Ray Serve is highly praised for its scalability, flexibility in deploying machine learning models, and effective handling of large-scale AI infrastructure, as evidenced by its usage by major companies such as Netflix and Tencent. The tool excels at simplifying large model development and providing robust support for distributed AI workloads. However, the absence of user reviews prevents insight into specific complaints or issues users might face. Overall, Ray Serve maintains a strong reputation within the tech community, and there's a generally positive sentiment surrounding its usability, but detailed pricing discussions are not evident from the social mentions.
Mentions (30d)
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3
GitHub Stars
41,936
7,402 forks
Ray Serve is highly praised for its scalability, flexibility in deploying machine learning models, and effective handling of large-scale AI infrastructure, as evidenced by its usage by major companies such as Netflix and Tencent. The tool excels at simplifying large model development and providing robust support for distributed AI workloads. However, the absence of user reviews prevents insight into specific complaints or issues users might face. Overall, Ray Serve maintains a strong reputation within the tech community, and there's a generally positive sentiment surrounding its usability, but detailed pricing discussions are not evident from the social mentions.
Features
Use Cases
Industry
information technology & services
Employees
11
41,936
GitHub stars
20
npm packages
3
HuggingFace models
🚀 Run SGLang with Ray! Try out Ray + SGLang (@lmsysorg) with new examples for • SGLang + Ray Serve (online inference) • SGLang + Ray Data (batch inference) Some example contributions to take a look.
🚀 Run SGLang with Ray! Try out Ray + SGLang (@lmsysorg) with new examples for • SGLang + Ray Serve (online inference) • SGLang + Ray Data (batch inference) Some example contributions to take a look. https://t.co/XoMWJMLH2f https://t.co/oNJ8qhgzJR
View originalPricing found: $100
Using RingRayLib and Claude Code
Hello The examples folder contains applications and games (over 59,000 lines of code) Developed in the Ring programming language using the RingRayLib library. All examples were generated 100% using Claude Code. Why these samples matter: This collection serves three purposes at once. First, it was born as a real-world stress test of Claude Code — each app and game was built during hands-on experimentation to explore what the tool is genuinely capable of, from simple clocks to shooters. Second, the samples double as a living test suite for the Ring language and RingRayLib themselves — using Claude Code to generate diverse programs is an effective way to surface edge cases, validate library coverage, and push the runtime across many different usage patterns. Third, and perhaps most powerfully, the collection acts as a reusable dataset for future development: because Claude Code can read and reason about existing code, you can point it at any sample here and instruct it to port a specific feature into your own project. This mix-and-match workflow is exactly how Ring games such as DaveTheFighter, Tank3D, LineDrawing3D, and CodeRooms3D were built — 100% with Claude Code, guided by prompts that referenced and combined ideas from samples like these. Thanks! submitted by /u/mrpro1a1 [link] [comments]
View original🌟 Help Shape the Future of Ray! Influence the future of Ray with our Ray Community Pulse survey. 🎁We are offering exclusive swag for all eligible participants, so take the survey before it closes o
🌟 Help Shape the Future of Ray! Influence the future of Ray with our Ray Community Pulse survey. 🎁We are offering exclusive swag for all eligible participants, so take the survey before it closes on Feb 20th, 2026! https://t.co/ie3uk4norr https://t.co/YrnaMOqUgz
View originalJoin us at Criteo (Paris) on Feb 5 for the Ray Paris Meetup 🇫🇷 Featured talk: Data-Intensive Distributed Training for Commerce Foundation Models @ Criteo + with Arij Sakhraoui & Alexis Benichou –
Join us at Criteo (Paris) on Feb 5 for the Ray Paris Meetup 🇫🇷 Featured talk: Data-Intensive Distributed Training for Commerce Foundation Models @ Criteo + with Arij Sakhraoui & Alexis Benichou – covering GPU orchestration, on-prem + cloud scaling, and real-world lessons using Ray! 📅 18:30–20:30 📍 32 Rue Blanche, 75009 Paris 👉 Save your spot: https://t.co/aBDBjNaPe7
View originalCall for Speakers: Ray Meetups @ Grab! 🇸🇬🇻🇳 🇸🇬 Singapore: Feb 24 (evening) 🇻🇳 Vietnam: Feb 26 (evening) Seeking talks on Ray + production ML (distributed training/inference, LLM apps/RAG, ev
Call for Speakers: Ray Meetups @ Grab! 🇸🇬🇻🇳 🇸🇬 Singapore: Feb 24 (evening) 🇻🇳 Vietnam: Feb 26 (evening) Seeking talks on Ray + production ML (distributed training/inference, LLM apps/RAG, eval, data, MLOps, perf, obs, demos). Submit your talk here: https://t.co/yYEAbRafdN
View originalTonight in London 🇬🇧! Ray x AWS London Meetup: Building Scalable AI & Data Platforms 📅 Today, Jan 13 | ⏰ 6–9pm GMT 📍 AWS LHR14, London Talks from Anyscale, AWS, and CloudNC on distributed traini
Tonight in London 🇬🇧! Ray x AWS London Meetup: Building Scalable AI & Data Platforms 📅 Today, Jan 13 | ⏰ 6–9pm GMT 📍 AWS LHR14, London Talks from Anyscale, AWS, and CloudNC on distributed training, Ray on AWS, and real-world ML platforms. 👉 Last chance to join: https://t.co/hgsFPr4t5i
View original🚀 Run SGLang with Ray! Try out Ray + SGLang (@lmsysorg) with new examples for • SGLang + Ray Serve (online inference) • SGLang + Ray Data (batch inference) Some example contributions to take a look.
