HTTP headers used by Response Caching Middleware
| Product family | ASP.NET Core |
|---|---|
| Document source | Aspnet Core Aspnetcore 10.0 |
| Guide type | Reference Guide |
| Skill level | Intermediate to advanced |
| Time | 15 - 60 minutes depending on environment |
This page documents HTTP headers used by Response Caching Middleware for engineers working with ASP.NET Core. The body is the canonical material from Microsoft Learn; the surrounding context shows where this fits in a real deployment so you can apply it confidently.
Hands-on walkthrough
Caching is the optimization people reach for after they've already screwed up the obvious things. Done right, it shaves 90% off response times. Done wrong, it serves stale data and you get a Slack message from a confused user. HTTP headers used by Response Caching Middleware is the bit of the puzzle worth getting right the first time.
I once watched a senior dev waste an entire Friday afternoon on this. The cause? A trailing slash in an OIDC redirect URI. I'm not joking.
Setup
- An ASP.NET Core 10 app.
- Decision: in-memory cache (free, single instance only), distributed cache via Redis (Azure Cache for Redis Basic C0: $16/month), or full output caching middleware (ships in ASP.NET Core 10, no extra dependency).
Walkthrough
- Decide what to cache. Read-heavy endpoints with infrequent updates. Anything per-user is harder — needs cache keys that include the user identity.
- Pick the layer. Response caching middleware for whole-response caching governed by HTTP headers. Output caching for response caching governed by attributes you control. In-memory or distributed for object-level caching inside your code.
- Register the middleware. Add
builder.Services.AddResponseCaching()orbuilder.Services.AddOutputCache(). Thenapp.UseResponseCaching()orapp.UseOutputCache()in the pipeline. - Attribute the endpoints.
[ResponseCache(Duration = 60, VaryByQueryKeys = new[] { "*" })]or, for output caching,app.MapGet("/list", () => ...).CacheOutput(). - Test the cache. Hit the endpoint twice. First should be slow, second should be fast. Watch the Age header — it tells you how long ago the cached response was stored.
Verification
- Use curl with
-von consecutive requests. Look at Cache-Control, Vary, Age, and Date headers. - Confirm the response time drops.
curl -w "@curl-format.txt"with a format file printing time_total. First hit might be 800 ms; cached hit should be under 30 ms. - Test cache invalidation. If you have a write endpoint that should invalidate, write to it, then read. Confirm you get fresh data.
- Check the Redis console (if using distributed cache). Keys should appear with the expected TTL.
Things that go wrong
- VaryBy not set correctly. Cache serves wrong response to wrong user. Authentication-sensitive responses must include the auth state in the vary key.
- Authorization caching. Response caching middleware skips authenticated requests by default. Read the docs carefully if you need to cache authenticated responses.
- Distributed cache connection failures. If Redis is down, your app shouldn't fall over. Wrap cache calls in try/catch and degrade to direct database reads.
- Stale-while-revalidate not configured. Modern browsers and CDNs support it; ASP.NET Core can emit the header. Use it for endpoints where slightly-stale is acceptable.
Rollback
Drop the caching middleware from the pipeline. Remove the attributes. Redeploy. Verify response times return to baseline. Watch downstream load, without cache, your database will see the full traffic again. Make sure it can handle it.
Test in a sandbox tenant first. I keep a $5/month Azure dev/test subscription specifically for this kind of throwaway work, and it has paid for itself a hundred times over.
Operational notes
Here's the wider context, because the surface-level steps only get you to 70% of a robust deployment.
Team and runbook. Pair this with code review checklist items. A two-line note in the PR template covers 80% of the recurring miss.
Cost watch. If your usage is spiky, consider Container Apps or Functions instead of always-on hosting. Scale-to-zero is real money saved.
Security pass. If you have a security team, loop them in before production. Even a one-paragraph summary in Slack catches issues earlier than a formal review.
Observability. Set an SLO. 99.5% of requests under 300 ms. 99.9% under 1 second. Pick numbers, write them down, monitor them. Without an SLO, 'fast enough' is whatever feels right to whoever complained last.
Patterns I keep coming back to
Across many projects in ASP.NET Core, a few patterns repeat that are worth naming explicitly.
- The "smoke test on every deploy" pattern. A 10-line script that hits 5 endpoints, checks response code and latency, exits non-zero on failure. Run it as the last step of every CD pipeline. Catches 70% of post-deploy regressions before they reach a user.
