LLM Gateway vs SGLang

LLM Gateway provides one OpenAI-compatible API for routing requests among more than 250 models from over 40 providers. If a provider errors, times out, or rate-limits a request, it can retry up to twice with another provider for the same model. Its dashboard tracks request, token, spend, latency, error, and cache metrics, with model and provider breakdowns. Teams can use their own provider API keys without a platform fee and pay providers directly. The open-source core is licensed under AGPLv3 and can be self-hosted for free; enterprise features require a license. The Free plan is free, billed free forever, and has a 5% fee on credit usage. It includes 200+ models across 40+ providers, activity logs and export, and 20 requests per minute on free models; paid models are not rate limited. Enterprise pricing is custom and not listed. It includes custom routing, unlimited data retention, a 99.9% uptime SLA, and 24/7 priority support. Published integration guides cover tools including Claude Code, Cursor, Codex CLI, Cline, n8n, and VS Code.

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.