When should an ASP.NET Core application use in-memory caching versus distributed caching?

The decision between in-memory caching and distributed caching should not be based simply on which technology is faster. The more important questions are: How many application instances do you have? What data are you caching? How much consistency is required? What happens when an instance is restarted?

A cache should also be treated as a performance optimization, not the system of record. Your application should continue to behave correctly when a cache entry expires, disappears, or becomes unavailable.

1. Understand where the cached data lives

With in-memory caching, the cache exists inside the memory of the application process.

Conceptually:

User
  ↓
Application Instance
  ↓
Local Memory Cache

With distributed caching:

User
  ↓
Load Balancer
  ↓
Application Instance
  ↓
Distributed Cache
       ↑
Other Application Instances

The key architectural difference is data locality.

In-memory cache belongs to one application instance. A distributed cache can be shared by multiple instances.

2. In-memory caching can be very fast

IMemoryCache is useful when the cached data is relatively small, local to the application instance, and does not need to be shared across instances.

For example:

Product metadata
Configuration
Frequently accessed reference data
Expensive calculation results

A lookup can occur directly against process memory, avoiding a network call to a separate caching service.

However, that speed comes with an important trade-off:

The cached data exists only within that application's process.

If the application restarts, that cache is normally lost.

3. Multiple instances change the architecture

Suppose your application runs three instances:

Instance A → Cache A
Instance B → Cache B
Instance C → Cache C

The same key could exist with different values on each instance.

A request might therefore behave like:

Request 1 → Instance A → Cache Miss → Database
Request 2 → Instance B → Cache Miss → Database
Request 3 → Instance A → Cache Hit

This can be perfectly acceptable for some workloads.

But if your application requires multiple instances to see the same cached state, an instance-local cache may not be appropriate.

4. Distributed caching provides shared cache state

With a distributed cache:

Instance A ─┐
Instance B ─┼──→ Distributed Cache
Instance C ─┘

all instances can access the same cache infrastructure.

ASP.NET Core provides abstractions such as IDistributedCache for distributed caching scenarios. Microsoft documents providers including SQL Server, Postgres, and Azure Cache for Redis, depending on the application's requirements. (learn.microsoft.com)

This becomes particularly relevant when the application is horizontally scaled.

5. Deployment architecture should influence the decision

Consider a simple application:

Internet
   ↓
ASP.NET Core Application
   ↓
Database

An in-memory cache may be perfectly reasonable if the application's requirements are simple.

Now consider:

Internet
   ↓
Load Balancer
   ↓
Instance 1
Instance 2
Instance 3
Instance 4
   ↓
Database

Now you should explicitly ask:

Should cached state be shared between instances?

If yes, a distributed cache may be more appropriate.

6. Don't use distributed caching automatically

Distributed caching introduces additional infrastructure.

You now have:

Application
    ↓
Network
    ↓
Cache Service

That means additional considerations:

• Network latency
• Cache availability
• Serialization
• Connection management
• Capacity
• Monitoring
• Security
• Cost
• Failure handling

So the decision shouldn't be:

"Distributed cache is more scalable, therefore always use it."

Instead:

Choose the simplest caching architecture that satisfies the application's consistency, scalability, availability, and performance requirements.

7. Think carefully about cache consistency

Suppose the database contains:

Customer Status = Active

and the cache contains:

Customer Status = Active

The database changes:

Customer Status = Inactive

but the cached value remains:

Active

Now the application is serving stale data.

This is not necessarily a bug. Caching often deliberately trades some freshness for performance.

The important question is:

How stale can this data safely be?

For product catalog information, several minutes of staleness might be acceptable.

For a financial balance or security permission, the caching strategy may need much stronger consistency considerations.

8. Define TTL deliberately

A cache entry should normally have a defined expiration strategy.

For example:

Cache Entry
     ↓
TTL = 5 minutes
     ↓
Expires
     ↓
Next Request
     ↓
Retrieve Fresh Data
     ↓
Populate Cache

The correct TTL depends on:

Data volatility

Business impact

Database cost

Acceptable staleness

Traffic pattern

Avoid choosing five minutes simply because a tutorial used five minutes.

9. Understand cache invalidation

One of the hardest parts of caching is not storing data.

It is deciding:

When is the cached value no longer valid?

