Evidence tier Source Confirmed · 2 captures on record
What the publisher says
As described on Microsoft Marketplace.
SGLang is an open-source high-performance serving framework designed for deploying large language models (LLMs) and multimodal language models in production and development environments. It provides an optimized runtime for efficient model inference and enables applications, AI platforms, developers, and organizations to serve models through programmatic and API-based interfaces.
The solution supports common AI inference and model-serving workflows including launching language models, generating text and chat responses, serving models through HTTP APIs, handling concurrent requests, and optimizing inference performance on supported GPU hardware. SGLang is suitable for AI developers, machine-learning engineers, researchers, application developers, and organizations that require a high-performance self-hosted runtime for large-scale model serving.
Show the rest of the publisher’s description (18 more lines)
Features of SGLang:
- Open-source framework for high-performance LLM and multimodal model serving.
- Provides an optimized runtime for efficient AI model inference.
- Supports serving large language models through HTTP-based APIs.
- Supports OpenAI-compatible API workflows for application integration.
- Designed for high-throughput and low-latency inference workloads.
- Supports efficient batching and concurrent request processing.
- Provides GPU-optimized inference capabilities for supported hardware.
- Supports distributed and scalable model-serving deployments.
- Can be integrated with AI applications, agents, and custom inference workflows.
- Suitable for development, research, production inference, and self-hosted AI platforms.
Usage instructions:
Activate Virtual Environment:
$ sudo su
$ source /opt/sglang/venv/bin/activate
$ cd /opt/sglang
# Check installed SGLang version: $ pip show sglang
Disclaimer: SGLang is provided “as is” under its applicable open-source license. Model availability, performance, GPU compatibility, CUDA requirements, and resource consumption depend on the selected model, hardware, drivers, and runtime configuration. Users are responsible for securing API endpoints, configuring authentication and network access controls where required, managing model licenses, and complying with applicable usage and data-protection requirements. This solution is suitable for self-hosted AI inference, LLM application development, research, experimentation, and production model-serving workflows.
Agent build and provenance
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Compliance
- FedRAMPConfirmedNot listed90%, registry-checkedNo FedRAMP Marketplace entry matched this vendor's domain, checked 2026-08-27registry recordas observed 2026-08-27
Confirmed means matched to a public authoritative registry. Claimed means the vendor or its listing states it, not yet cross-checked. A framework not shown was not found in any source we hold, which is not evidence against it. Not listed means a scoped registry check found no match for this vendor's domain: a No is a scoped registry check, not a compliance judgment. Confidence bands: 95% domain-verified, 90% registry-checked, 80% self-attested, 70% weak signal. Self-attested items marked “vendor's site” are gathered from the vendor's own website and are not verified by us.
Vendor
External enrichment · as of 2026-08-29
Reconciled on 9/3/2026
Sources
Publisher resources
1 linkLinked repositories
Unknown means this listing does not publish a repository. It is not a statement that the code is closed, and a linked repository is not a claim that the publisher wrote it: the registry computes that relationship privately and does not publish it.
Evidence risk is the share of the build you cannot see before you deploy, not a security rating. Sign in to see the layer-by-layer basis for this band.