OpenAI Fine-tuning vs SGLang

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

	OpenAI Fine-tuning Fine-tuning	SGLang Fine-tuning
Tagline	Fine-tune GPT-4o-mini and friends on your own data.	Open-source high-throughput inference engine for LLMs and multimodal models with OpenAI-compatible serving.
Category	Fine-tuning	Fine-tuning
Pricing	Paid· Basic: $10 · Pro: $25 · Enterprise: Contact sales	Free· Free, open-source (Apache 2.0); self-hosted infra cost only
Model	GPT-4o-mini / GPT-3.5	Multi-model (DeepSeek, Qwen, Llama, Mistral, GLM, GPT-OSS)
Editorial score	8.4 / 10	8.2 / 10
Use cases	styleformatdomain knowledge	llm-servingmultimodal-inferenceself-hostingopenai-compatible-apihigh-throughput-inference
Pros	Easiest fine-tuning UX Vision FT now supported Works inside the OpenAI ecosystem Same infra/SLA as base models	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	Pricier than open-model FT No weights export	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	platform.openai.com	sglang.io

Pick OpenAI Fine-tuning 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)