SGLang vs TensorZero
SGLang
sglang.io
TensorZero
tensorzero.comSide by side
| Figure | SGLang | TensorZero |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · LLM gateways | 8.4 (#11) | 8.6 (#7) |
| Spec score · LLM Gateway Software | 8.1 (#12) | 8.0 (#13) |
| Integrations | The README lists deployment and orchestration integrations including Ray Serve, NVIDIA Dynamo, and llm-d. | Documented providers include Anthropic, AWS Bedrock, Azure, Google, OpenAI, OpenRouter, Together AI, vLLM, and OpenAI-compatible endpoints. |
| License | The GitHub repository identifies the project license as Apache-2.0. | The FAQ describes TensorZero as open source under the Apache 2.0 License. |
| Notable limitation | The October 2, 2026 release notes state that prefill context parallelism is unavailable on HIP, NPU, and MUSA until those platforms are ported. | The FAQ says TensorZero had no revenue model at the time described and planned a managed service for the future; the current homepage says the project is no longer maintained. |
| Security | The repository identifies its license as Apache-2.0. | TensorZero's privacy policy says it uses physical, technical, organizational, and administrative security measures to protect personal data. |
| Support | The project points users to GitHub issues, Slack, Discord, and community discussions for questions and help. | The project README directs users to Slack or Discord and offers work teams a Slack or Teams channel by email. |
Plans
In their own words
SGLang
SGLang is a free, open-source inference framework for serving large language, vision-language and diffusion models. It targets agentic workloads, reinforcement-learning rollouts and large-scale serving, from one GPU to distributed clusters. Runtime features include RadixAttention, prefix caching and multi-GPU parallelism. Listed optimizations include disaggregated prefill and decode, speculative decoding, a zero-overhead scheduler and optimized GPU kernels. Supported models include DeepSeek, Qwen, GPT-OSS, Llama, Mistral and GLM. SGLang works with Hugging Face and OpenAI APIs and exposes OpenAI-compatible endpoints. It installs with pip or Docker and supports self-hosted deployment. Hardware support spans NVIDIA and AMD GPUs, Google TPUs, Intel GPUs and CPUs, Apple Silicon, Huawei Ascend NPUs and Moore Threads GPUs. SGLang Diffusion adds image and video generation. The Apache-2.0 license permits open-source use.
TensorZero
TensorZero is a free, open-source stack that brings together an LLM gateway, observability, optimization, evaluation, and experimentation. Its gateway provides a unified API for model providers and supports routing, retries, fallbacks, prompt templates, schemas, multi-step workflows, and traffic routing for A/B tests. Documented integrations include Anthropic, AWS Bedrock, Azure, Google, OpenAI, OpenRouter, Together AI, vLLM, xAI, and OpenAI-compatible endpoints. Inference and feedback data go to the database you configure, where they can be queried with SQL; feedback can be tied to individual inferences or episodes. The gateway and UI can run through Docker, Docker Compose, Kubernetes and Helm, or a source build. API key authentication is available for gateway endpoints other than status and health checks. The project is available on GitHub, but is no longer maintained; its repository is archived and read-only. TensorZero is self-hosted and licensed under Apache 2.0. It collects pseudonymous aggregate usage analytics, excluding application data and inference inputs and outputs, with an option to disable collection.