Modal vs SGLang

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

	Modal Fine-tuning	SGLang Fine-tuning
Tagline	Serverless GPUs and infra for training & serving ML.	Open-source high-throughput inference engine for LLMs and multimodal models with OpenAI-compatible serving.
Category	Fine-tuning	Fine-tuning
Pricing	Freemium· $30/mo free credits; pay-as-you-go GPU rates	Free· Free, open-source (Apache 2.0); self-hosted infra cost only
Model	Infrastructure (any model you can host)	Multi-model (DeepSeek, Qwen, Llama, Mistral, GLM, GPT-OSS)
Editorial score	8.7 / 10	—
Use cases	serverless GPUfine-tuningbatch inference	llm-servingmultimodal-inferenceself-hostingopenai-compatible-apihigh-throughput-inference
Pros	Zero-ops GPU access Python-native Auto-scaling Honest pay-per-second pricing	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
Cons	Cold start latency on big models Bills can surprise at scale	Self-hosted only; no managed inference offering Tuning for peak throughput requires real ML-infra expertise Documentation assumes you already know LLM-serving concepts
Website	modal.com	sglang.io

Pick Modal if

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)