Azure Cosmos DB and Kafka are both platforms used for managing and processing data. However, they have significant differences in terms of their architecture, data model, scalability, messaging capabilities, data consistency, and use cases.

1. Architecture: Azure Cosmos DB is a globally distributed, multi-model database service with built-in high availability and automatic scaling. It provides transparent multi-region replication and supports multiple consistency models. On the other hand, Kafka is a distributed streaming platform that is designed for high-throughput, fault-tolerant, and real-time data streaming. It follows a distributed pub-sub architecture and relies on a cluster of brokers for message storage and processing.

2. Data Model: Azure Cosmos DB supports multiple data models, including document, key-value, graph, and columnar. It allows developers to choose the most suitable data model for their application. In contrast, Kafka does not define a specific data model. Instead, it provides a publish-subscribe mechanism where producers can publish messages to topics and consumers can subscribe to these topics to receive the messages.

3. Scalability: Azure Cosmos DB offers elastic scalability, allowing users to scale their database throughput and storage on-demand. It can handle large-scale workloads and automatically distribute data across multiple regions for low-latency access. On the other hand, Kafka is designed to be highly scalable by distributing the load across multiple brokers. It can handle millions of messages per second and supports horizontal scaling by adding more brokers to the cluster.

4. Messaging Capabilities: Azure Cosmos DB provides the Change Feed feature, which allows developers to listen to real-time data changes and trigger actions based on those changes. However, it is primarily a database service and does not provide the same messaging capabilities as Kafka. Kafka, on the other hand, is specifically designed for messaging and allows users to publish and consume messages in real-time. It provides features like guaranteed message delivery, message replay, and fault tolerance.

5. Data Consistency: Azure Cosmos DB provides multiple consistency models, including strong, bounded staleness, session, and eventual consistency. Users can choose the most appropriate consistency level for their application's requirements. Kafka, on the other hand, does not guarantee strong consistency out of the box. It provides at-least-once message delivery semantics and allows users to configure their own level of data consistency.

6. Use Cases: Azure Cosmos DB is well-suited for applications that require low-latency global distribution, high availability, and flexible data models. It is commonly used for web and mobile applications, real-time analytics, and IoT scenarios. Kafka, on the other hand, is commonly used for building real-time event streaming pipelines, data integration, log aggregation, and event-driven microservices architectures.

In Summary, Azure Cosmos DB is a globally distributed, multi-model database service with elastic scalability and various consistency models. It is well-suited for web and mobile applications, real-time analytics, and IoT scenarios. Kafka, on the other hand, is a distributed streaming platform designed for high-throughput, fault-tolerant, and real-time data streaming. It provides messaging capabilities, fault tolerance, and is commonly used for building real-time event streaming pipelines and event-driven architectures.