Common approaches include:

Absolute expiration

Sliding expiration

Explicit invalidation

Versioned cache keys

Event-driven invalidation

For example:

Customer Updated
      ↓
Database Updated
      ↓
Invalidate Customer Cache
      ↓
Next Request Reads Fresh Data
      ↓
Cache Rebuilt

The more frequently the underlying data changes, the more carefully cache invalidation needs to be designed.

10. Cache stampede is a real production concern

Imagine a popular cache entry expires.

At almost the same moment:

1,000 requests arrive.

If all 1,000 requests discover a cache miss and query the database, you can suddenly generate:

1,000 Requests
      ↓
1,000 Cache Misses
      ↓
1,000 Database Queries

This is sometimes called a cache stampede or thundering-herd problem.

A production architecture may need mechanisms such as:

• Request coalescing
• Locking
• Refresh-ahead
• Jittered expiration
• Background refresh
• Appropriate cache warming

The correct approach depends on the workload.

11. Don't cache everything

Caching should be selective.

Good candidates often include data that is:

Expensive to calculate

Frequently requested

Relatively stable

Safe to reuse

Poor candidates may include highly volatile data where stale information creates significant business risk.

For every cache candidate, ask:

Is it expensive?
Is it frequently accessed?
Can it tolerate staleness?
Can it be reconstructed?
What happens if the cache disappears?

12. Cache failure should not automatically mean application failure

A mature design should consider:

Application
    ↓
Cache unavailable
    ↓
Can application safely use source data?

If the cache is merely an optimization, the application may be able to fall back to the primary data source, subject to performance and availability considerations.

However, if the application treats the cache as mandatory infrastructure, then the cache becomes part of the application's availability architecture and needs appropriate resilience.

13. Don't put sensitive information into a cache casually

Caching customer, employee, authentication, or financial information introduces additional security considerations.

Ask:

• Who can access the cache?
• Is data encrypted in transit?
• Is it encrypted at rest where required?
• How long is sensitive data retained?
• Are cache keys predictable?
• Could one user retrieve another user's cached data?
• Are authorization boundaries preserved?

A cache can improve performance while simultaneously creating a data-exposure risk if the key design and access controls are wrong.

14. A practical ASP.NET Core architecture

For a scalable API, a common pattern could be:

Client
  ↓
Load Balancer
  ↓
ASP.NET Core Instances
  ↓
Distributed Cache
  ↓
Database

The request path might be:

Request
  ↓
Check Cache
  ↓
Cache Hit?
 ↙       ↘
Yes       No
 ↓         ↓
Return    Database
            ↓
        Store in Cache
            ↓
          Return

The cache therefore sits between the application and the expensive data source.

15. Interview-level scenario

Suppose an interviewer asks:

"Your ASP.NET Core application currently has one instance and uses IMemoryCache. The application is now deployed to five instances behind a load balancer. What would you consider?"

Don't immediately answer:

"Replace it with Redis."

Instead explain the reasoning:

Do all instances need shared cache data?

Can stale values be tolerated?

What happens after an instance restart?

How frequently does the underlying data change?

What is the cost of a cache miss?

What is the expected traffic volume?

What happens if the distributed cache becomes unavailable?

Does the data contain sensitive information?

Then choose the appropriate architecture based on those requirements.

For students and freshers

A useful project exercise is to build an ASP.NET Core Web API with:

API
 ↓
Database
 ↓
IMemoryCache

Measure the difference between cached and uncached requests.

Then deploy multiple application instances and investigate what happens when each instance has its own cache.

Finally, introduce a distributed cache:

API Instances
      ↓
Distributed Cache
      ↓
Database

Now compare:

Latency

Cache hit rate

Database calls

Instance behavior

Failure scenarios

This gives you a much stronger understanding than simply memorizing IMemoryCache and IDistributedCache.

For working professionals

At an enterprise level, caching becomes an architecture decision involving:

Scalability → Consistency → Availability → Cost → Security → Observability → Failure handling

You should also consider whether the caching requirement is actually a distributed caching problem, or whether a database optimization, indexing strategy, query improvement, CDN, application-level optimization, or asynchronous processing would address the underlying problem more appropriately.

Key points

The simplest way to remember the distinction is:

In-memory cache: fast, local, instance-specific, simple, but not naturally shared.

Distributed cache: shared across instances, better suited to horizontally scaled applications, but introduces additional infrastructure and operational considerations.

The right decision comes from the application's workload and consistency requirements, not from the technology being labeled as more advanced.