1. Scalability: Kafka is designed for massive scalability, allowing it to handle a high volume of messages across multiple producers and consumers efficiently. On the other hand, Resque is more suitable for smaller-scale applications due to its limitations in scaling to handle large workloads.

2. Message Persistence: Kafka stores messages on disk, providing durability and fault tolerance, making it suitable for scenarios where data retention is crucial. In contrast, Resque keeps messages in memory, which can be limiting in terms of data persistence if there are failures.

3. Processing Latency: Kafka is optimized for low-latency message processing, making it a preferred choice for real-time data processing and analytics. Resque, on the other hand, may introduce higher latency due to its reliance on Redis and Ruby for message processing.

4. Message Ordering: Kafka guarantees message ordering within a partition, ensuring that messages are processed in the order they were produced. Resque does not provide this guarantee out of the box, which can be a consideration for applications where strict message sequencing is required.

5. Language Support: Kafka offers support for multiple programming languages through its APIs, enabling developers to work with a variety of languages and frameworks. In contrast, Resque is tightly coupled with Ruby, which can be a limiting factor for teams using different languages in their tech stack.

6. Built-in Monitoring: Kafka comes with built-in monitoring and management tools, making it easier to track performance metrics and troubleshoot issues. Resque lacks these integrated monitoring capabilities, requiring additional setup and configuration for monitoring and maintenance tasks.

In Summary, Kafka and Resque differ in scalability, message persistence, processing latency, message ordering, language support, and built-in monitoring capabilities.