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ASP.NET Core output caching can make repeat Web API requests substantially cheaper and faster by storing a complete HTTP response and serving it without running the endpoint again. To use it safely, register the output-cache services, place the middleware after authentication and authorization, and opt only suitable GET or HEAD endpoints into a policy. Then design cache keys around every input that changes the response, choose an explicit freshness window, and provide tag-based invalidation for writes.
The framework does not publish a universal latency or throughput percentage because gains depend on endpoint cost, hit rate, storage, payload size, and deployment topology. Measure those values in your own environment rather than promising a fixed improvement.
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What ASP.NET Core output caching does
Output caching stores the generated HTTP response for an endpoint: status code, headers, and body. When a later request matches the cache key and the entry is still valid, ASP.NET Core returns that stored response instead of invoking the handler, controller action, view, database query, or downstream service again. This is different from caching an individual database query or object inside application code; the cache sits at the HTTP response boundary.
Microsoft’s middleware supports Minimal APIs, controller-based Web APIs, MVC, and Razor Pages. Registration makes the feature available, but an endpoint still needs an opt-in policy or attribute before a response is cached.
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Where output caching helps most
- Public GET endpoints that return the same representation to many callers.
- Expensive aggregations, catalog pages, documentation pages, and read-heavy reports.
- Responses backed by slow or rate-limited downstream APIs where a short period of reuse is acceptable.
- Endpoints whose freshness requirement can be expressed as a clear time-to-live or an explicit purge event.
Where it is risky
- User-specific, tenant-specific, or permission-sensitive data unless the cache key deliberately varies by every relevant identity or claim.
- Responses that change faster than the selected expiration period.
- Operations with side effects, including POST, PUT, PATCH, and DELETE; these are not cached by the default rules.
- Responses whose representation depends on an unaccounted-for header, route value, host, query parameter, or custom context.
Enable output caching in a .NET application
The minimum setup has three parts: register services, add middleware, and opt an endpoint into caching.
- Register the service. Add
AddOutputCachewhile configuring services. - Add middleware. Call
UseOutputCachein the request pipeline. - Opt in the endpoint. Use
CacheOutput()for a Minimal API route, a named policy, or the[OutputCache]attribute on a controller action.
var builder = WebApplication.CreateBuilder(args);
builder.Services.AddOutputCache();
var app = builder.Build();
app.UseOutputCache();
app.MapGet("/cached", () => Results.Ok(new { message = "cached response" }))
.CacheOutput();
app.Run();Calling only AddOutputCache and UseOutputCache does not cache every route. The endpoint must be configured for output caching. This explicit opt-in prevents an unexpected policy from applying to sensitive or highly dynamic responses.
Pipeline order with authentication and authorization
In an application that uses authentication or authorization, place output caching after both middleware components:
app.UseRouting();
app.UseAuthentication();
app.UseAuthorization();
app.UseOutputCache();
app.MapControllers();This ordering ensures the request reaches authentication and authorization before a cached representation can be served. A cached response must never bypass an authorization decision. Keep user-specific endpoints excluded unless you have designed and tested identity-aware variation.
Choose expiration policies deliberately
If a policy does not specify an expiration, the documented default is one minute. Relying on that implicit value can produce surprising staleness, so set a duration that matches the endpoint’s business requirement and state the tolerance in code review or documentation.
builder.Services.AddOutputCache(options =>
{
options.AddBasePolicy(policy =>
policy.Expire(TimeSpan.FromSeconds(10)));
options.AddPolicy("Products", policy =>
policy.Expire(TimeSpan.FromSeconds(30)));
});A base policy provides a broad default for endpoints that opt in without a more specific policy. Named policies let you give a product list, exchange-rate snapshot, or documentation page a different lifetime. The duration is a correctness decision, not merely a performance setting: a longer lifetime increases reuse while increasing the maximum age of data a client can receive.
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Apply a named policy
Minimal API endpoints select a named policy with CacheOutput("PolicyName"):
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app.MapGet("/products", GetProducts)
.CacheOutput("Products");Controller actions can select a policy with the [OutputCache] attribute:
[ApiController]
[Route("api/products")]
public sealed class ProductsController : ControllerBase
{
[HttpGet]
[OutputCache(PolicyName = "Products")]
public IActionResult Get() => Ok(_catalog.GetProducts());
}Keep policy names descriptive. A name such as Products should identify the data contract and its variation rules, not just the fact that caching is enabled.
