Prompt caching with Azure OpenAI in Microsoft Foundry Models

Learn how to use prompt caching with Azure OpenAI

Prompt caching reduces overall request latency and cost for longer prompts that have identical content at the beginning of the prompt. In this context, “prompt” refers to the input you send to the model as part of your chat completions or response creation requests. Rather than reprocessing the same input tokens over and over again, the service retains a temporary cache of processed input token computations to improve overall performance. Prompt caching has no impact on the output content returned in the model response beyond a reduction in latency and cost.

For supported models, cache reads are billed at a discount on input token pricing for Standard deployment types and up to 100% discount on input tokens for Provisioned deployment types. Prompt cache pricing is the same for both retention policies.

Models before gpt-5.6 don’t charge extra to write to the cache. On gpt-5.6 and later models, cache writes are billed in addition to discounted cache reads. To keep costs predictable, structure your prompts so that reused content stays identical across requests, which favors cache reads over cache writes. For current rates, see the Azure OpenAI pricing page.

Prompt cache retention

Prompt caching can use either in-memory or extended retention policies. When available, extended prompt caching aims to retain the cache for longer, so that subsequent requests are more likely to match the cache.

To configure the prompt cache retention policy, set the prompt_cache_retention parameter on the Responses or Chat Completions API.

In-memory prompt cache retention

The system typically clears caches within 5 to 10 minutes of inactivity and always removes them within one hour of the cache’s last use. The system doesn’t share prompt caches between Azure subscriptions.

All Azure OpenAI models GPT-4o or newer support in-memory prompt cache retention. It applies to models that have chat-completion, completion, responses, or real-time operations. For models that don’t have these operations, this feature isn’t available.

Extended prompt cache retention

Extended prompt cache retention keeps cached prefixes active for longer, up to a maximum of 24 hours. Extended prompt caching works by offloading the key/value tensors to GPU-local storage when memory is full, which significantly increases the storage capacity available for caching.

Extended prompt cache retention is available for the following models:

• gpt-5.4
• gpt-5.3-codex
• gpt-5.2
• gpt-5.1-codex-max
• gpt-5.1
• gpt-5.1-codex
• gpt-5.1-codex-mini
• gpt-5.1-chat
• gpt-5
• gpt-5-codex
• gpt-4.1

Configure per request

For gpt-5.4 and older models, if you don’t specify a retention policy, the default is in_memory. Allowed values are in_memory and 24h. For all newer models, the default is 24h and in_memory isn’t supported.

{
  "model": "gpt-5.4",
  "input": "Your prompt goes here...",
  "prompt_cache_retention": "24h"
}

Getting started

To take advantage of prompt caching, a request must meet both of these requirements:

• A minimum of 1,024 tokens in length.
• The first 1,024 tokens in the prompt must be identical.

Requests are routed based on a hash of the initial prefix of a prompt. The hash typically uses the first 256 tokens, though the exact length varies depending on the model.

When a match is found between the token computations in a prompt and the current content of the prompt cache, it’s referred to as a cache hit. Cache hits show up as cached_tokens under prompt_tokens_details in the chat completions response.

For gpt-5.6 models, the usage response doesn’t report cache writes separately. Use cached_tokens to monitor cache reads, and use the Azure OpenAI pricing page to understand how reads and writes affect your costs.

{
  "created": 1729227448,
  "model": "o1-2024-12-17",
  "object": "chat.completion",
  "service_tier": null,
  "system_fingerprint": "fp_50cdd5dc04",
  "usage": {
    "completion_tokens": 1518,
    "prompt_tokens": 1566,
    "total_tokens": 3084,
    "completion_tokens_details": {
      "audio_tokens": null,
      "reasoning_tokens": 576
    },
    "prompt_tokens_details": {
      "audio_tokens": null,
      "cached_tokens": 1408
    }
  }
}

After the first 1,024 tokens, cache hits occur for every 128 additional identical tokens.

A single character difference in the first 1,024 tokens results in a cache miss, which is characterized by a cached_tokens value of 0. Prompt caching is enabled by default for supported models.

For gpt-5.6 models, set the prompt_cache_key parameter and reuse the same key for requests that share long, common prompt prefixes. The parameter combines with the prefix hash to improve cache matching. You don’t need a specific API version to use prompt_cache_key. For new integrations, use the v1 API.

If requests for the same prefix and prompt_cache_key combination exceed approximately 15 requests per minute, some requests might miss the cache. For higher-volume workloads, distribute requests across multiple keys while keeping a stable mapping between each key and its shared prompt prefixes.

Azure OpenAI doesn’t currently support prompt_cache_options or prompt_cache_breakpoint. Prompt caching remains automatic, so use prompt_cache_key and a stable prompt structure to improve cache matching and manage costs.

Manage prompt caching costs

On gpt-5.6 and later models, you pay for cache writes, so control how often your prompts write to the cache. Cache reads stay discounted, so structure your requests to favor reads over writes.

To reduce cache-write costs:

• Place stable, reusable content, like system instructions and examples, at the start of the prompt, and put variable content at the end.
• Keep the first tokens of your prompt identical across requests. A single change in the cached prefix forces a new cache write.
• Send requests that share the same prefix at a steady rate, so that earlier cache writes become later cache reads instead of repeated writes.

Frequently asked questions

What is cached?

Feature support for o1-series models varies by model. For more information, see the dedicated reasoning models guide.

Prompt caching supports:

Caching supported	Description
Messages	The complete messages array: system, developer, user, and assistant content
Images	Images included in user messages, both as links or as base64-encoded data. The detail parameter must be set the same across requests.
Tool use	Both the messages array and tool definitions.
Structured outputs	Structured output schema is appended as a prefix to the system message.

To improve the likelihood of cache hits, structure your requests so that repetitive content occurs at the beginning of the messages array.

Can I disable prompt caching?

Prompt caching is enabled by default for all supported models. There’s no opt-out support for prompt caching.

Do I pay extra to write to the cache?

On models before gpt-5.6, there’s no extra charge to write to the cache. On gpt-5.6 and later models, cache writes are billed in addition to discounted cache reads. To see current rates, go to the Azure OpenAI pricing page.

Does prompt caching work with data residency?

In-memory prompt caching is compatible with all data residency regions. Extended prompt caching temporarily stores data on GPU machines. Data stays within the data zone boundary for Data Zone Standard and Data Zone Provisioned deployment types, and within the regional boundary for Regional Standard and Regional Provisioned deployment types.