If your LLM bill is climbing faster than your usage, prompt caching is probably the single biggest lever you’re not pulling. In 2026, both Anthropic and OpenAI offer prompt caching that can cut input token costs by up to 90% and drop time-to-first-token by as much as 85%. For production AI systems, that’s the difference between a feature that ships and one that gets killed by unit economics.
This guide explains what prompt caching is, how the major providers implement it, and the exact patterns developers are using to squeeze the most savings out of it this year.
What Is Prompt Caching in LLMs?
Prompt caching is a server-side optimization where an LLM provider stores the internal key-value (KV) state of a previously processed prompt prefix. When you send a new request that starts with the same prefix, the model reuses that cached state instead of recomputing attention over every token from scratch.
The result is twofold: you pay a heavily discounted rate for the cached portion of the prompt, and the model skips most of the prefill work, so responses start streaming almost immediately. Anthropic reports latency improvements of up to 85% on long prompts, and both providers charge roughly one-tenth the normal input price on cache hits.
Why it matters in 2026
Modern agentic workflows send the same system prompt, tool definitions, and retrieved documents over and over. Without caching, you pay full price every turn. With caching, those stable prefixes become essentially free after the first call. At scale, teams are reporting 70–90% reductions in real monthly spend.
How Prompt Caching Works Under the Hood
When a transformer processes a prompt, it computes a key and value tensor for every token in every attention layer. These KV tensors are what the model attends to when generating output. Prompt caching saves that computed KV state to fast storage, keyed by a hash of the prompt prefix.
- Cache write: First call computes and stores the KV state. You pay a small premium (around 1.25x normal input price on Anthropic).
- Cache hit: Subsequent calls with the same prefix reuse stored state at ~10% of normal input price.
- Cache miss: Any change to the prefix invalidates the cache from that token onward.
- TTL: Anthropic supports 5-minute and 1-hour caches. OpenAI manages it automatically, typically 5–10 minutes.
Anthropic vs OpenAI: Prompt Caching Compared
Both major providers support prompt caching in 2026, but the implementations differ in meaningful ways.
Anthropic Claude
Explicit and granular. You mark cache breakpoints using cache_control on individual content blocks. This lets you cache the system prompt, tool definitions, and a long document independently. You can pick a 5-minute or 1-hour TTL per breakpoint, which is ideal for long-running agents. See the Claude prompt caching docs for implementation details.
OpenAI
Implicit and automatic. OpenAI caches any prompt prefix over roughly 1,024 tokens and routes matching requests to the cache without any code changes. You get the discount automatically but lose fine-grained control and can’t force a cache write. Details are in the OpenAI prompt caching guide.
When to Use Prompt Caching (and When Not To)
Prompt caching is a near-free win for workloads with stable, repeated prefixes. It’s a waste of effort — and sometimes a net negative — when every request is unique.
Ideal use cases
- Agentic loops: Same system prompt and tool schema across dozens of turns.
- RAG pipelines: Long retrieved documents that users ask multiple questions about.
- Coding assistants: Entire codebases or repo maps sent as context.
- Customer support bots: Large knowledge base prefixed to every conversation.
- Few-shot classifiers: Fixed example set + variable input.
Skip caching when
- Prompts are short (under 1,024 tokens on OpenAI, under 1,024 on Claude Sonnet).
- Every request has a unique prefix — the cache-write premium will cost more than it saves.
- Traffic is so sparse that the TTL expires between calls.
Best Practices to Maximize Cache Hit Rate
Getting prompt caching to actually save money takes a little discipline. The biggest mistake teams make is accidentally invalidating their own cache with tiny variations in the prefix.
- Put static content first. System prompt, tools, long docs at the top. Variable user input at the bottom.
- Never inject timestamps or request IDs into the prefix. This is the #1 silent cache killer.
- Normalize whitespace and ordering. JSON key order matters. Sort tool definitions deterministically.
- Batch similar requests. Route requests with the same prefix to the same time window to stay within the TTL.
- Monitor hit rate. Both providers return cache-hit token counts in the response. Log and alert on sudden drops.
- Use the 1-hour cache on Anthropic for workloads where a 5-minute TTL keeps expiring between calls.
A Realistic Cost Example
Imagine a customer support agent that sends a 20,000-token system prompt plus knowledge base with every query, and handles 100,000 queries a month. At standard Claude Sonnet input pricing of roughly $3 per million tokens, that’s $6,000 per month just for the repeated prefix.
With prompt caching at a 90% discount on cache hits, that same prefix drops to around $600 per month — a savings of $5,400 every month, or about $65,000 per year, on a single workload. Multiply across a product line and the ROI on a single afternoon of refactoring is enormous.
Frequently Asked Questions
Does prompt caching reduce output token costs?
No. Prompt caching only discounts input tokens on the cached prefix. Output tokens are always billed at the standard rate, since each response is unique.
Is my cached data secure and isolated from other customers?
Yes. Both Anthropic and OpenAI scope caches to your organization or API key. Another customer sending the same prompt cannot access your cache, and cache hashes are not shared across accounts.
How do I know if a cache hit occurred?
Check the usage field in the API response. Anthropic returns cache_read_input_tokens and cache_creation_input_tokens. OpenAI returns cached_tokens inside prompt_tokens_details.
Can I combine prompt caching with batch APIs for even more savings?
Yes. Stacking prompt caching (90% off input) with batch processing (50% off everything) can push total savings to around 95% on eligible workloads. This is how teams running large-scale evals or offline pipelines keep costs near zero.
Conclusion: The Cheapest Optimization You’re Not Using
Prompt caching in LLMs is the rare optimization that costs almost nothing to implement and pays back immediately. If you’re building agents, RAG systems, or any workflow that reuses long context, turning on prompt caching should be at the top of your backlog this week. Measure your current hit rate, restructure your prompts to put stable content first, and watch your bill drop.
Want more hands-on LLM guides? Check out our deep dives on RAG vs fine-tuning, reducing LLM hallucinations, and the best LLMs for coding in 2026, and subscribe to NewsifyAll for weekly AI engineering tips.
