Envoy AI Gateway vs SGLang
Envoy AI Gateway
aigateway.envoyproxy.ioSGLang
sglang.ioSide by side
| Figure | Envoy AI Gateway | SGLang |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · LLM Gateway Software | 8.4 (#8) | 8.1 (#12) |
| Spec score · LLM gateways | 8.4 (#10) | 8.4 (#11) |
| License | The project uses the Apache 2.0 license. | The GitHub repository identifies the project license as Apache-2.0. |
| Purpose | The open-source project routes and manages generative AI traffic between applications and AI services, with rate limiting and policy control. | SGLang is an open-source inference framework for serving large language, vision-language, and diffusion models. |
| Security | The project describes upstream authentication for securing connections to AI services and says provider credentials can be kept out of application workloads. | The repository identifies its license as Apache-2.0. |
| Support | The project directs users to its community Discord and GitHub issues for help. | The project points users to GitHub issues, Slack, Discord, and community discussions for questions and help. |
Plans
In their own words
Envoy AI Gateway
Envoy AI Gateway, now named Agent Router, is an open-source control plane for managing AI and agent traffic through Envoy. It provides an OpenAI-compatible API for hosted and self-hosted models and MCP servers, with integrations for providers including OpenAI, Azure OpenAI, Google Gemini, AWS Bedrock, Mistral, and Anthropic. Traffic controls include model virtualization, provider failover, token quotas, and usage-based rate limiting. Its MCP gateway supports server multiplexing, tool routing, OAuth authentication, and observability; InferencePool support adds routing and load balancing for inference endpoints. Credentials can be managed centrally, and supported short-lived credential flows include AWS Bedrock, Azure OpenAI, and GCP Vertex AI. The project describes metrics collection and monitoring for AI workloads. The free open-source project is listed at free. Its CLI supports local use on Linux and macOS; production Kubernetes deployment uses Envoy Gateway and requires compatible dependencies. The project uses the Apache 2.0 license and points users to Discord and GitHub issues for help.
SGLang
SGLang is a free, open-source inference framework for serving large language, vision-language and diffusion models. It targets agentic workloads, reinforcement-learning rollouts and large-scale serving, from one GPU to distributed clusters. Runtime features include RadixAttention, prefix caching and multi-GPU parallelism. Listed optimizations include disaggregated prefill and decode, speculative decoding, a zero-overhead scheduler and optimized GPU kernels. Supported models include DeepSeek, Qwen, GPT-OSS, Llama, Mistral and GLM. SGLang works with Hugging Face and OpenAI APIs and exposes OpenAI-compatible endpoints. It installs with pip or Docker and supports self-hosted deployment. Hardware support spans NVIDIA and AMD GPUs, Google TPUs, Intel GPUs and CPUs, Apple Silicon, Huawei Ascend NPUs and Moore Threads GPUs. SGLang Diffusion adds image and video generation. The Apache-2.0 license permits open-source use.