SGLang vs Together AI

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	SGLang Fine-tuning	Together AI Fine-tuning
Tagline	Open-source high-throughput inference engine for LLMs and multimodal models with OpenAI-compatible serving.	Fine-tune & serve open-weight models (Llama, Mistral, DeepSeek).
Category	Fine-tuning	Fine-tuning
Pricing	Free· Free, open-source (Apache 2.0); self-hosted infra cost only	Paid· Pay-per-token; fine-tuning per-token
Model	Multi-model (DeepSeek, Qwen, Llama, Mistral, GLM, GPT-OSS)	Llama / Mistral / Qwen / DeepSeek and others
Editorial score	—	8.6 / 10
Use cases	llm-servingmultimodal-inferenceself-hostingopenai-compatible-apihigh-throughput-inference	open modelsfine-tuninginference
Pros	State-of-the-art throughput via speculative decoding and disaggregated prefill/decode OpenAI-compatible endpoints make migration from hosted APIs trivial Broad hardware coverage: NVIDIA, AMD, TPU, Ascend, XPU, CPU Backed by real production users (NVIDIA, xAI, Oracle, LinkedIn) Fully open source under Apache 2.0	Wide open-model catalogue Competitive inference pricing Fine-tune + serve in one place Dedicated endpoints for production
Cons	Self-hosted only; no managed inference offering Tuning for peak throughput requires real ML-infra expertise Documentation assumes you already know LLM-serving concepts	Latency varies by model Less polish than OpenAI
Website	sglang.io	www.together.ai

Pick SGLang if

✅ State-of-the-art throughput via speculative decoding and disaggregated prefill/decode
✅ OpenAI-compatible endpoints make migration from hosted APIs trivial
✅ Broad hardware coverage: NVIDIA, AMD, TPU, Ascend, XPU, CPU
✅ Backed by real production users (NVIDIA, xAI, Oracle, LinkedIn)

Pick Together AI if