The Tool Desk
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What each cache reuses—and what it can save
| Decision point | Prompt caching | Semantic caching |
|---|---|---|
| Reuse condition | An eligible prompt prefix matches according to the provider’s rules. | A new query meets a similarity threshold and any required metadata or eligibility rules. |
| What a hit avoids | Reprocessing or billing for cached prefix tokens; the model call still happens. | Potentially the full generation call; a miss still requires one. |
| Costs to count | Cache reads and writes under model-specific pricing, plus eligible tokens that miss or are not reused. | Embeddings, lookup, storage and serving, any validation, and model calls for misses. |
| Primary quality concern | Whether the prefix is eligible and its context is still appropriate; the model still generates a response. | Whether a similar-looking query is sufficiently equivalent to receive the stored answer. |
| Natural starting workload | Repeated long, stable instructions or context followed by changing input. | Repeated questions with answers that remain valid during the cache lifetime. |
For prompt caching, the reusable material is part of the request sent to the model; wording that is merely similar does not establish a prefix-cache hit. For semantic caching, a system typically stores a query and its generated response, then uses vector similarity and metadata to decide whether to return that response to a later query. That is different from retrieval-augmented generation, where retrieved source material grounds a new model response rather than substituting an old answer. See OpenAI’s prompt-caching guide and Redis’s semantic-cache documentation.
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How to calculate prompt-caching break-even
A useful starting calculation comes from OpenAI’s simplified prefix-caching example. Let M be the minimum cacheable prefix length in tokens, L the original shorter prefix length, r the cache-read cost multiplier, w the cache-write multiplier, and N the number of requests that reuse the prefix. If you expand the prefix to exactly M, write it once, and reuse it on every later request, its cost in uncached-token equivalents is M × [w + (N − 1)r]. Leaving the original prefix uncached costs N × L.
Under those assumptions, expanding the prefix is cheaper when the original prefix is longer than the crossover value:
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Break-even original length: M × [r + (w − r) / N]
OpenAI’s stated example uses M = 1,024 tokens, r = 0.1, and w = 1.25. The crossover is 102.4 + 1,177.6 / N tokens. With ten requests, an original prefix of at least 221 tokens is cheaper to expand to 1,024 under this cost-only model. A 103-token prefix needs at least 1,963 requests; a prefix of 102 tokens or less never reaches break-even under these assumptions. These are calculations from the documentation’s illustrative inputs, not general thresholds. The example excludes performance, output tokens, and request costs that do not change; different reuse, writes, misses, model prices, or traffic mixes change the result. See OpenAI’s formula and example.
How to find semantic-caching break-even
There is no single semantic-cache formula in the cited documentation that applies across implementations. Compare the full cost of the same traffic with and without the semantic layer. Count embeddings and similarity lookups, storage and serving, optional validation, and the model calls actually avoided after misses and rejected matches. Include costs that continue on a hit, not just the model expense that may disappear.
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How to measure both options on real traffic
- Build a representative replay. Use a privacy-appropriate sample with realistic request ordering, cadence, and concurrency. Those details affect both repeated prefixes and repeated semantic queries.
- Establish a no-cache baseline. Replay the same workload without the candidate layer, then run each cache configuration against that workload. If testing semantic thresholds, compare multiple settings rather than selecting one from a vendor chart.
- Record the full cost and performance picture. Capture realized model spend; cache reads, writes, misses, and eligible tokens; embedding and lookup expense; infrastructure cost; and end-to-end latency.
- Measure quality alongside savings. For semantic hits, assess whether the stored answer is correct for the new query, especially when entities, dates, constraints, or user context differ.
- Segment the results. Break them out by workload, tenant, locale, model or version, request type, and time sensitivity. For prompt caching, OpenAI recommends tracking cached tokens, cache-write tokens, input tokens, latency, and realized cost. Where token usage is available, calculate token cache-hit rate as cached tokens divided by total input tokens. A request-level hit rate answers a different question from a token-level rate.
- Test operational settings. Compare semantic thresholds and TTLs, and note uncertainty where the sample is small. Keep a vendor’s benchmark distinct from measurements of your own workload.
OpenAI’s guide describes the usage and cost measures for prompt caching. AWS’s semantic-caching best practices recommend threshold A/B tests and accuracy monitoring.
