When we talk about application performance, caching often takes center stage. It’s one of the easiest and most effective ways to reduce latency and database load. But when developers think cache, they often reach straight for Redis — a powerful, distributed in-memory data store.

While Redis is great, it’s not always the right first choice. In many applications, especially those with a single-node architecture or low concurrency, in-memory caching using Caffeine can offer massive performance gains with minimal complexity. Let’s explore why.

Why caching matters?

Every time your application fetches data from a database or makes an API call, it incurs latency. Multiply that by thousands of requests per second, and you’ve got a performance bottleneck.

Caching solves this by storing frequently accessed data in memory, allowing subsequent reads to be served in microseconds instead of milliseconds.

Why You Shouldn’t Start with Redis Right Away?

Before you spin up a Redis cluster, pause for a moment. Here are a few reasons why jumping to Redis immediately might not be the best idea:

• Added Operational Overhead

Redis runs as a separate process or service—meaning you’ll need to manage its lifecycle, monitor its memory usage, handle scaling, configure persistence, and secure it. For a simple web application, this can be overkill.

• Network Latency

Even though Redis is fast, it still involves network calls between your application and the Redis server. With Caffeine, the cache lives in the same JVM, eliminating network hops entirely.

• Unnecessary Complexity for Single-Node Systems

If your application isn’t horizontally scaled yet, you don’t need distributed caching. Local in-memory caches like Caffeine can serve requests faster and simpler.

• Cost

Redis adds infrastructure cost—either in terms of compute (if self-hosted) or cloud services (like AWS ElastiCache, Azure Cache for Redis, etc.). Caffeine is free and runs within your app process.

Enter Caffeine: The Lightweight Java Cache

Caffeine is a high-performance, near-optimal caching library for Java. It’s designed to be fast, lightweight, and easy to integrate. It’s the successor to Guava’s Cache, with improvements in speed and efficiency based on research-driven algorithms (like W-TinyLFU). If you’re using Spring Boot, integration is straightforward.

Maven dependency

<dependency>
<groupId>com.github.ben-manes.caffeine</groupId>
<artifactId>caffeine</artifactId>
</dependency>

Spring Boot Configuration

In your application.yml,

spring:
cache:
type: caffeine
caffeine:
spec: maximumSize=500,expireAfterAccess=10m

Enable caching in the main class,

@SpringBootApplication
@EnableCaching
public class DemoApplication {
public static void main(String[] args) {
SpringApplication.run(DemoApplication.class, args);
}
}

That’s all it takes to start caching in your Spring Boot app!

Fine-Tuning Your Cache

Caffeine offers more flexibility if you want programmatic control.

@Configuration
public class CacheConfig {

@Bean
public Caffeine<Object, Object> caffeineConfig() {
return Caffeine.newBuilder()
.maximumSize(1000)
.expireAfterWrite(15, TimeUnit.MINUTES)
.recordStats();
}

@Bean
public CacheManager cacheManager(Caffeine<Object, Object> caffeine) {
CaffeineCacheManager cacheManager = new CaffeineCacheManager("users", "projects", "settings");
cacheManager.setCaffeine(caffeine);
return cacheManager;
}
}

You can even inspect cache performance:

CacheStats stats = caffeineConfig().recordStats().build().stats();
System.out.println("Hit rate: " + stats.hitRate());

When Caffeine Shines?

Caffeine is perfect for:

Low to medium traffic apps running on a single node

API response or DB query caching with short TTLs

Feature flags, configuration lookups, or session-level caching

Use cases where data can be easily re-fetched if evicted

It’s lightweight, fast, and requires zero infrastructure.

When to Move to Redis?

As your application grows, you’ll eventually reach Caffeine’s limits. That’s when Redis becomes a great next step.

Switch to Redis if:

You’re running multiple instances behind a load balancer

Cached data needs to be shared across nodes

You require persistence, pub/sub, or complex data structures

Your cache size exceeds available JVM heap memory

A good caching journey looks like this:

Local Cache (Caffeine) → Distributed Cache (Redis) →
Hybrid Cache (Caffeine + Redis)

Conclusion

Redis is incredibly powerful, but you don’t need to start there. For many applications, especially early-stage or single-node systems, Caffeine offers a simpler, faster, and cheaper caching layer that can dramatically boost performance.

Start small, measure, and evolve your caching strategy as your architecture scales.

Because in software design — the simplest thing that works is often the best place to begin.

If this post helped you rethink caching or saved you from spinning up Redis a little too early, drop a ❤️ to show some love.

And if you know someone reaching for Redis by default, share this with them. Sometimes the fastest cache is the one already sitting in your JVM 😉