

2. Context Caching: Stop Paying Full Price for Repeated Input

To address this problem, major cloud and model providers have introduced context caching. The basic idea is simple: when a request repeatedly uses the same prefix—such as system instructions, previous messages, or knowledge base content—the service stores and reuses that content at a lower price instead of processing it from scratch every time.

Consider a common pricing model. Suppose a model charges $3 per million input tokens, and every request includes 1,000 tokens of system instructions. Without caching, 100,000 calls would cost:

$3 × (1,000 × 100,000 / 1,000,000) = $300

With caching, the initial cache creation is charged at the regular input rate, while the remaining 99,000 requests are read from the cache. If cached input costs approximately 10% of the standard input price, the total cost becomes approximately:

$3 + $0.3 × 99,000 × 1,000 / 1,000,000
≈ $3 + $29.7
= $32.7

This represents savings of more than 89%, which is close to 90%.

Caching is also not necessarily a black box. Well-designed APIs expose detailed token usage statistics, including two useful fields:

• cache_creation_input_tokens: the number of tokens used to create a new cache during the request.
• cache_read_input_tokens: the number of tokens read from an existing cache during the request.

These metrics allow developers to measure cache effectiveness and adjust request construction accordingly. If cache_creation_input_tokens remains high, the system prompt or other prefix content may be changing too frequently for the cache to be reused. If cache_read_input_tokens accounts for a large share of input tokens, the caching strategy is likely working as intended.

3. The Economics of Cache Hits: Why Cached Input Can Cost 10%

Why can cached input cost as little as 10% of the regular input price?

From a technical perspective, tokenization and the prefill stage of inference consume a substantial amount of computation. For a repeated prefix, the service can skip repeated attention calculations and reuse the previously built KV (Key-Value) cache. This significantly reduces the required computation.

Passing part of that savings to users through a lower cached-input price creates a reasonable balance between technical efficiency and commercial pricing.

However, context caching is not a permanently free resource. Providers generally impose a cache lifetime, or TTL (time to live), which may range from five minutes to one hour. If the interval between two requests exceeds the TTL, the cache may be evicted. The next request must then create the cache again and pay the full input price.

For caching to produce meaningful savings, a workload generally needs to meet two conditions:

1. Requests must arrive within the cache TTL.
2. The request prefix must remain highly consistent.

4. Implementation: From Blind API Calls to Deliberate Cache Management

4.1 Define the Cacheable Prefix

Not every part of a request belongs in the cache. Ideal cacheable prefixes include:

• System prompts: Fixed instructions such as “You are a professional technical writer. Structure your answers clearly.”
• Tool definitions: Tool descriptions and parameter schemas that remain stable across requests.
• Long reference documents: Contracts, reports, source files, or other documents reused across multiple conversations.
• Earlier conversation history: In a multi-turn conversation, you can cache older messages while appending the newest turn dynamically.

By contrast, user messages, dynamic timestamps, randomly generated IDs, and other frequently changing values should not be placed in the cacheable prefix. Otherwise, the prefix may change on every request and the cache will be difficult to reuse.

4.2 Build Requests to Minimize Cache Fragmentation

In practice, developers should design the request structure around a stable prefix. One common approach is to separate fixed content, reusable reference material, and dynamic conversation content:

1. Combine all fixed instructions into a constant string called SYS_PROMPT.
2. Combine reusable documents or knowledge base content into DOC_CONTEXT.
3. Store conversation history on the backend and organize it by turn. The cacheable prefix can contain SYS_PROMPT, DOC_CONTEXT, and all history except the latest turn.
4. Append the latest user message separately at the end.

Example pseudocode:

# Build a stable prefix
prefix = f"{SYS_PROMPT}\n{DOC_CONTEXT}\n{history_without_last}"
# Send the request
response = client.chat.completions.create(
    model="some-model",
    messages=[{"role": "system", "content": prefix},
              {"role": "user", "content": user_turn}],
    extra_headers={
        "X-Cache-Prefix": "true"  # Hint that the provider should cache this prefix
    }
)

The exact caching mechanism depends on the provider, so developers should verify the relevant API documentation before using provider-specific headers or parameters.

Request timing also matters. When possible, group requests with identical prefixes within a short time window. For batch workloads, fixed schedules or time-windowed processing can improve cache utilization.

4.3 Use Token Metrics to Improve Cache Hit Rates

Each response may include a usage object with the cache token fields described above. It is useful to record the ratio of cache_read_input_tokens to total input tokens in the application’s logging and monitoring systems.

Teams can also configure alerts when the cache hit rate drops below a target such as 50%. Common responses include:

• If cache_creation_input_tokens is frequently high, check whether dynamic content has been included in the prefix.
• If cached content is frequently evicted, consider compressing documents or removing redundant descriptions.
• If the TTL causes frequent expiration, adjust the interval between calls or use a warm-up strategy, such as periodically sending lightweight requests to keep the cache active.

4.4 Combine Caching with Other Cost-Reduction Strategies

Context caching is only one part of a broader cost optimization strategy. To achieve an overall reduction approaching 90%, consider combining it with:

• Model routing: Use smaller, less expensive models for simple requests and reserve flagship models for complex tasks.
• Output length controls: Use parameters such as max_tokens and temperature to limit unnecessary generation.
• Batch APIs: Batch processing interfaces may provide discounts of approximately 50% when the workload can tolerate additional latency.
• Timeouts and retries: Set reasonable timeout values and retry policies to avoid duplicate charges caused by unnecessary repeated requests.
• Local caching: When appropriate, store selected previous responses on the client or application side to avoid redundant API calls.

In a typical customer service scenario, context caching combined with model routing can reduce the cost of an individual call to approximately 10% to 20% of its original level. Adding batch processing may bring the overall reduction closer to 90%, depending on the workload and provider pricing.

5. Industry Perspective: The Future of Context Caching

Context caching has become a standard capability in many LLM services, although implementation details vary between providers.

Some cloud platforms integrate caching with prompt management and support versioned prompt templates, allowing template content to be identified and cached automatically. Other platforms offer automatic caching, where the system creates caches based on request similarity without requiring developers to explicitly mark a prefix.

Caching also introduces security and privacy considerations. If cached content can be reused by other requests within a shared tenant or caching scope, sensitive user data and commercial information should not be placed in a public or shared cache. For confidential workloads, developers should use an isolated caching option when available and carefully review the provider’s data-isolation documentation.

6. Conclusion: Move from “Making API Calls” to “Making Efficient API Calls”

Optimizing LLM spending is ultimately about managing API usage with greater precision.

Context caching provides a way to lower costs without sacrificing response quality, but meaningful savings require more than simply enabling a feature. Developers need to understand the pricing model, design stable prefixes, monitor cache creation and read metrics, and combine caching with other optimization techniques.

Organizations should introduce cost dashboards early in a project and track metrics such as cache hit rate, cost per request, and total monthly spending. Over time, this helps establish a development culture in which cost awareness is built into everyday engineering decisions.

As LLMs move from experimentation to large-scale deployment, competitive advantage will depend not only on model intelligence, but also on the marginal cost of every thousand calls. Reducing that cost is a critical first step toward making AI economically sustainable.