Design cache keys so responses stay correct
The default cache key includes the request scheme, host, port, path, and query string. That is a safe starting point for many simple routes, but it can be broader than necessary or insufficient for an endpoint whose output depends on other request inputs.
Vary by only the query parameters that matter
If only the culture query parameter changes the representation, configure that parameter explicitly:
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builder.Services.AddOutputCache(options =>
{
options.AddPolicy("Culture", policy =>
policy.SetVaryByQuery("culture"));
});Narrow variation can improve the hit rate and reduce memory use because irrelevant query parameters do not create separate entries. Do not narrow it if another parameter changes the body, status, or important headers.
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Available variation controls
SetVaryByQueryvaries by selected query-string keys.SetVaryByHeadervaries by headers such as an explicitly supported representation or language header.SetVaryByRouteValuevaries by route parameters.SetVaryByHostseparates host names when the same route serves different tenants or sites.VaryByValuelets you derive a custom value for an input not covered by the standard selectors.- Case-sensitive path configuration controls whether path casing creates distinct cache entries.
Think of the key as a complete list of response-driving inputs. Under-varying can leak one representation to a request that should receive another. Over-varying is safer for correctness but lowers reuse and consumes more storage. Document why each varied input exists and test two requests that differ only in that input.
Headers, routes, and custom context
Use header variation only for headers that actually alter the response. For example, if your endpoint emits different languages based on a supported language header, include that header and normalize the accepted values in application logic. If a route value such as {version} changes the contract, vary by that route value. For a tenant resolved from a trusted host or custom request context, use host variation or a custom value and verify that tenant resolution occurs before the cache key is evaluated.
Respect the built-in safety boundaries
By default, output caching stores only HTTP 200 responses to GET or HEAD requests. Responses that set cookies and responses to authenticated requests are excluded by default. These defaults reduce the chance of caching a mutation, an error, a private session, or a user-specific representation.
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Check response behavior before opting in
- Confirm the route is safe to replay for another request with the same key.
- Confirm no cookie, session identifier, CSRF token, or per-user link is emitted.
- List every query, route, host, header, and custom value that changes the payload or status.
- Decide what a caller may observe during the expiration window.
- Verify that authorization runs before output-cache middleware.
Invalidate related entries with tags
Expiration alone is useful for time-bounded freshness, but a write operation often knows exactly when cached data became obsolete. Tags let you evict a group of entries together, such as every endpoint that exposes products.
app.MapPost("/purge/{tag}", async (IOutputCacheStore store, string tag) =>
{
await store.EvictByTagAsync(tag, default);
return Results.NoContent();
});Attach the same tag to the relevant policy or route group, then call the store after a successful update. A base policy can attach a broad tag for a route group, while a narrower tag can represent one domain area. A broad tag can also support a whole-cache eviction workflow when an operational event requires it.
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A purge endpoint is an administrative capability. Protect it with authentication, authorization, network controls, auditing, and rate limits appropriate to your environment. Do not expose an unauthenticated route that lets any caller erase hot entries or repeatedly force expensive origin work.
Prevent cache stampedes and choose storage
When an entry expires under heavy traffic, many requests can otherwise regenerate the same response at once. ASP.NET Core enables resource locking by default to mitigate this cache-stampede or thundering-herd behavior. Microsoft’s documentation states: “By default, resource locking is enabled to mitigate the risk of cache stampede and thundering herd.”
Keep the default locking behavior unless you have measured a workload where waiting requests create a worse outcome than duplicate regeneration. You can disable it deliberately:
builder.Services.AddOutputCache(options =>
{
options.AddPolicy("NoLock", policy =>
policy.SetLocking(false));
});Disabling locking can make sense for very cheap handlers or workloads where lock contention is demonstrably harmful, but it can multiply database and downstream calls during an expiry burst. Test with the same concurrency and expiry pattern your production traffic produces.
In-process versus distributed storage
In-process storage is simple and fast for a single application instance, but each instance has its own entries. A request routed to a different instance may miss even when another instance has a fresh response, and a restart clears local entries.
