Prompt vs semantic caching: Complementary techniques for high-performance AI agents

Large language models (LLMs) and AI agents are transforming how we interact with technology. But anyone who has built AI systems knows one hard truth: these models can be slow and expensive if they repeatedly process the same data or answer similar queries.

That’s where caching comes in. At Redis, we believe caching isn’t just a nice-to-have but it’s a critical technique for building high-performance, cost-efficient AI agents. In this post, we’ll explain two key caching approaches: prompt caching and semantic caching, and show how they can turbocharge your AI workflows.

Prompt caching: Don’t recompute the same context

Prompt caching means saving a previously processed prompt so the model can quickly reuse it for similar requests rather than performing the same computation again.

Imagine you’re building a system that summarizes a 200-page document. Each time your LLM sees it, it has to process all 200 pages before answering a single question. That’s like asking someone to read War and Peace every time you want a summary of one chapter. Prompt caching for AI agents compounds this problem for every step in the agentic flow.

Prompt caching solves this problem by storing the processed tokens for reuse. When a request comes in with the same context, the cached tokens can be used immediately, avoiding redundant computation.

Benefits of prompt caching:

• Faster responses: Reusing cached tokens avoids re-processing the same context.
• Lower costs: Compute heavy operations once, then read cheaply from cache.
• Consistency: Repeated queries with the same context produce consistent results.

Use cases: Summarizing documents, multi-turn conversations with fixed prompts, or workflows requiring repeated processing of the same large context.

Learn more:

• Anthropic: Prompt Caching
• OpenAI: Prompt Caching

Semantic caching: Cache the meaning, not just the words

Semantic caching is matching data based on semantic meaning rather than exact key-value lookups. In AI agent workflows, this allows storing past query–answer pairs and reusing them when a new query is semantically similar, avoiding another LLM call.

Sometimes two queries look different but mean the same thing. Traditional caching would treat them as separate requests. Semantic caching fixes this by storing the meaning of queries and their responses—usually via vector embeddings—and retrieving them based on similarity. For example, if one user asks “How do I reset my password?” and another later asks “I can’t log in — how do I change my password?”, semantic caching can recognize that both queries have the same meaning and return the stored answer.

Benefits of semantic caching:

• Faster response: Returning a cached answer is far quicker than getting a response from the LLM
• Cost reduction: Reduce redundant LLM calls
• Better scalability: Handle more queries simultaneously, without slowing down.

Use cases: Chatbots, customer support systems, knowledge base lookups, or RAG pipelines where users ask similar questions in different ways.

Redis LangCache: Redis’s LangCache makes semantic caching simple and performant. With LangCache, you can store embeddings, perform similarity searches, and define eviction or TTL policies—all while reducing LLM costs and speeding up response times. Check out the Redis LangCache blog and LangCache docs to get started.

Prompt caching vs semantic caching: A quick comparison

Prompt caching reuses identical prompt prefixes to cut token work, while semantic caching uses embedding-based similarity to reuse responses across meaningfully similar queries—improving cost, latency, and scalability with different TTLs, complexity, and use-case profiles.

Why not combine both? Double caching

For complex AI systems, the best approach is often double caching:

1. Prompt caching handles repeated large contexts.
2. Semantic caching handles repeated queries with similar meanings.

Example: A customer support agent analyzing a large knowledge base:

• Prompt cache avoids reprocessing the entire KB for every query.
• Semantic cache ensures that “How do I reset my password?” and “I forgot my password, what do I do?” hit the same cached response.

This hybrid approach can dramatically reduce latency, server load, and API costs.

Takeaways for AI builders

• Caching is critical: Without it, your LLMs will be slower and more expensive.
• Choose the right type: Prompt caching for fixed, large contexts; semantic caching for repeated queries. Combine them for maximum efficiency.
• Redis LangCache has you covered: LangCache provides a managed, easy-to-use platform for semantic caching that scales and speeds up your AI agents.

Don’t let your AI agents waste time and money recomputing the same context or answers. With caching, you can make them faster, cheaper, and smarter.

Learn more & get started

• Redis LangCache: Developer Docs
• Redis Semantic Caching Blog: What is Semantic Caching?