Optimizing AI Development Costs at Scale

As engineering teams integrate Large Language Models (LLMs) more deeply into their products, the associated API costs and response times can quickly become major concerns. Every call to an AI assistant API incurs a cost and adds latency. When you’re operating at scale, with thousands or millions of users, these factors can significantly impact your budget and user experience. This is where prompt caching comes in.

Prompt caching is a set of techniques used to store and reuse the results of LLM API calls, rather than making a new request for the same or similar input. By avoiding redundant API calls, you can dramatically reduce costs, lower latency, and build faster, more efficient AI-powered applications.

Prompt caching is the practice of storing the responses to LLM prompts in a temporary, fast-access data store (a cache). Before sending a new prompt to an LLM, the application first checks if an identical or semantically similar prompt has been processed before.

If a matching prompt is found in the cache (a “cache hit”), the stored response is returned immediately, bypassing the expensive and time-consuming call to the LLM API. If no match is found (a “cache miss”), the prompt is sent to the LLM, and the new response is stored in the cache for future use.

Think of it like a barista who memorizes a regular’s coffee order. Instead of asking for the order every morning, they can prepare it immediately upon seeing the customer, saving time for everyone.

Implementing a prompt caching system involves a few key components. At its core, you need a way to generate a unique identifier (a cache key) for each prompt and a data store to hold the cached responses.

1. Cache Key Generation: The application intercepts an outgoing prompt and creates a unique key for it. The simplest method is to use a cryptographic hash (like SHA-256) of the entire prompt string. This ensures that even a tiny change in the prompt results in a different key.
2. Cache Lookup: The application checks the cache (e.g., Redis, Memcached, or a simple in-memory dictionary for local development) for an entry with this key.
3. Cache Hit or Miss:
   • On a hit, the stored response is retrieved and sent back to the user, and the process stops here.
   • On a miss, the prompt is sent to the LLM API as usual.
4. Store the Response: When the LLM API returns a response, the application stores it in the cache using the key generated in step 1 before passing it back to the user.

This basic mechanism, often called an “exact-match cache,” is effective for applications where users frequently ask the exact same questions.

While exact-match caching is a great start, more advanced techniques can deliver even better performance and cost savings, especially when prompts are dynamic or personalized.

• Prompt Templating: Many applications use templates to generate prompts, inserting dynamic data like usernames or search queries. By separating the static template from the dynamic variables, you can create more sophisticated caching logic. For example, you could cache the template’s response and then perform simple substitutions, or use the template’s structure to find semantically similar cached prompts.
• Semantic Caching: Instead of hashing the entire prompt, semantic caching uses embedding models to convert the prompt into a vector representation (a series of numbers that captures its meaning). When a new prompt comes in, the system calculates its vector and searches the cache for vectors that are “close” in meaning. This allows you to serve cached responses for questions that are phrased differently but have the same intent.
• Layered Caching: For complex applications, you can use multiple layers of caching. A fast in-memory cache can handle exact matches for a single user’s session, while a larger, shared cache (like Redis) can store results for common queries across all users. This tiered approach provides the best of both worlds: speed and high hit rates.

For development teams, a local proxy server can be a powerful tool for managing API access and implementing caching. Instead of having each developer’s machine call the LLM API directly, all requests are routed through a central proxy server on the local network.

This proxy can implement caching for the entire team, so if one developer tests a specific prompt, the response is cached for everyone else. This not only reduces redundant API calls during development and testing but also provides a central point for logging, monitoring, and managing API keys.

This setup offers several advantages:

• Cost Reduction: A single cache serves the entire team, maximizing the chances of a cache hit.
• Increased Speed: Developers get faster responses during testing, improving their workflow.
• Centralized Control: API keys, model versions, and system-level prompts can be managed in one place.
• Consistent Responses: Ensures the team is testing against a consistent set of cached outputs.

While powerful, prompt caching requires thoughtful implementation to be effective.

• Cache Invalidation: How do you know when a cached response is no longer valid? For example, if the underlying data or the LLM version has changed. Setting a Time-to-Live (TTL) on cache entries is a common best practice.
• Personalization: If responses are user-specific, the user’s ID should be part of the cache key to prevent leaking data between users.
• Semantic Drift: For semantic caching, the meaning of words can change over time. The embedding models may need to be retrained or updated periodically.
• Cost of Caching: Maintaining a cache isn’t free. The cost of running a Redis instance or other caching infrastructure must be weighed against the savings on LLM API calls.

While Kinde doesn’t offer a prompt caching service directly, it plays a crucial role in the ecosystem of an AI application where caching is implemented. Secure and effective caching often relies on knowing who the user is.

Kinde provides robust user management, authentication, and authorization, giving your application the user context needed for sophisticated caching strategies. For instance, you can use a user’s unique Kinde ID (sub claim in the JWT) as part of your cache key. This ensures that personalized AI responses are cached securely and only served to the correct user.

By combining Kinde’s user context with a caching layer, you can build personalized, efficient, and scalable AI features. Your application can confidently identify the user, retrieve their permissions and profile data, and then use that information to safely cache and retrieve AI-generated content meant specifically for them.

While there are no direct documents on prompt caching, understanding how to access user data is the first step to implementing personalized caching. You can learn more about working with user information in Kinde’s documentation:

• About authentication