What a published semantic-cache benchmark shows
AWS evaluated 63,796 chatbot queries and paraphrased variants from the public SemBenchmarkLmArena dataset. The test used an ElastiCache cache.r7g.large store, Amazon Titan Text Embeddings V2, and Claude 3 Haiku; AWS streamed queries into an initially empty cache in random order. The figures below are AWS’s reported results for that test, not a forecast for another workload. The page was accessed in 2026. AWS benchmark details.
| Configuration | Cache hit ratio | Cached-response accuracy | Reported daily cost | Average latency |
|---|---|---|---|---|
| No cache (baseline) | — | — | $49.50 | 4.35 seconds |
| Threshold 0.95 | 56.0% | 92.6% | $23.80 | 1.84 seconds |
| Threshold 0.90 | 74.5% | 92.3% | $13.60 | 1.21 seconds |
| Threshold 0.80 | 87.6% | 91.8% | $7.60 | 0.60 seconds |
| Threshold 0.75 | 90.3% | 91.2% | $6.80 | 0.51 seconds |
| Threshold 0.50 | 94.3% | 87.5% | $5.90 | 0.46 seconds |
In this benchmark, lowering the threshold raised the hit ratio while cached-response accuracy declined. AWS reports up to 86.3% cost savings at threshold 0.75 in this specific test. The results illustrate why hit rate cannot stand in for quality or for a workload-specific cost calculation.
Which workloads suit each approach?
Use prompt caching for stable prefixes
Prompt caching is a natural fit when requests repeatedly include the same long system instructions, tool definitions, or reference documents before a changing user input. Put stable content before dynamic content so the prefix can be reused. Eligibility, minimum token lengths, TTLs, and charges vary by provider, model, API, and platform; verify the rules that apply to the exact configuration you use. See OpenAI’s documentation, Anthropic’s prompt-caching documentation, and Amazon Bedrock’s prompt-caching guide.
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Use semantic caching for repeatable, stable answers
Frequently repeated FAQ or support questions can be candidates when their answers remain valid for the cache lifetime. Real-time or highly dynamic answers are poor candidates unless freshness is handled explicitly. Set metadata boundaries—such as tenant, locale, product, category, or user segment—where those differences can change what answer is valid. For multi-turn conversations, AWS recommends representing the current turn together with relevant retrieved context instead of embedding the entire raw dialogue. See AWS’s best-practice guidance.
Thresholds, freshness, and provider-specific misses
Choose a semantic threshold for acceptable errors, not maximum hits
A lower similarity threshold can admit more matches, but it also makes it easier to reuse an answer for a query with a meaningful difference. AWS recommends starting conservatively, then lowering the threshold while monitoring accuracy; its generic threshold examples are guidance, not guaranteed production results. Use test cases that vary entities, dates, constraints, and context, and review hit quality as well as aggregate accuracy. AWS’s guidance covers threshold tuning and monitoring.
Set TTL to the answer’s useful life
The cache lifetime should reflect how quickly the underlying answer can change. AWS gives illustrative TTL guidance of 5–15 minutes for real-time prices or inventory and 24 hours for static documentation or policies, while advising teams to tune TTL to the application. Redis also documents TTL and eviction controls. These examples are starting points, not universal settings. See AWS’s best practices and Redis semantic cache documentation.
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Account for prompt-cache eligibility and timing
A prompt-cache request can miss even when a provider supports caching: eligible minimum lengths and match rules differ, and a supported request is not a guaranteed hit. Amazon Bedrock describes implicit best-effort reuse and explicit breakpoints, and notes that billing for successful reads and writes depends on the model. Anthropic documents a default five-minute ephemeral cache on its API and notes that an entry becomes available after the first response begins, which matters when parallel requests arrive. Verify the model, platform, region, API, minimum token count, TTL, and current price before forecasting savings. See Amazon Bedrock’s guide and Anthropic’s documentation.
Quick Recap
A practical decision rule
- Start with prompt caching when a long, stable prefix repeats and you want to reduce the cost of processing it while still generating a fresh model response.
- Evaluate semantic caching when the same stable questions recur and a validated prior answer can safely replace a new generation.
- Compare both against no cache on the same representative traffic; choose based on net cost, end-to-end latency, and task-specific correctness—not a nominal discount or hit ratio alone.
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