Enable efficient handling of data at different values and volumes. Process high-volume low-value data directly at the edge and reliably transmit critical low-volume, high-value events to and from the cloud.

Facilitate reliable bi-directional data flows for updating AI models, distributing binaries, or time-sensitive data changes to and from edge locations, for synchronized data exchanges and enhanced response times.

Re-deploy and re-architect apps without breaking the solution or re-configuring existing structures with subject-based addressing. Layer 7 conversational-style transport supports dynamic, event-based communication between AI services over TCP/IP.

Unifies diverse messaging models, lowering latency and system complexity while boosting performance. Offers asynchronous catch-up to sync messages when remote leaf nodes at the edge with intermittent connectivity reconnect.

Manage data easily with a unified data layer for streaming, key-value stores and object storage. Ephemeral, durable data handling enables flexible, scalable inferencing and updates without IP multicast across AI components.

Accelerate development and deployment of AI applications by focusing on core application logic without reinventing the communication layer, eliminating extensive development and debugging of infrastructure code.