You’ve heard it a thousand times: “Cache everything!” But here’s the truth most developers learn the hard way—wrong caching decisions can silently drain your budget and cripple performance.

I’ve seen production systems where over-eager caching actually made things slower. I’ve watched server costs skyrocket because teams cached data they shouldn’t have. The problem? Most developers know that they should cache, but not what and when to cache.

Let’s fix that.

The Caching Paradox

Caching seems straightforward: store frequently accessed data in memory, reduce database hits, and watch performance soar. Right?

Not quite.

Every cached item consumes memory. Every cache check costs CPU cycles. Every stale cache entry serves outdated data. Cache the wrong things, and you’re paying twice—once for the cache infrastructure and again for the problems it creates.

What You Should Cache (And Why)

1. Computation-Heavy Results

If it takes more resources to compute than to store, cache it.

Cache this:

• Complex calculations (statistics, aggregations, reports)
• Machine learning model predictions
• Image/video processing results
• Compiled templates or rendered HTML

Why: A 5-second database aggregation query across millions of rows? Cache that result. The memory cost is negligible compared to repeatedly hammering your database.

2. Static or Slow-Changing Data

Data that rarely changes but is accessed frequently is caching gold.

Cache this:

• Configuration settings
• Product catalogs (when updated in batches)
• User profile information (not real-time activity)
• Translation strings
• API rate limit counters

Why: If your configuration is loaded on every request but only changes during deployments, you’re wasting resources. Cache it for hours.

3. External API Responses

Third-party APIs are expensive, slow, and have rate limits.

Cache this:

• Weather data (refresh every 30 minutes)
• Currency exchange rates (refresh every hour)
• Social media profile data
• Geolocation lookups

Why: External APIs often charge per request and have strict rate limits. Caching can reduce costs by 90% while improving response times dramatically.

4. Database Query Results with Predictable Access Patterns

Not all queries deserve caching, but some definitely do.

Cache this:

• Homepage content
• Popular product details
• Category listings
• User authentication tokens

Why: If 80% of your users hit the same 20 endpoints, cache those results. Your database will thank you.

What You Should NOT Cache

This is where most developers go wrong.

1. Rapidly Changing Data

Don’t cache:

• Real-time stock prices
• Live sports scores
• User session states that update constantly
• Inventory counts that change every few seconds

Why: By the time you cache it, it’s already stale. You’ll spend more resources invalidating the cache than you save. Plus, serving outdated data here creates a terrible user experience—imagine showing an item as “in stock” when it sold out minutes ago.

2. User-Specific Data That’s Rarely Reused

Don’t cache:

• Unique search queries
• One-time report generations
• Shopping cart contents for new users
• Personalized recommendation results that constantly change

Why: If data is requested once and never again, you’re just filling your cache with garbage. Cache memory is valuable—don’t waste it on data with a 0% hit rate.

3. Small, Simple Database Queries

Don’t cache:

• Single-row lookups by primary key
• Simple SELECT queries that take < 10ms
• Data already optimized with database indexes

Why: Modern databases are incredibly fast for simple queries. The overhead of checking the cache, deserializing data, and managing cache keys can actually be slower than just hitting the database. Plus, you’re adding complexity for zero benefit.

4. Sensitive Data Without Proper Security

Don’t cache:

• Credit card information
• Unencrypted passwords or tokens
• Medical records
• Personally identifiable information (PII) in shared caches

Why: Even if performance would benefit, the security risks aren’t worth it. Cached data often lives in less secure layers (like Redis without proper ACLs). One misconfiguration exposes everything.

The Real Performance Killers

Cache Stampede

When cached data expires, hundreds of concurrent requests hit your database simultaneously. Your server crashes, ironically, because you were trying to optimize performance.

Solution: Use cache warming and staggered expiration times.

Over-Caching Memory Issues

Caching too much data fills your RAM, causing the operating system to swap to disk. Now your “fast” cache is slower than your database.

Solution: Monitor cache memory usage and implement LRU (Least Recently Used) eviction policies.

Cache Invalidation Hell

Phil Karlton famously said there are only two hard things in computer science: cache invalidation and naming things. He wasn’t wrong.

Solution: Cache data with clear expiration strategies. Use time-based expiration for most cases, and event-based invalidation only when necessary.

The Cost Equation

Here’s a simple formula I use:

Cache it if:

(Computation Cost × Frequency) > (Cache Storage Cost + Cache Management Overhead)

If retrieving data from the database costs $0.0001 per query and you get 1,000 requests per second, that’s $8.64/day. But if your cache saves 80% of those queries, you save ~$7/day—and that’s just one endpoint.

Multiply that across your application, and smart caching can reduce infrastructure costs by 40-60%.

The Bottom Line

Cache intelligently, not aggressively.

Stop caching everything “just in case.” Start by identifying your actual bottlenecks through monitoring and profiling. Cache the expensive, frequently accessed, slow-changing data. Skip the fast, unique, or rapidly changing data.

Your users will get better performance. Your servers will handle more traffic. Your AWS bill will drop.

And isn’t that what we’re all here for?

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What’s your caching horror story? Share your lessons learned in the comments below.