How to Enable Context Caching for Lower API Call Costs: A Practical Guide

1. The Problem: Why API Call Costs Soar

In practice, developers often use third-party AI gateway aggregation platforms to call various large language models. The appeal is clear: a single unified interface grants access to multiple model providers, reducing integration complexity. Yet a common and easily overlooked issue is that context caching is not always enabled by default, so every API request is billed at the full input/output token rate — far more than expected.

Consider a real developer experience: an intelligent coding tool was configured with a premium model endpoint through an AI gateway using a mainstream-compatible API protocol. After just a few multi-turn conversations, tens of dollars in prepaid credits were quickly exhausted, with individual requests costing between $0.30 and $0.70. Even accounting for context sizes in the hundreds of thousands of tokens, the price was obviously too high. A careful comparison of official pricing against actual bills revealed the root cause: the cache-hit billing mechanism was not in effect, and all requests were charged as full-token computations.

The core issue is that different API gateways and direct-model platforms differ in their default caching behavior. Some platforms automatically detect and leverage prefix caching, while certain aggregation gateways require developers to explicitly opt in through request parameters — otherwise, the request is treated as entirely new.

2. How Context Caching Billing Works

2.1 What Is Context Caching

When a large language model processes a request, it must compute over the entire prompt. In multi-turn conversations or when the same system prompt or document prefix is reused, recomputing those repeated portions is redundant. Context caching stores already-computed prefix tokens on the server side. If a subsequent request contains the same prefix, only the newly added tokens are billed.

Typically, cached input tokens drop dramatically in price — for example, from several dollars per million tokens to several cents per million tokens — while output tokens remain unaffected and are billed at the standard rate.

2.2 Prerequisites for Cache Hits

For caching to take effect, the following generally need to be met:

• Prefix consistency: The prompt prefix in a subsequent request must be identical to the cached prefix, including spaces, formatting, and every character.
• Explicit declaration: Specific parameters in the API request must tell the server which parts should be considered for caching.
• Server-side support: The model and gateway being called must support this caching capability, and it must be enabled for the current account or configuration.

2.3 Differences in Default Behavior

Direct-model platforms typically enable caching detection by default at the SDK or API level, requiring no extra action from developers. Third-party aggregation gateways, however, must accommodate many model providers, so their default strategy tends to be conservative — caching is not automatically enabled to avoid miscomputation or billing disputes from mismatched caches. This means developers must take the initiative to configure it.

3. Troubleshooting: Confirming Whether Caching Is Active

3.1 Reverse-Engineering from Billing Data

The most straightforward check is to compare actual charges against theoretical costs:

1. Record the input token count, output token count, and actual charge for each request.
2. Using the model’s official pricing, calculate theoretical costs under both “no cache” and “cache hit” scenarios.
3. If actual charges consistently track near the no-cache figure, caching is not working.

For example, if a model’s official rate is $5 per million input tokens and $25 per million output tokens, a request with 200K input tokens and 5K output tokens would cost roughly $1.125 without caching. With a cache hit, the input portion might drop to around $0.10, for a total near $0.225 — a difference of several times.

3.2 Inspecting API Request Parameters

Look for caching-related fields in the request body. Common parameter names include cache_control, prompt_caching, or similar boolean-type fields. If the field is missing or set to false or null, caching will not be triggered.

3.3 Checking Gateway Console Settings

Log in to the AI gateway platform and look for caching options under account settings or model routing configuration. Some platforms offer cache preference settings within routing policies, but changing only the routing policy without updating the request parameters is often insufficient to activate caching.

4. The Solution: Properly Enabling Cache-Hit Billing

4.1 Explicitly Specifying Caching in Request Parameters

The most critical step is to add a cache control field to every API request, at either the message level or the request level. For a mainstream-compatible API, the structure typically looks like this:

{
  "model": "target-model-identifier",
  "messages": [
    {
      "role": "system",
      "content": "This is a long prefix that may be cached..."
    },
    {
      "role": "user",
      "content": "The specific question for this conversation"
    }
  ],
  "stream": false,
  "cache_control": {
    "type": "ephemeral"
  }
}

Here, the type in cache_control is often set to ephemeral (short-lived cache) or another type supported by the platform. Note that this field should be placed at the system message or user message level, not at the top level of the request — the exact placement depends on the API specification in use.

4.2 Maintaining Strict Prefix Consistency

Cache hits require an exact prefix match. This means:

• Every character of the system prompt must be identical to what was cached.
• Any modification between requests — even a single added or removed space — invalidates the cache.
• It is recommended to extract long, stable system prompts into a dedicated message block and set the caching parameter on that block.

4.3 Designing a Sensible Caching Strategy

Not all content benefits from caching. Practical guidelines include:

• Good candidates for caching: long documents, system instructions, role definitions, knowledge base excerpts — anything that stays the same across multiple requests.
• Poor candidates: per-conversation user inputs, dynamically generated context summaries, and other frequently changing content.
• Cache validity period: Some platforms offer a retention duration option. Choose a period that matches your usage frequency to avoid repeated billing after expiration.

4.4 Verifying That Caching Is Working

After configuration, validate with these steps:

1. Send two requests with the same prefix and check whether the second request’s input cost drops significantly.
2. Look in the gateway platform’s call logs for cache-hit indicators or status codes.
3. Run several consecutive tests to confirm that as long as the prefix remains unchanged, subsequent requests continue to receive cached pricing.

5. Broader Recommendations: General API Cost Optimization

5.1 Monitoring and Alerting

Set up cost monitoring for API calls with budget threshold alerts. When daily or per-request spending spikes abnormally, investigate promptly for cache failures or other billing anomalies.

5.2 Token Usage Auditing

Periodically review token consumption to identify requests with disproportionately high input token counts. For high-token scenarios, consider whether prefixes can be shortened or requests split to reduce per-call size.

5.3 Multi-Gateway Comparison and Routing

Where feasible, compare pricing and caching support across different AI gateways. Some gateways may offer better cache rates or more flexible configuration for the same model. Use routing policies thoughtfully to balance cost and performance.

5.4 Documentation and Team Standards

Document caching best practices in your team’s technical guides so that every developer using the API follows a consistent configuration standard, avoiding cost waste from individual misconfiguration.

6. Conclusion

When calling large language models through third-party AI gateways, context caching billing is not always on by default. Developers need to proactively configure cache control parameters in API requests, maintain prefix consistency, and adjust settings on the gateway platform. A three-step diagnostic — billing analysis, parameter inspection, and comparative testing — can quickly pinpoint the issue. Once properly enabled, API costs in multi-turn and repetitive-prefix scenarios can drop by more than an order of magnitude. This is a low-effort, high-return optimization that every developer using LLM APIs should implement.