A cloud-native traffic orchestration system for high availability, extensibility, and observability in API management and service mesh.
Easegress is a cloud-native traffic orchestration system designed to manage and route network traffic with high availability, performance, and extensibility. It provides a versatile platform for API management, service mesh integration, and implementing resilience patterns like circuit breaking and rate limiting, making it essential for building and operating modern distributed applications.
Platform engineers, SREs, and backend developers building and managing scalable, resilient microservices architectures and cloud-native applications that require sophisticated traffic routing, security, and observability.
Developers choose Easegress for its unique combination of a simple pipeline-filter orchestration model, built-in high availability via Raft consensus, and broad extensibility including WebAssembly support and deep AI integration, all while supporting multiple protocols like HTTP/1.1-3, MQTT, and WebSocket.
A Cloud Native traffic orchestration system
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Uses Raft consensus and leader election to ensure 99.99% availability, as highlighted in the features, providing fault tolerance for critical traffic management.
Handles HTTP/1.1, HTTP/2, HTTP/3 (QUIC), MQTT, and WebSocket, making it versatile for diverse applications from web APIs to IoT, as listed in the protocol features.
Supports custom filter development and WebAssembly execution, allowing users to tailor traffic handling without core modifications, evidenced by the extensibility section.
Proxies requests to LLM providers like OpenAI, adapts API formats, and integrates with vector databases for caching, catering to modern AI-driven workflows as described in the AI integration features.
Service mesh integration is tightly coupled with EaseMesh, limiting interoperability for teams using other mesh solutions, as noted in the mesh master and sidecar features.
The pipeline-filter mechanism and cluster deployment require significant configuration effort, which can be daunting compared to simpler proxies, despite the provided tutorials.
Managing a Raft-based cluster with hot-updates and multiple protocols adds maintenance complexity, potentially increasing costs for small teams or simple use cases.