SGLang
A high-performance framework for efficiently serving language and multimodal models at scale
- Also known as
- sglang
What is SGLang?
An open-source inference system designed for production deployments of large language and multimodal models, supporting both single-GPU and distributed cluster setups with emphasis on low latency and high throughput.
SGLang pricing
We don't have SGLang's full plan breakdown yet (its pricing page resisted automated reading). Here's what we could confirm. Always check live pricing for exact numbers.
What SGLang does
The capabilities that matter for model serving, normalised so it lines up with every alternative. “-” means we haven't confirmed it, not that it's missing.
- Continuous batching
- -
- Dynamic batching
- -
- Multi framework support
- ✓
- GPU acceleration
- ✓
- Multi GPU multi node
- ✓
- Quantization support
- -
- Openai compatible API
- ✓
- Autoscaling scale to zero
- -
- Multi model serving
- -
- Canary ab rollout
- -
- Kubernetes native
- -
- Open source
- ✓
Platform & deployment
Independently observed- Self-hosted
Integrations (3)
Independently observed- Hugging Face
- OpenAI
- Slack
SGLang FAQ
Common questions about SGLang, answered from independent, dated evidence.
What is SGLang?
An open-source inference system designed for production deployments of large language and multimodal models, supporting both single-GPU and distributed cluster setups with emphasis on low latency and high throughput. It is indexed under Model Serving.
Source: https://sgl-project.github.io/
Is SGLang free to use?
SGLang is open source, so it can be self-hosted and used at no licence cost. Pricing changes often, so verify at source before relying on it.
Source: https://sgl-project.github.io/
Can SGLang be self-hosted?
Yes. SGLang can be deployed self-hosted, so it does not have to run on the vendor's infrastructure.
Source: https://sgl-project.github.io/
SGLang alternatives
Other model serving we track, ranked by the same independent score.
- KServeA Kubernetes-based platform for deploying and scaling AI models across multiple frameworkshigh · 79%
- TorchServeA tool for hosting multiple PyTorch models in production with built-in logging and metricshigh · 76%
- Amazon SageMaker InferenceDeploy and serve machine learning models at scale with support for multiple frameworks and inference patternshigh · 75%
- LMDeployA framework for optimizing, deploying, and serving large language modelsmedium · 64%
- NVIDIA Triton Inference Serverhigh · 77%
- NVIDIA NIMContainerized GPU-accelerated inference services for deploying AI models across cloud, on-premises, and edge environments.medium · 74%
Compare SGLang
Side by side against other model serving, attribute by attribute, with a source on every value.
The vioscaleAI score: one lens on the evidence
Not user reviews and not a paid placement: a confidence-weighted blend of the independent signals below (adoption, activity, security posture, and more), which you can sort and re-weight yourself. Vendors can correct their listing but can never move their rank, and stars are weighted low as a vanity metric. It is one way to read the evidence for SGLang, not the verdict.
| Signal | Score | Weight | Contribution | Evidence |
|---|---|---|---|---|
| Capabilities | 75 | 0.08 | 6.0 | ✓ |
| Integrations | 17 | 0.05 | 0.9 | ✓ |
| Stars | 0 | 0.05 | 0.0 | - |
| Dependent projects | 0 | 0.05 | 0.0 | - |
| Development activity | 0 | 0.14 | 0.0 | - |
| Release cadence | 0 | 0.08 | 0.0 | - |
| Security posture | 0 | 0.12 | 0.0 | - |
| Package downloads | 0 | 0.04 | 0.0 | - |
| Security score | 0 | 0.05 | 0.0 | - |
| Developer Q&A activity | 0 | 0.14 | 0.0 | - |
Computed . Re-weight it by intent, or see the full method.
All data & sourcesshow ↓
Every value we hold, with its source, retrieval date, and confidence. This is the evidence behind the score: don't trust it, verify it.
Content
| Attribute | Value | Evidence |
|---|---|---|
| Faq | 3 items | mediumsource · 2026-09-10 · 60% |
Features
| Attribute | Value | Evidence |
|---|---|---|
| Capabilities | Open source, Gpu acceleration, Multi gpu multi node, Openai compatible api, Multi framework support | mediumsource · 2026-09-29 · 60% |
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 3 | mediumsource · 2026-09-29 · 60% |
Pricing
Is SGLang the right choice for you?
Tell us the job, the constraints and what you weigh most, and we will rank SGLang against the rest of the model serving we index, using the same dated evidence weighted your way.
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