Channels vs Message Queue for AI Processing?

Question for the community 🤔

When building a background AI processing pipeline in .NET, when do you choose System.Threading.Channels over a message queue like RabbitMQ or Azure Service Bus?

I've been using Channels (as covered in our ASP.NET Core background processing article) for in-process AI job queuing with great results. But as systems grow distributed, I keep wondering where to draw the line.

My current mental model

Use Channels when:

• Single process, single machine
• High throughput, low latency is critical (< 5ms overhead)
• You don't need persistence across restarts
• Processing thousands of small AI requests per second

Use a Message Queue when:

• Multiple services need to consume the same jobs
• You need guaranteed delivery and persistence
• Processing could take minutes (long AI jobs, batch inference)
• You want dead-letter queues and retry policies out of the box

The grey area

What about hosted services with multiple instances (e.g., 3 replicas in Kubernetes)? Each instance has its own Channel, but you need to distribute load across all three. At that point, are you better off with Redis Streams or Azure Service Bus from the start?

Have you hit the limits of Channels in production AI workloads? I'd love to hear concrete examples of when you made the switch — and whether you regretted it either way.

Drop your thoughts below! 👇