# Ray Serve

Ray Serve is an open-source Python library for building scalable online inference APIs for machine learning models, including LLMs. It supports model composition, model multiplexing, built-in autoscaling, response streaming and dynamic request batching, and works with frameworks such as PyTorch and TensorFlow. It can run on a laptop, in a Docker container, on a managed Ray cluster or on Kubernetes via KubeRay. It is part of the open-source Ray project built by Anyscale.

Page: https://slateindex.ai/brand/ray-serve · data of 2026-11-02

## Facts

- Headquarters: San Francisco, California
- Company type: Division of Anyscale
- Latest funding: Series C
- Deployment: Self-managed on a laptop, in a Docker container, on a managed Ray cluster, or on Kubernetes via KubeRay
- Best for: Python teams serving machine learning models, including LLMs, as online inference APIs that need independent scaling
- Compliance: SOC 2 Type II, ISO/IEC 27001

## Products

- Ray Serve: Scalable model serving library for building online inference APIs, with model composition, multiplexing, autoscaling and LLM serving features.

## Rankings

| Category | Rank | Visibility | Mentions |
|---|---|---|---|
| MLOps Platforms | 61 of 61 | 0.0 | 0 |

## By engine

| Engine | Visibility | Mention rate |
|---|---|---|
| chatgpt | 0.0 | 0.0% |
| google_ai_mode | 0.0 | 0.0% |
| perplexity | 0.0 | 0.0% |

## Definitions

- Visibility (0–100): how often and how high AI answers name the brand, weighted by position.
- Mention rate: share of collected AI answers that name the brand.
- Share of voice: the brand's mentions as a share of all brand mentions in the category.
- Citation authority: share of answers naming the brand that cite the brand's own site.

Cite as: SlateIndex, “Ray Serve AI visibility profile”, https://slateindex.ai/brand/ray-serve, data of 2026-11-02. Licence: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).