SGLang
Open-source serving framework for large language and multimodal models.
At a glance
- Editor scoreNot yet scored
- PricingFree plan
- Best forTeams serving language models on varied hardware
- Free planYes
- Paid fromNone
- Deployment modeDedicated
- Facts checked22 Sep 2026
Where it wins
- OpenAI-compatible chat and completion APIs
- Multi-GPU and distributed-cluster serving
- Supports NVIDIA, AMD, CPU, TPU, NPU, and XPU hardware
Where it doesn't
- Self-hosted rather than a managed model-hosting service
- Requires deployment through local, container, cluster, or cloud infrastructure
- No vendor-hosted pricing or published deployment-region list
Our verdict on SGLang
SGLang is an open-source inference and serving framework for large language, vision-language, and other multimodal models. It is designed for teams that need to serve models on varied hardware, including NVIDIA, AMD, CPU, TPU, NPU, and XPU systems. The framework provides OpenAI-compatible chat and completion APIs, a native inference engine for offline batch inference, GPU acceleration, and model-parallel serving. It runs locally, in Docker, on Kubernetes, or through cloud deployment tools.
Its strongest fit is a varied deployment ecosystem. SGLang supports Hugging Face, the OpenAI API, Docker, Kubernetes, SkyPilot, and AWS SageMaker, giving teams several ways to connect model serving with existing infrastructure. It also supports safetensors, PyTorch, GGUF, and Mistral-native checkpoints, which broadens the range of model artifacts it can serve. RadixAttention and prefix caching address repeated-prefix workloads, while multi-GPU and distributed-cluster serving support larger deployments. Teams choosing SGLang should value infrastructure control and compatibility across hardware and model formats.
The trade-off is operational ownership. SGLang is a self-hosted framework, not a managed model-hosting service, so deployment depends on the team’s local, container, Kubernetes, or cloud environment. Its published capabilities include Docker, Kubernetes, and cloud deployment options, but the product does not provide vendor-hosted pricing or a published deployment-region list. Teams wanting direct control over serving infrastructure, offline inference, multimodal support, and distributed hardware can choose SGLang. Teams seeking a managed service with vendor-operated hosting should choose a different model-hosting option.
SGLang pricing
SGLang fact sheet
| Free plan | Yes |
|---|---|
| Paid from | None |
| Deployment mode | Dedicated |
| Autoscaling | Not verified |
| GPU accelerators | Yes |
| Private deployment | Not verified |
| Supported model formats | safetensors, PyTorch .bin, GGUF, Mistral native |
| Batch inference | Yes |
| Deployment regions | Not verified |
| Deployment | Cloud, Self-hosted |
| Platforms | Linux |
| Support | Community, Docs |
| Built for | Solo, Small business, Mid-market, Enterprise (editorial estimate) |
| Integrations | 6 integrations: Hugging Face, OpenAI API, Docker, Kubernetes, SkyPilot, AWS SageMaker |
| Pricing | Free plan |
| Website | sglang.io |
| Facts checked | 22 Sep 2026 |
SGLang integrations
SGLang lists 6 integrations on its own site.
- Hugging Face
- OpenAI API
- Docker
- Kubernetes
- SkyPilot
- AWS SageMaker
Alternatives to SGLang
- BasetenManaged model APIs with GPU deployments, autoscaling, training, and observability.8.0
- BentoMLBentoML gives open-source teams flexible paths from local serving to managed inference.7.8
- KServeOpen-source model serving for teams operating extensible inference on Kubernetes.—
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Reviewed by iTechGuides Editors · Editorial team · Updated Sep 2026
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