Sold by: kCloudHubs LLC
Deployed on AWS
AWS Free Tier
This product has charges associated with it for support and maintenance. ONNX is an open-source format used to represent machine learning and deep learning models so they can run across different frameworks and platforms.
Overview
ONNX 1.21.0 on Ubuntu 26.04 with Free Maintenance Support by kCloudHubs
kCloudHubs provides ONNX 1.21.0 on Ubuntu 26.04, packaged as a ready-to-deploy AWS Marketplace AMI for machine learning model interoperability, deployment, and inference workloads. ONNX (Open Neural Network Exchange) is an open-source standard for representing machine learning models, enabling portability across different AI frameworks, runtimes, and hardware platforms.
Tech Use Cases
- Cross-platform machine learning model deployment
- AI and deep learning inference workloads
- Model exchange between different machine learning frameworks
- Edge AI, cloud AI, and production inference environments
- Model portability across supported runtimes and hardware accelerators
Available Now on AWS Marketplace
ONNX 1.21.0 on Ubuntu 26.04 with Maintenance Support by kCloudHubs is a preconfigured Amazon Machine Image (AMI) designed to simplify deployment of ONNX-based machine learning workloads on AWS. The solution provides a production-ready environment for model interoperability, validation, and inference while reducing setup and configuration time.
Key Features
- ONNX 1.21.0 preinstalled and configured
- Open standard for machine learning model interoperability
- Cross-framework model portability and exchange
- Support for scalable inference and deployment workflows
- Ready-to-launch AWS EC2 AMI environment
- Ubuntu 26.04 LTS foundation for stability and reliability
Supporting Version
- ONNX 1.21.0
AWS Marketplace Setup
- Preconfigured Amazon Machine Image (AMI)
- Launch directly on Amazon EC2
- AWS-integrated billing and subscription management
- Rapid deployment with minimal configuration requirements
Help Options
kCloudHubs provides optional assistance for installation, configuration, deployment, upgrades, and troubleshooting related to the packaged ONNX environment.
Highlights
- Standard model format for AI/ML interoperability
- Runs models across frameworks like PyTorch & TensorFlow
- Supports fast and optimized inference execution
Details
Sold by
Categories
Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 26.04
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
If you are an AWS Free Tier customer with a free plan, you are eligible to subscribe to this offer. You can use free credits to cover the cost of eligible AWS infrastructure. See AWS Free Tier for more details. If you created an AWS account before July 15th, 2025, and qualify for the Legacy AWS Free Tier, Amazon EC2 charges for Micro instances are free for up to 750 hours per month. See Legacy AWS Free Tier for more details.
Dimension | Cost/hour |
|---|---|
m4.large Recommended | $0.07 |
t2.micro | $0.01 |
t3.micro | $0.07 |
t3.nano | $0.07 |
t3.medium | $0.07 |
t2.2xlarge | $0.07 |
t2.medium | $0.07 |
t2.large | $0.07 |
r3.large | $0.07 |
r4.large | $0.07 |
Vendor refund policy
No Refund
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Packaged with latest updates as of June/2026
Additional details
Usage instructions
Connect you your instance via SSH, the username is ubuntu. More info on SSH: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/AccessingInstancesLinux.html - Run the following commands:
sudo su
cd ~/onnx-project
source onnx-env/bin/activate
pip show onnx
Support
Vendor support
Feel free to reach out anytime. Our support team is available 24x7 for assistance. Email: meha@kcloudhubs.com
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Similar products
This product has charges associated with it for support and maintenance. ONNX is an open-source format used to represent machine learning and deep learning models so they can run across different frameworks and platforms. It was developed to make AI models portable and interoperable between tools like PyTorch, TensorFlow, and others.
Self-hosted embedding server with dynamic batching. 10x faster than sentence-transformers with ONNX and TensorRT optimization. OpenAI-compatible API.
This product has charges associated with it for seller support. Ultralytics YOLOv8 is a cutting edge, open source computer vision framework for real time object detection, classification, segmentation, and tracking, optimized for speed and accuracy on both CPU and GPU.
Self-hosted ML model serving framework. Deploy any model as a production API with batching, GPU support, and auto-scaling. Supports PyTorch, TensorFlow, HuggingFace, and custom models.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.