🚀 Run SGLang with Ray! Try out Ray + SGLang (@lmsysorg) with new examples for • SGLang + Ray Serve (online inference) • SGLang + Ray Data (batch inference) Some example contributions to take a look. https://t.co/XoMWJMLH2f https://t.co/oNJ8qhgzJR
View originalSneak peek at our Ray x AWS London meetup talks (Jan 13, 6–9pm GMT): - Ray & Anyscale for distributed training – Ali Sezer (Anyscale) - Ray on AWS for scalable AI/data platforms – Christian Melendez
Sneak peek at our Ray x AWS London meetup talks (Jan 13, 6–9pm GMT): - Ray & Anyscale for distributed training – Ali Sezer (Anyscale) - Ray on AWS for scalable AI/data platforms – Christian Melendez & Robert Northard (AWS) - Real-world ML training w/ Ray – Chris Emery (CloudNC) 📍 AWS LHR14, London 👉 Join now: https://t.co/hgsFPr3VfK
View originalCurious how Ray scales AI training in practice? We’re hosting a live webinar to help you transition from single-GPU constraints to full-scale distributed clusters. Seats are limited to keep the ses
Curious how Ray scales AI training in practice? We’re hosting a live webinar to help you transition from single-GPU constraints to full-scale distributed clusters. Seats are limited to keep the session interactive. Reserve your spot today: https://t.co/FXebPOfXLW https://t.co/aOSduSWZd2
View originalParis 🇫🇷 we're hosting a Ray Meetup: Scaling AI Workloads – Distributed Compute Made Simple with Ray at Criteo! 📅 Thu, Feb 5 | 🕡 18:30–20:30 (GMT+1) 📍 32 Rue Blanche, 75009 Paris Learn how team
Paris 🇫🇷 we're hosting a Ray Meetup: Scaling AI Workloads – Distributed Compute Made Simple with Ray at Criteo! 📅 Thu, Feb 5 | 🕡 18:30–20:30 (GMT+1) 📍 32 Rue Blanche, 75009 Paris Learn how teams use Ray to scale training & inference from a single machine to distributed clusters. 👉 Sign up: https://t.co/aBDBjNaPe7
View originalLondon 🇬🇧 – we're hosting our first Ray x AWS meetup on Jan 13 (6–9pm GMT) at the AWS LHR14 office. We'll cover: - Ray & Anyscale for distributed training - Running Ray on AWS - Real-world ML
London 🇬🇧 – we're hosting our first Ray x AWS meetup on Jan 13 (6–9pm GMT) at the AWS LHR14 office. We'll cover: - Ray & Anyscale for distributed training - Running Ray on AWS - Real-world ML training at scale with Ray 👉 Request to join: https://t.co/qZfNbtBplB
View originalWant to learn how AI characters are built using reinforcement learning? This talk dives into how @character_ai adapts open-source RL libraries (like Verl) to serve millions of users every day: https
Want to learn how AI characters are built using reinforcement learning? This talk dives into how @character_ai adapts open-source RL libraries (like Verl) to serve millions of users every day: https://t.co/4HL9vjPZyy
View originalLooking for a quick TL;DR of all the updates vLLM had in 2025? Catch this talk from Simon Mo as he goes through the details. https://t.co/lNcQXQKOIT
Looking for a quick TL;DR of all the updates vLLM had in 2025? Catch this talk from Simon Mo as he goes through the details. https://t.co/lNcQXQKOIT
View originalJoin us on Jan 13 at the AWS LHR14 office for the first Ray x AWS London Meetup: Building Scalable AI & Data Platforms! Talks on: - Distributed training with Ray & Anyscale - Running Ray on A
Join us on Jan 13 at the AWS LHR14 office for the first Ray x AWS London Meetup: Building Scalable AI & Data Platforms! Talks on: - Distributed training with Ray & Anyscale - Running Ray on AWS - Real-world scaling stories in production 👉 Register now: https://t.co/qZfNbtBXb9
View originalJoin us for the final Ray Meetup of the year, where we will deep dive with technical talks on core advancements in Ray, as well as discuss what's coming in 2026. 🎉 Ray Meetup: A Year of Distributed
Join us for the final Ray Meetup of the year, where we will deep dive with technical talks on core advancements in Ray, as well as discuss what's coming in 2026. 🎉 Ray Meetup: A Year of Distributed Systems Innovation (End-of-Year Celebration) 🗓️ December 18 ⏱️ 5:30 - 7:30 PM 📍 Anyscale Office, San Francisco, CA 👉 Register here: https://t.co/ArzisWfG6d
View originalRepository Audit Available
Deep analysis of ray-project/ray — architecture, costs, security, dependencies & more
Pricing found: $100
Key features include: Ray Serve:....
Ray Serve is commonly used for: Serving real-time predictions for deep learning models in production environments., Deploying machine learning models as REST APIs for web applications., Scaling model inference across multiple nodes to handle high traffic loads., Integrating with CI/CD pipelines for automated model deployment., A/B testing different model versions to evaluate performance., Serving ensemble models that combine predictions from multiple algorithms..
Ray Serve integrates with: PyTorch, TensorFlow, Keras, Scikit-Learn, FastAPI, Flask, Django, Ray Core, Kubernetes, Docker.
Ray Serve has a public GitHub repository with 41,936 stars.
Based on 79 social mentions analyzed, 9% of sentiment is positive, 90% neutral, and 1% negative.
