Envoy Proxy: Evolved for Serving LLMs

Introduction

As large language models (LLMs) become central to modern applications, the need for efficient, secure, and scalable infrastructure to serve these models has grown exponentially. Envoy Proxy, a high-performance edge and service proxy, has evolved to address the unique challenges of LLM serving. This article explores how Envoy Proxy leverages its advanced capabilities in cloud load balancing, upstreaming, and CNCF-aligned architecture to meet the demands of LLM deployment, while ensuring reliability, security, and cost efficiency.

Key Requirements and Challenges

LLM serving introduces several critical challenges:

• Simplified Access: Developers require a unified interface to interact with multiple LLM providers (e.g., OpenAI, Anthropic, Vertex AI), avoiding API fragmentation.
• Security: Protection against attacks like jailbreak and prompt injection, along with secure management of API keys and credentials.
• Reliability: Ensuring service stability and low latency despite GPU/TPU resource constraints.
• Cost Efficiency: Optimizing resource utilization to reduce operational costs.

Deployment Scenarios

Multi-Provider API Integration

Envoy Proxy acts as a unified API gateway, abstracting differences between LLM providers. It supports:

• Centralized authentication and credential management.
• Security checks and logging for auditability.
• Streamlined developer workflows through consistent API contracts.

Kubernetes Cluster Deployment

For enterprises seeking control over infrastructure, Envoy integrates with Kubernetes clusters via Service Mesh patterns. Key benefits include:

• Dynamic scaling of LLM workloads.
• Seamless integration with Kubernetes Gateway APIs.
• Support for both ingress and egress traffic, enabling hybrid cloud deployments.

LLM Tool and Service Integration

LLMs often interact with external tools (e.g., MCP Server). Envoy handles outbound traffic (egress) while maintaining:

• Ingress and egress routing policies.
• Mesh-based traffic management for distributed LLM services.

Envoy Proxy’s Solutions

Model-Aware Routing

LLM requests often include model-specific metadata in the payload. Envoy addresses this with:

• XRO Extensions: gRPC calls to external services for header and body event processing.
• Body-Based Router: Parses request bodies to extract model names (e.g., x-gateway-model-name) and routes traffic to appropriate backend clusters.
• Support for streaming and buffer management for large payloads.

Inference-Optimized Load Balancing

Envoy introduces advanced load balancing strategies tailored for LLM inference:

• Weighted Round Robin with Lease Requests: Prioritizes underutilized resources.
• Orca Mechanism: Collects real-time metrics (e.g., GPU utilization, cache usage) from model servers.
• Control Plane Integration: Uses LRS (Load Reporting Service) and XCS API for dynamic regional weight adjustments.

Technical Implementation Details

• XRO Extensions: Handle HTTP lifecycle stages (headers, body, trailers) with external gRPC services.
• Orca Mechanism: Currently uses HTTP headers for metric collection; future enhancements include asynchronous probing and periodic checks.
• Kubernetes CRDs: Custom Resource Definitions (CRDs) define model-aware routing rules, pool strategies, and dynamic weight adjustments.

Core Technical Features

• Unified API Interface: Simplifies interactions with diverse LLM providers.
• Security Integration: Embeds AI safety checks (e.g., Google Model Armor, Palo Alto Nemo Guardrails).
• Language Agnosticism: Supports extensions in languages like Python for custom algorithms.
• Cost-Efficiency: Balances resource usage and latency through load signals and traffic control.

Scalability and Language Agnosticism

Envoy’s architecture emphasizes extensibility, allowing:

• Bring Your Own Extension (BYOE): Enables platform builders to customize logic without vendor lock-in.
• WASM Integration: WebAssembly modules for lightweight, language-agnostic extensions.
• Modular Design: Supports chaining of extensions for complex workflows.

Load Balancing Optimization and Metrics Collection

• Orca Mechanism: Collects metrics like KB buffer usage and QAP (Queue and Pool) from model servers.
• Asynchronous Probing: Future enhancements will enable pre-selected endpoints and periodic checks for streaming workloads.
• Cost Considerations: Avoids over-provisioning backend resources due to high GPU/TPU costs.

Kubernetes Gateway API Expansion

The Kubernetes Serving Working Group is advancing Gateway APIs with:

• CRD Definitions: Model-aware routing policies, per-model/pool strategies, and XRO-based protocols.
• Laura Algorithm: Dynamically loads model weights for load-aware routing.
• Prefix Cache Awareness: Reuses KV caches for repeated requests.
• Compatibility: Ensures backward compatibility with existing Kubernetes Gateway APIs.

Inference-Optimized Architecture and Endpoint Selection

• Body-Based Routing: Uses HTTP headers to route traffic based on model metadata.
• Endpoint Picker: Dynamically selects endpoints using Orca metrics (e.g., KB buffer usage) and Laura weights.
• Traffic Prioritization: Applies criticality-based policies to handle key workloads.
• Endpoint Filtering: Pre-filters unsuitable endpoints for efficient resource allocation.

Performance Validation and Ecosystem

• Benchmark Results: Envoy achieves lower tail latency and consistent throughput compared to Kubernetes Cluster IP-based load balancing.
• Ecosystem Adoption: Projects like Amoi Gateway, GKE Gateway, and Von Production Stack leverage Envoy’s capabilities.

Technical Integration and Future Directions

• XRO Protocol: Positioned as a key standard for future LLM serving.
• WASM Advantages: Expected to enhance extensibility and performance.
• Composability: Emphasizes modular, chainable extensions for complex use cases.

Conclusion

Envoy Proxy’s evolution into a specialized tool for LLM serving addresses critical challenges in scalability, security, and cost efficiency. By integrating advanced load balancing, model-aware routing, and Kubernetes compatibility, Envoy enables enterprises to deploy and manage LLM workloads effectively. Its extensible architecture and alignment with CNCF standards position it as a foundational component for next-generation AI infrastructure.