Sold by: Elm Computing
Deployed on AWS
Free Trial
AWS Free Tier
JupyterHub Server AMI (x86_64), published by Elm Computing.
Overview
This is a repackaged open source software product wherein additional charges apply for support by Elm Computing.
Disclaimer: All trademarks referenced in this listing belong to their respective owners. Their use does not imply any affiliation with or endorsement by the trademark holders.
JupyterHub is best suited to serve Jupyter notebook for multiple users. With the diverse EC2 instances, JupyterHub can find various applications such as course teaching, data mining, or scientific research simulations. For details, please visit docs.elmcomputing.io.
Highlights
- Pre-configured JupyterHub
- Popular data science packages
- Hundreds of Python packages
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 26.04
Deployed on AWS
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Pricing
Free trial
Try this product free for 7 days according to the free trial terms set by the vendor. Usage-based pricing is in effect for usage beyond the free trial terms. Your free trial gets automatically converted to a paid subscription when the trial ends, but may be canceled any time before that.
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 |
|---|---|
c5.2xlarge Recommended | $0.10 |
t2.micro | $0.10 |
t3.micro | $0.10 |
x1e.32xlarge | $0.10 |
c5a.24xlarge | $0.10 |
g4dn.8xlarge | $0.10 |
m7a.large | $0.10 |
m5dn.16xlarge | $0.10 |
c6a.12xlarge | $0.10 |
z1d.xlarge | $0.10 |
Dimensions summary
You pay by the hour for JupyterHub Server, billed on top of your AWS compute usage. The charge you see covers Elm Computing's support for this open-source software; standard AWS infrastructure costs apply separately. Each dimension maps to a specific EC2 instance type, so your rate scales with the machine you choose. Options range from small shared instances like t2.nano and t3.medium up to large compute-, memory-, and GPU-focused instances such as p5.48xlarge and r7a.metal-48xl. Pick the instance that fits your workload; there is no upfront commitment, and you pay only for hours you run.
Top-of-mind questions for buyers
The hourly charge covers Elm Computing's support for the JupyterHub Server software, which is repackaged open-source software. You pay standard AWS compute, storage, and network fees separately. Both appear on your AWS bill. The software rate depends on the EC2 instance type you select for your chosen dimension.
The software rate meters running hours only. When you stop the instance, the hourly software charge stops accruing. Stopped instances may still incur AWS storage fees for attached volumes, but that is separate from the software charge you see here. You pay only for hours the instance runs.
Each dimension maps to one EC2 instance type. To change capacity, you switch to a different dimension, such as a GPU-focused instance like p5.48xlarge or a memory-focused one like r7a.metal-48xl. Your hourly software rate then follows the instance you run. There is no upfront commitment.
docs.elmcomputing.io+1
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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
Ubuntu 26.04.1 x86_64 CPU image with Python 3.12.14, JupyterHub 6.0.1, JupyterLab 4.6.4, Dash 4.4.1 with the built JupyterLab extension, and PyTorch 2.14.0+cpu. Updated and locked the Python package stack; validated administrator and approved-user login and spawn.
Additional details
Usage instructions
The details can be found at https://docs.elmcomputing.io/ami/x86/ami.html .
Resources
Vendor resources
Support
Vendor support
Please contact us at support@elmcomputing.io if any assistance is needed.
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.
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Multi-User Notebook Server
Pre-configured JupyterHub environment for serving Jupyter notebooks to multiple concurrent users
Data Science Package Ecosystem
Popular data science packages included for analytics and scientific computing workflows
Python Package Library
Hundreds of Python packages pre-installed for development and data processing tasks
EC2 Instance Compatibility
Support for diverse EC2 instance types enabling flexible deployment across various computational requirements
AMI-based Deployment
x86_64 architecture Amazon Machine Image for streamlined provisioning and infrastructure deployment
R and Python Integration
R 4.6.1 built from source against OpenBLAS 0.3.29 with runtime CPU dispatch, 4,952 packages (2,043 Bioconductor, 2,878 CRAN), Python 3.12.3 with numpy, scipy, pandas, scikit-learn and statsmodels bridged to R through reticulate
Parallel Computing Framework
Open MPI 4.1.6 with Rmpi, pbdMPI, doMPI, snow, foreach, doParallel, future and BiocParallel, delivering 4.68x speedup across 8 cores and 184 GFLOPS on threaded OpenBLAS matrix operations
Provenance-Tracked Memory System
PostgreSQL 17 with pgvector backend enabling lineage tracking of analytical results, result superseding without data destruction, and semantic retrieval capabilities
Web-Based Development Interfaces
RStudio Server 2026.08.1 and Shiny Server 1.5.23 for interactive analysis and application development
Offline Security Architecture
Entirely offline operation with all services bound to localhost, SSH key-based access, and per-instance generated credentials at first boot with no embedded credentials in the image
Browser-Based Remote Access
Full-featured Ubuntu 22.04 LTS Desktop environment accessible through a web browser without requiring client software installation.
Remote Desktop Protocol Support
Native Remote Desktop client connectivity enabling workspace access through remote desktop protocol.
Integrated Development Environments
Pre-installed latest versions of Jupyter notebooks, RStudio Server, Visual Studio Code, and Google Chrome for development and data science workflows.
Multi-User Concurrent Access
Multi-user capable environment supporting large numbers of concurrent users on appropriately sized instances.
Container and Virtualization Support
Docker pre-installed for containerization and application deployment capabilities.
Standard contract
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