SGLang vs Inference — Comparison

SGLang

SGLang is a high-performance serving framework for large language models and multimodal models. - sgl-project/sglang

SGLang is a high-performance serving framework for large language models and multimodal models. It is designed to deliver low-latency and high-throughput inference across a wide range of setups, from a single GPU to large distributed clusters. Its core features include: SGLang has been deployed at large scale, generating trillions of tokens in production each day. It is trusted and adopted by a wide range of leading enterprises and institutions, including xAI, AMD, NVIDIA, Intel, LinkedIn, Cursor, Oracle Cloud, Google Cloud, Microsoft Azure, AWS, Atlas Cloud, Voltage Park, Nebius, DataCrunch, Novita, InnoMatrix, MIT, UCLA, the University of Washington, Stanford, UC Berkeley, Tsinghua University, Jam Tea Studios, Baseten, and other major technology organizations. As an open-source LLM inference engine, SGLang has become the de facto industry standard, with deployments running on over 400,000 GPUs worldwide. SGLang is currently hosted under the non-profit open-source organization LMSYS. For enterprises interested in adopting or deploying SGLang at scale, including technical consulting, sponsorship opportunities, or partnership inquiries, please contact us at sglang@lmsys.org. Long-term active SGLang contributors are eligible for coding agent sponsorship, such as Cursor, Claude Code, or OpenAI Codex. Email sglang@lmsys.org with your most important commits or pull requests. SGLang is a high-performance serving framework for large language models and multimodal models. There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. There was an error while loading. Please reload this page.

Inference

Train, deploy, observe, and evaluate LLMs from a single platform. Lower cost, faster latency, and dedicated support from Inference.net.

Based on the social mentions, users are primarily concerned with **cost optimization and performance efficiency** for AI inference. There's significant discussion around pricing strategies, with founders seeking guidance on appropriate markup multipliers (3x-10x) from token costs to customer pricing. The community shows strong interest in **cost-saving alternatives** like open-source solutions and performance optimizations, with mentions of tools that reduce inference expenses and improve speed (like IndexCache delivering 1.82x faster inference). Users appear frustrated with **expensive closed APIs** and are actively seeking more affordable, deployable alternatives that don't compromise on quality, as evidenced by interest in open-weight models and specialized inference hardware.

SGLang

Inference

SGLang

Inference

SGLang

Inference