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| Design choice | Strength | Trade-off | Best fit |
|---|---|---|---|
| In-process output cache | Lowest access overhead and simplest setup | Entries are local to one instance and disappear on restart | Single-instance services or workloads tolerant of per-instance misses |
| Distributed output cache | Shared entries across application instances | Network, serialization, capacity, and dependency overhead | Scaled-out services that need consistent reuse across nodes |
| Time-based expiration | Easy to reason about and configure | Can serve stale data until the duration ends | Data with a known maximum staleness window |
| Tag-driven eviction | Removes related entries immediately after a known change | Requires reliable purge calls and protected operations | Catalogs, configuration, or other write-triggered domains |
Build a policy around the endpoint, not a blanket rule
A practical policy review uses six questions:
- Freshness: How old may a response be before it is unacceptable?
- Correctness: Which request inputs alter the representation, status, or important headers?
- Scope: Is the response public, tenant-scoped, authenticated, or user-specific?
- Invalidation: Is time-based expiry sufficient, or must a write trigger tag eviction?
- Concurrency: Should default resource locking protect the origin during expiry?
- Topology: Will local entries be adequate, or does the service need shared distributed storage?
For example, a public product list might use a 30-second named policy, vary by a culture query parameter, carry a product tag, and be evicted after a catalog update. A user dashboard should normally remain uncached because its output depends on identity and authorization. These are different correctness problems even if both handlers return JSON.
Verify that caching is actually working
Do not infer success from a faster local request. Validate behavior with repeatable checks:
- Send the same eligible GET twice and confirm the second request does not execute the origin work.
- Change one response-driving query, route value, host, or header and confirm it produces the correct separate representation.
- Send an authenticated request and verify that private content is not served from a public entry.
- Trigger a successful write, evict its tag, and confirm the next read reflects the update.
- Wait through expiration and observe whether one request regenerates the entry while concurrent requests avoid duplicate origin work.
- Restart one instance or route traffic to another instance to understand local-cache miss behavior.
Instrument cache hits and misses, endpoint latency, origin execution time, database and downstream call volume, cache memory or distributed-store usage, eviction counts, lock wait behavior, and the age of data returned. Compare these metrics before and after enabling a policy. The framework documentation does not establish a universal percentage reduction for latency, CPU, or throughput.
Troubleshooting common failures
Every request still runs the handler
Check that AddOutputCache is registered, UseOutputCache is in the active pipeline, and the endpoint has CacheOutput, a named policy, or [OutputCache]. Then confirm the response is a GET or HEAD with status 200 and does not set a cookie or come from an authenticated request excluded by the defaults.
Different users see the same private response
Remove output caching from the endpoint immediately, verify middleware ordering, and inspect every cache-key component. Do not attempt to repair a private endpoint by adding one guessed header; identify the complete authorization or identity boundary and either vary by all of it or keep the endpoint uncached.
Requests return the wrong language, tenant, or filter
List every input used by the handler and compare it with the policy’s variation settings. Add the missing query, header, route, host, or custom value. Also check path-casing behavior and any normalization that causes distinct inputs to collapse into one key.
Updated data remains visible
Shorten the expiration only if that staleness window is acceptable. For immediate consistency after a write, assign a shared tag to the affected read policies and call EvictByTagAsync after the write succeeds. Ensure the purge code reaches the same storage used by the readers.
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Keep resource locking enabled, inspect whether several keys expire simultaneously, and consider staggered durations or explicit invalidation for synchronized workloads. If locking was disabled, restore it and re-test under production-like concurrency.
Hit rate is unexpectedly low
Look for unnecessary query-string or header variation, multiple hosts or instances using separate local stores, very short expirations, and frequent tag evictions. Remove only variation that you can prove does not affect the response; a higher hit rate is not a success if it serves an incorrect representation.
Quick Recap
A deployment checklist
- Register output-cache services and middleware in every environment where the policy is expected.
- Place output caching after authentication and authorization.
- Opt in only endpoints that can safely replay a stored response.
- Set an explicit expiration for each important policy.
- Map all response-driving inputs into the cache key.
- Choose tags and protected purge operations for write-heavy domains.
- Keep resource locking unless measured evidence supports disabling it.
- Select local or distributed storage based on instance topology and consistency needs.
- Load-test hit, miss, expiry, purge, and concurrent-request paths.
- Monitor freshness impact as well as latency and infrastructure savings.
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