SGLang

SGLang is an open-source framework for serving language, vision-language, and diffusion models, with a runtime aimed at demanding inference workloads. It suits engineering teams running their own model infrastructure, from a single accelerator to distributed clusters. The zero-cost license and broad deployment options are compelling, but platform-specific gaps matter when you depend on advanced parallelism.

Overview

SGLang targets low-latency, high-throughput inference for agentic applications, reinforcement-learning rollouts, and large-scale serving. Its dedicated deployment mode supports GPU accelerators, batch inference, and self-hosting, while model inputs can use safetensors, PyTorch .bin, GGUF, or Mistral-native formats. Apache-2.0 licensing allows organizations to operate and adapt the framework without a software subscription.

The project can serve models through standard OpenAI-compatible endpoints and works with Hugging Face APIs, which reduces integration work for clients already built around those interfaces. Installation is available through Python tooling or Docker, so teams can choose a package-based workflow or a containerized deployment.

Key features

Inference runtime and optimization

RadixAttention, prefix caching, and multi-GPU parallelism are built into the runtime. In practice, these capabilities target repeated prompt prefixes, larger model footprints, and higher concurrent throughput. The optimization set also includes disaggregated prefill and decode, speculative decoding, a zero-overhead scheduler, and optimized GPU kernels. Together they make SGLang better suited to production serving and rollout generation than a minimal model wrapper, although getting the benefit requires infrastructure expertise.

Models, diffusion, and integrations

Named support covers DeepSeek, Qwen, GPT-OSS, Llama, Mistral, and GLM models. SGLang Diffusion is included in the repository and Python package for image and video generation, extending the framework beyond text serving. Reinforcement-learning integrations include Miles, slime, AReaL, Tunix, and verl, while deployment integrations include Ray Serve, NVIDIA Dynamo, and llm-d.

Hierarchical KV caching can span GPU memory, host memory, and external storage through projects such as HiCache, Mooncake, and LMCache. That architecture is useful when working sets exceed accelerator memory, but it also introduces additional components to operate.

Pricing

SGLang costs free under its single SGLang plan. The plan is an open-source inference framework installed with pip or Docker, with no paid tier, seat charge, trial period, or usage quota stated. It fits teams prepared to fund their own compute and operations rather than buy a managed serving service. The trade-off is that infrastructure, scaling, monitoring, and support remain your responsibility.

Platforms

SGLang supports API access, Linux, macOS, and self-hosted deployment. The hardware list spans NVIDIA and AMD GPUs, Google TPUs, Intel GPUs and CPUs, Apple Silicon, Huawei Ascend NPUs, and Moore Threads GPUs. The October 2, 2026 release notes identify one concrete constraint: prefill context parallelism is not available on HIP, NPU, or MUSA until those ports are completed. Teams relying on that feature should verify their accelerator choice before standardizing.

Who it's for

SGLang is a strong fit for platform engineers and research teams serving open models, running reinforcement-learning rollouts, or building agentic systems that need high throughput and OpenAI-style access. It also suits organizations wanting one stack for language, vision-language, and diffusion workloads across varied hardware. It is less suitable for buyers seeking a hosted service, built-in fallback routing, virtual API keys, or a turnkey operational layer; those functions are not part of this plan.

Pros and cons

• Pro: No software charge and Apache-2.0 licensing make self-hosted deployment economically flexible.
• Pro: Caching, speculative decoding, parallelism, and kernel optimizations address both latency and throughput bottlenecks.
• Pro: OpenAI-compatible endpoints and Hugging Face compatibility simplify adoption by existing clients.
• Pro: Broad accelerator coverage and included diffusion support reduce the need for separate serving stacks.
• Con: Prefill context parallelism is unavailable on HIP, NPU, and MUSA.
• Con: There is no fallback routing or virtual API-key capability, limiting built-in gateway controls.
• Con: Self-hosting leaves compute, reliability, and day-to-day operations to your team.

Alternatives

Choose Bifrost instead when you want a freemium gateway with self-hosted OSS deployment through npx, Docker, Kubernetes, or a Go binary, plus a separate enterprise tier. Routerly is a simpler free self-hosted option with no markup on API calls and AGPL-3.0 licensing.

YoloRouter fits teams preferring a single binary with SQLite out of the box and optional PostgreSQL. Envoy AI Gateway is another free, self-managed project for Linux and macOS, with Kubernetes deployments requiring compatible dependencies.

Kong Gateway is worth considering if a 30-day, no-credit-card trial with no Gateway limits and 30 days of analytics retention is more important than an entirely free framework. TensorZero offers a 100% self-hosted, open-source LLMOps platform. Eden AI is a paid API gateway that charges providers' API prices plus a 5.5% platform fee when purchasing credits. Helicone is a freemium, proudly open-source option with a Hobby plan including 10,000 free requests and 1 GB of storage.

For a broader gateway comparison, see LLM Gateway Software.

Verdict

Choose SGLang if you need a free, self-hosted serving foundation for open models, reinforcement-learning rollouts, or agentic workloads and have the engineering capacity to run it. Its runtime optimizations, API compatibility, model breadth, and hardware reach are substantial advantages. Look elsewhere if you require managed operations, fallback routing, virtual API keys, or prefill context parallelism on HIP, NPU, or MUSA.