Reduce repeated API lookup costs by first finding where duplicate work occurs, then caching only responses that are safe to reuse and coalescing identical requests that arrive at the same time. Savings depend on traffic, freshness requirements, and which part of each request is billed: a cache hit can avoid backend work while leaving gateway or provider charges intact.
Measure repeated work before choosing a cache
Start with a baseline for cost per successful lookup. Instrument the endpoint, normalized request parameters, caller or tenant scope, response variability, request concurrency, latency, and billable units. This reveals whether excess cost comes from repeated sequential lookups, simultaneous duplicates, or recurring shared context in LLM prompts.
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Track how often requests repeat and whether their responses actually change. A high request count alone does not prove caching will help: highly variable inputs, short-lived data, or a low repeat rate may produce few useful hits. Compare costs at the boundary that matters to your service, including API charges, backend compute, cache capacity, data transfer, and operational overhead.
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| Approach | What it reuses | Best fit | Important limit |
|---|---|---|---|
| Application cache | Completed responses keyed by request inputs and caller scope | Your application needs control over keys, tenancy, invalidation, and fallback behavior | You own correctness, cache operations, and invalidation logic |
| Managed API gateway cache | Endpoint responses for requests matching configured cache keys | You want response caching at the gateway and can configure the relevant request parameters | It may avoid endpoint calls without eliminating gateway request charges; caching is best-effort |
| LLM provider prompt caching | Eligible matching prompt prefixes, reducing eligible input-token costs | Requests repeatedly share a stable, sufficiently long prompt prefix | The model request still runs and generates output; this is not a completed-response cache |
Application cache
An application cache is often the most flexible option when the application knows which response dimensions vary and who is allowed to see a result. It can also support endpoint-specific freshness rules and a defined fallback when the cache is unavailable.
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Managed API gateway cache
Amazon API Gateway documents REST API response caching with keys built from method or integration parameters, including headers, URL paths, and query strings. Matching cached responses can be returned rather than calling the endpoint. AWS describes this caching as best-effort and documents CloudWatch hit and miss metrics. AWS API Gateway caching documentation.
LLM prompt-prefix cache
OpenAI prompt caching is a separate optimization: it reuses eligible matching prompt prefixes and discounts eligible input tokens, but the API request still executes and produces output. OpenAI says prompt caching is enabled by default for supported models; eligibility, minimum prompt length, controls, retention, and read/write pricing vary by model and organization policy. See the current prompt caching documentation and check current model pricing before estimating savings.
Build cache keys that preserve correctness and isolation
A cache key must capture every input that can change the response. Depending on the API, that may include normalized query arguments, locale, API or schema version, relevant headers, authorization scope, and tenant identity. In a managed gateway, verify which parameters actually participate in the key rather than assuming every request attribute is included.
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Coalesce simultaneous identical requests
Caching a completed result helps later requests. It does not by itself prevent a burst of identical requests from all reaching the backend before the first result has been stored. For that case, use request coalescing, sometimes called single-flight behavior: keep one in-flight operation per key, and have concurrent callers wait for and reuse its result.
Treat this as an application design pattern, not a universal platform recipe. The details depend on your language runtime and SDK. Account for cancellation, timeouts, and errors so one caller’s cancellation or failure does not incorrectly cancel or corrupt another caller’s work. Apply authorization checks to each caller; coalescing must not let a caller reuse a result they are not permitted to access. If later requests should benefit too, store the completed result separately under the correct cache key.
Set freshness and invalidation rules
Choose a maximum reuse window based on how quickly the underlying data changes and how much staleness the endpoint can tolerate. A time-to-live (TTL) limits reuse; where reliable change events are available, invalidate affected entries sooner. Define the acceptable stale window per endpoint rather than applying one blanket TTL to all data.
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For Amazon API Gateway REST API caching, AWS documents a default TTL of 300 seconds, a maximum of 3600 seconds, and TTL=0 to disable caching. These are AWS configuration settings, not general recommendations or performance guarantees. AWS also characterizes caching as best-effort and recommends monitoring CacheHitCount and CacheMissCount. AWS API Gateway caching documentation.
For frequently accessed information in OpenAI applications, OpenAI recommends using cached data and invalidating it when new information is added. OpenAI prompt caching documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Estimate savings at the right billing boundary
Calculate net cost per successful lookup, not just the number of backend calls avoided. Include any cache-service charge, gateway charge, origin compute, API-provider request or token charges, data transfer, and the operational cost of running the system. Measure cache hits, misses, latency, and errors so you can see whether the configuration is reducing total cost without harming correctness or reliability.
AWS says API Gateway calls count for billing whether the backend serves them or the API Gateway cache does; AWS also lists cache charges separately. A hit can therefore save endpoint work without removing the gateway request charge. Check the current AWS pricing page for your region and API type before comparing costs. Amazon API Gateway pricing; Amazon API Gateway FAQ.
For OpenAI, estimate prompt-cache savings using the eligible cached input tokens and the current model-specific rates, not an old launch-era discount. OpenAI’s 2024 announcement described a 50% cached-input discount for the models and prices named at that time; current rates and eligibility vary. OpenAI’s prompt caching announcement. No general percentage reduction in API lookup costs is established: the result depends on your repeat-request pattern, cache hit rate, and billing mix.
Quick Recap
Use a rollout checklist
- Baseline: Record repeated and concurrent duplicate requests, response variability, latency, errors, and cost per successful lookup.
- Select a layer: Choose application caching, gateway response caching, or provider prompt-prefix caching according to what is repeated and what control you need.
- Define keys and access scope: Include response-varying inputs and authorization boundaries; test that different tenants or permissions cannot collide.
- Set freshness behavior: Specify TTL, event-driven invalidation where reliable, and the acceptable stale window for each endpoint.
- Coalesce in-flight work: For concurrent identical lookups, let callers share one operation while keeping cancellation, errors, timeouts, and permissions safe.
- Monitor and compare: Review hits, misses, latency, errors, backend work, and total cost per successful lookup; adjust or remove caching where reuse is too low or staleness is unacceptable.
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