惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

大猫的无限游戏
大猫的无限游戏
H
Hackread – Cybersecurity News, Data Breaches, AI and More
博客园_首页
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
D
Docker
酷 壳 – CoolShell
酷 壳 – CoolShell
宝玉的分享
宝玉的分享
Martin Fowler
Martin Fowler
美团技术团队
量子位
M
MIT News - Artificial intelligence
Apple Machine Learning Research
Apple Machine Learning Research
阮一峰的网络日志
阮一峰的网络日志
博客园 - 叶小钗
博客园 - 三生石上(FineUI控件)
腾讯CDC
Hugging Face - Blog
Hugging Face - Blog
博客园 - 【当耐特】
小众软件
小众软件
博客园 - 司徒正美
罗磊的独立博客
云风的 BLOG
云风的 BLOG
B
Blog RSS Feed
博客园 - 聂微东

Runpod Blog.

New Runpod datacenter now live: AP-IN-1 Track GPU spend across your team with Cost Centers The GPU supply supercycle is here. Here’s what AI builders need to know. Community Spotlight: One-click AI image and video generation on Runpod with SwarmUI | Runpod Blog Community Spotlight: LoRA Pilot Data Prep to Inference Introducing the Runpod Assistant: Manage Your Cloud GPU Resources with Natural Language OpenAI's Parameter Golf: Train the Best Language Model That Fits in 16MB on Runpod LLM inference optimization: techniques that actually reduce latency and cost Pruna P-Video and Vidu Q3 public endpoints now available on Runpod Runpod brand spelling guide Quickstart - Runpod Documentation The AI market looks nothing like the narrative Training StyleGAN3 with Vision-Aided GAN on Runpod KoboldAI – The Other Roleplay Front End, And Why You May Want to Use It How to Connect Cursor to LLM Pods on Runpod for Seamless AI Dev Community Spotlight: How AnonAI Scaled Its Private Chatbot Platform with Runpod Prompt Scheduling with Disco Diffusion on Runpod Runpod's Latest Innovation: Dockerless CLI for Streamlined AI Development Run Your Own AI from Your iPhone Using Runpod Introducing Flash: Run GPU workloads on Runpod Serverless: No Docker required Use Claude Code with your own model on Runpod: No Anthropic account required Avoid Errors by Selecting the Proper Resources for Your Pod What hackers built on Runpod at TreeHacks 2026 Easily Back Up and Restore Your Pod with Cloud Sync + Backblaze B2 The Complete Guide to GPU Requirements for LLM Fine-Tuning AI Guides, Tutorials & GPU Infrastructure Insights | Runpod Your first Claude Code project within Runpod: a complete setup guide 10 billion Serverless requests and counting Building for resilience: Runpod’s response to the AWS us-east-1 outage How to Connect Google Colab to Runpod
Refocusing on Core Strengths: The Shift from Managed AI A...
Justin Merrell · 2024-04-29 · via Runpod Blog.

As we strive to enhance our core infrastructure and streamline our services at Runpod, we have made the strategic decision to sunset our Managed AI APIs (previously referred to as "Endpoints"). This move is designed to sharpen our focus on the robust capabilities of our Serverless platform, which continues to support user endpoints effectively.

Understanding the Transition

The Managed AI APIs have been instrumental for users who wanted to quickly integrate AI functionalities into their projects without handling the underlying infrastructure. As the AI field grows and evolves rapidly, maintaining these APIs has presented significant challenges.

Why We Are Sunsetting Managed AI APIs

The phase-out of the Managed AI APIs is a reflection of our commitment to enhancing our core serverless offerings, which allow greater flexibility and control. By focusing on these core services, we can ensure that our platform remains agile and responsive to the latest technological advancements and user needs.

Alternatives for Users

We are dedicated to making this transition as smooth as possible for our users. Here’s how you can continue to leverage AI functionalities within our Serverless platform, with minimal setup and maximum flexibility:

Simplified Guide to Leveraging Serverless AI Functionalities
  1. Identify Your Needs: Determine the specific AI functionalities that are essential for your applications.
  2. Deploy with Ease: Use our Serverless platform to deploy your AI models without the complexity of managing infrastructure. Explore our easy-to-use quick deploy option.
  3. Customize as Needed: Tailor your setup to meet the unique demands of your projects, with support from our team if needed.

Looking Forward

This change is part of our broader strategy to empower users with more control over their AI deployments while streamlining our own resource allocation. Our Serverless platform will continue to support the creation and management of user-defined endpoints, ensuring you have the tools needed to succeed.

Engage with Us

We invite our users to engage with us for support during this transition. Our team is ready to provide assistance and ensure that your shift to a more customizable serverless environment is successful.

Conclusion

This evolution of our service offerings is a significant step towards a more flexible and user-centric approach. As we sunset the Managed AI APIs, we're enhancing our Serverless solutions to provide you with the freedom and tools to innovate and excel.

Deploy a Serverless Instance on Runpod

Author profile: Justin Merrell