- The "feature flag everything risky" pattern. Microsoft.FeatureManagement.AspNetCore (free, ships with the framework). Wrap any change that could blow up behind a flag, default off in production, flip the flag after the change is verified.
- The "twin environment" pattern. A staging environment that is byte-for-byte identical to production in configuration, with a fraction of the data. Costs maybe 20% extra on Azure if you size sensibly. Catches roughly 50% of "works locally, fails in prod" bugs.
- The "single source of secrets" pattern. Azure Key Vault or HashiCorp Vault. Not a .env file. Not appsettings.Production.json with values committed. Real secret storage with rotation policies.
Decisions worth revisiting periodically
Some choices made early in a project age fast. I keep a list of "look at this every six months" items per project. ASP.NET Core-flavored versions look like this:
- SDK and framework version. Are you on the current LTS? Microsoft's support window is fixed; falling behind two majors means you're paying interest in security patches.
- Dependency surface. Run
dotnet list package --outdatedquarterly. Each outdated package is a small risk; cumulatively, large risk. - Build time. If your CI builds creep past 8-10 minutes, the developer feedback loop slows enough to hurt productivity. Profile and trim.
- Test coverage and execution time. Coverage that climbs slowly is fine. Test suites that climb slowly toward 30 minutes is a warning sign. refactor or parallelize.
- Production error budget. If you're hitting your error budget consistently, slow feature work and pay down operational debt. If you're under-using your error budget, you're shipping too slowly.
Why I do it this way
Years ago I treated each topic in isolation. I'd read the docs, implement the feature, ship it, move on. The result was a lot of features that worked individually and didn't compose well. Today I default to: read the docs, sketch a diagram on paper or a digital whiteboard, identify the trust boundary, identify the failure mode, then implement. Ten extra minutes up front, hours saved down the road.
The other shift was treating tests as the design tool, not the verification tool. Writing the test first forces you to think about the API surface from the caller's perspective. By the time the test compiles, the design has been pressure-tested by your own most demanding consumer: a test that has to drive the feature without knowing the implementation.
Practical example: last month I rewrote a small reporting endpoint. The first draft was a single 60-line action method that pulled data, transformed it, and returned JSON. Tests forced me to split it into an IReportRepository, an IReportFormatter, and a thin controller. Same functionality, but now I can swap the formatter for HTML output by writing one new class and zero changes to the controller. That's the power of writing the test first.
References worth bookmarking
- Microsoft Learn, the canonical source. Whatever version of this page exists at learn.microsoft.com beats my summary.
- The ASP.NET Core GitHub repo's issues and discussions. Real bugs and real solutions, sometimes faster than the docs catch up.
- The .NET API browser at learn.microsoft.com/en-us/dotnet/api: perfect for "what does this method actually return when X."
- The official .NET blog at devblogs.microsoft.com/dotnet, explains the "why" behind changes that the API docs leave implicit.
How to apply this in practice
- Treat this as a starting point. Your tenant, SKU, region, and licence level all change the surface area in small but real ways.
- Run the procedure in a non-production environment first. A staging slot, a dev tenant, a sandboxed subscription. pick one and use it.
- Pin the version you implement against. When you commit to a design choice based on this page, write the date and the exact ASP.NET Core version into your ADR (architecture decision record).
- Cross-check the current Microsoft Learn page before rolling production. The product team updates docs often; what's true today may shift in two months.
Caveats and what to double-check
- ASP.NET Core terminology drifts. The same concept can have two or three names across docs cohorts written in different years.
- Some features in this area may still be in preview, confirm GA status before relying on it for production SLAs.
- Regional availability varies. A feature documented as "global" may actually roll out region by region across months.
- Pricing for ASP.NET Core-related services changes regularly. This page does not track pricing. Use the official Microsoft pricing calculator for the latest numbers.
Related work in your environment
- Document this reference in your team wiki along with which workloads currently depend on it.
- Set up a Microsoft Learn RSS feed or doc-change alert for the source page so your team is notified when Microsoft updates the canonical version.
- Add a periodic review to your governance cadence. Quarterly is a sensible default for ASP.NET Core.
FAQ
References
- Microsoft Learn - official documentation for ASP.NET Core
- Microsoft tech community forums and Q&A
- Azure / Microsoft 365 service health dashboards
Related fixes
Related guides worth a look while you sort this one out: