Run your own
GPT-Neo
Whisper
Gradio
StableLM
Mosec
Streamlit
LLaMA
BLOOM
GPT-Neo
on cloud
in seconds.
Deploy your machine learning models effortlessly on a pay-as-you-go serverless infrastructure with ModelZ.
How it works

Spend more time perfecting AI with your application and less time on infrastructure.

Step 01

Build and push

Build the inference server and push it to an docker registry, or use our pre-built templates directly.
Step 02

Create deployment

Write your prediction code and deploy it to the cloud. You could use one of our templates to get started or build and deploy a new deployment from scratch.
Step 03

Make predictions

The easiest way to make predictions is to use the ModelZ SDK. You could also use the curl command to make predictions.

ModelZ provides the following features out-of-the-box

Auto scaling
Serverless architecture enables us to easily scale seemlessly from zero, up or down, according to your needs. This allows us to provide a reliable and scalable solution for deploying and prototyping machine learning applications at any scale.

Rich ecosystem

ModelZ is designed for machine learning workloads, providing support for popular ML serving frameworks like mosec and user-friendly UI tools like Gradio and Streamlit, which facilitate easy model deployment and prototyping with interactive UIs.

DevOps (Coming soon)

At ModelZ, we are dedicated to being a developer-first platform. In line with this, we are currently working on supporting OpenAPI for model operations, to enable developers to seamlessly integrate their models into existing workflows and systems.

Pricing model that’s best for you

No upfront fees and long-term commitments, making it a cost-effective solution for machine learning needs.

Pay as you go

Just pay for what you use. No more paying for cold starts and idle servers.

Our domain experience

The ModelZ founding team has years of experience building ML infrastructure at AWS, Tiktok, Shopee, Tencent, and the open-source community.
creators of

envd

envd (ɪnˈvdɪ) is a command-line tool that helps you create the container-based development environment for AI/ML.
View project

Mosec

High-performance and flexible model serving framework for building ML model-enabled backend and microservices.
View project
Other contributions
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