Redis Streams vs Kafka: When to Use What

First thing: the difference is not about features

When you search this topic, you’ll mostly see feature comparisons.

That’s not very helpful when you're building something real.

The actual difference is how you treat events.

With Redis, you usually process the event and move on. With Kafka, you store the event and can use it later.

If your system doesn’t care about old events, Kafka starts feeling unnecessary.

Performance in real terms

Redis runs in memory, so it’s very fast. You push an event and it gets processed almost immediately.

Kafka writes to disk and replicates data. So yes, it’s slower than Redis, but it can handle a lot more data over time.

In simple terms:

If you want fast, real-time processing → Redis If you're building something that handles a lot of data continuously → Kafka

In most projects I’ve seen so far, Redis was already enough.

Durability (this confused me at first)

I used to think:

Redis = risky Kafka = safe

But it’s not that simple.

Redis can persist data, but it’s not meant to store a long history of events. Memory usage becomes a problem, and replaying old data is not very clean.

Kafka is built for that exact use case. It stores events and lets you read them again whenever you want.

So now I think about it like this:

Redis = process and forget Kafka = store and replay

If your events matter later, Kafka makes more sense.

Consumers (this is where Kafka feels different)

Redis Streams has consumer groups. It works well for background jobs and simple pipelines.

Kafka gives you more control.

You can replay events from any point. Multiple services can read the same data independently. You can control offsets directly.

Redis feels like a queue with some memory. Kafka feels like a log you can go back to.

This becomes important once your system grows.

Scaling (where things start getting tricky)

Redis works great at small to medium scale.

But as traffic grows, you start handling more things yourself like partitioning and rebalancing consumers.

Kafka handles this much better out of the box.

Partitioning is built in. Consumers rebalance automatically.

You don’t have to manually manage everything.

Setup and maintenance

Redis is simple. Most teams already have it.

Kafka takes more effort.

You need to manage brokers. You need proper configs. You need monitoring.

If your team is small, this adds overhead.

When I would use Redis Streams

From what I’ve seen, Redis works really well for:

• background jobs
• notifications
• chat systems
• async workflows
• service-to-service communication

Basically, when I just need to process events and move on.

When I would use Kafka

Kafka makes more sense when:

• you need event history
• multiple services depend on the same data
• you want to replay events later
• you’re building analytics or logging pipelines

Here, events are not temporary. They are part of your system.

How I decide now

I usually ask myself a few questions:

Do I need to replay events later? Will multiple services read the same data? Are these events important long-term? Do I need to store them for a long time?

If the answer is yes to more than one, I start thinking about Kafka.

Otherwise, Redis is usually enough.

One mistake I almost made

Starting with Kafka just because “we might need it later”.

That sounds smart, but in reality it just slows you down.

More setup. More complexity. No real benefit early on.

Final thought

If your events are short-lived, go with Redis. If your events need to stay and be reused, go with Kafka.

That’s how I think about it now.

Still learning, but this cleared up a lot of confusion for me.