Every LLM call costs money and time. And in a lot of cases, many of those calls are the same question asked in different words, such as “how do I reset my password,” “I forgot my password, what do I do,” and “can’t log in, need a new password.” The same answer is fit for all of these questions, but it results in three paid calls to the model because the questions are worded differently.

Semantic caching is a way to stop paying for the same answer more than once. This guide covers what semantic caching is, how it works, where it helps and where it causes problems, and a core semantic caching setting that can help you in the long run.

What Is Semantic Caching

Semantic caching is a technique that pulls stored LLM responses based on how similar two prompts are in meaning, rather than requiring an exact text match.

A usual cache looks for exact text or queries, but semantic caching looks, as its name suggests, at the semantics. As a result, answers can be reused even when users rephrase. 

How Semantic Caching Works

When a query comes in and a user sends a prompt, instead of going straight to the model, the input goes to the cache layer first. The input is then converted into a vector by an embedding model, which can be a hosted embedding API or a local model. A new vector is compared against the stored vectors of the past queries. A common approach is cosine similarity, a metric used to measure how similar two vectors are by calculating the cosine of the angle between them. This approach is combined with an approximate nearest neighbor index so searches stay fast as the cache grows.

If the closest match scores above a set threshold, the stored response is returned as opposed to the new one, and the LLM is never called. If nothing is close enough, the query calls the LLM and the new response is saved along with its embedding for future queries. If the next person asks something similar, the same response will get a hit. Most setups also give each entry in a cache an expiry date so that old or outdated data is removed automatically and the cache doesn’t grow without limit.

Benefits of Semantic Caching in LLMs

Semantic caching has a lot of benefits in large language models, including:

Cost Reduction

Every cache hit is a model call you don’t pay for. On a hit, the cost is one embedding and one lookup instead of a full model response.

Latency Gains

A cache hit returns a stored answer, so there is no wait for the model to generate one. For user-facing tools, that is the difference between an instant reply and a visible loading state.

The Warmup Period

A semantic cache starts empty. Every early query is a miss, and each miss costs slightly more than no cache at all, because you pay for the embedding and storage on top of the LLM call. The hit rate climbs only as the cache fills with common questions. This is called the warmup period. But the benefit is that you can shorten the warmup by seeding the cache before using the LLM.

When Semantic Caching Works 

Semantic cache is most commonly used in:

• Customer support chatbots that tend to receive the same set of questions again and again in different words.
• Internal knowledge-based assistants and Q&A bots that can only recall information from a limited set of data.
• E-commerce search and product recommendation queries.
• High-volume multi-agent pipelines with repetitive tool-call patterns. 

When Semantic Caching Doesn’t Work

Semantic cache does not work in systems or LLMs where:

• Every query is genuinely unique or has a unique use case.
• Responses are personalized per user.
• Real-time data is always required.
• Confidently wrong cache can become a compliance problem, especially in regulated industries.
• Query volume is low, so maintaining a semantic cache becomes a burden instead of an efficient system.

The Similarity Threshold Problem in Semantic Caching

In simple words, semantic caching looks at multiple queries that ask the same question in different words and checks if the same answer can be given to all of them. In technical words, it’s called the similarity threshold, a numerical decision boundary to determine if a new query’s meaning is close enough to a previously cached query to reuse its respons. In most cases, it works and saves you time. 

However, in some cases, the cache can have a pitfall and serve wrong answers with full confidence, thinking that the query relates to the response. For example, “how do I cancel my order” and “how do I cancel my subscription” are very closely related. In fact, in some platforms, subscription and order may even mean the same thing. But it’s not always the same thing in literal terms, and it may very well not be the same thing in your model. These questions need different answers. That’s why you can’t set your semantic caching too loose.

On the other hand, if you set it too tight, you rarely get a hit because the cache might treat each query as unique. The right way is to have a well-tuned cache layer. Many experts recommend a similarity threshold of 0.8, which results in 93% to 97% of correct cache hits. But this threshold may not work across all cases, so proper evaluation is necessary.

Where WorkflowFiesta Fits in Your LLM Cost Strategy

Semantic caching is only one piece of LLM cost control. WorkflowFiesta addresses the other major levers: model routing, token cost optimization, and agentic workflows.

Match models to specific tasks: WorkflowFiesta is model-agnostic, allowing you to assign tailored models to individual agents. Route high-volume triage to lightweight models like Claude Haiku, Gemini Flash, or local Llama via Ollama, while reserving heavy reasoning for advanced models. Switch providers seamlessly via conversation without altering your underlying agents, skills, or workflows.

Pay direct provider rates: Bring your own API keys and pay providers directly with zero markups—you only pay WorkflowFiesta for platform usage. Any token savings directly reduce your provider bill.

Eliminate redundant agent work: Embed logic directly into script skills to prevent agents from regenerating code on every run. Use prompt skills to enforce consistent behavior without repeated context in every session, minimizing token overhead.

Run high-volume tasks locally: Execute models locally with Ollama to keep sensitive data on-premises and eliminate API fees for high-frequency runs.

Gain full operational visibility: Complete audit logs track every action, requester, and state change, helping you identify repetitive queries and determine whether to apply caching, switch models, or introduce reusable skills.