Data Science Virtual Machines
Infrastructure as a service (IaaS) providers
- Features
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- Ease of management
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What is Data Science Virtual Machines
Data Science Virtual Machines is a managed virtual machine image offering on Microsoft Azure that provides preconfigured environments for data science and machine learning work. It targets data scientists, analysts, and developers who need common tools and libraries available quickly for experimentation, model development, and training. The service focuses on ready-to-use VM images (Windows and Linux) with curated open-source and Microsoft tooling, rather than a fully managed platform layer.
Preconfigured data science toolchain
The VM images come with a curated set of data science and machine learning tools, languages, and libraries preinstalled. This reduces time spent on environment setup and dependency management compared with building custom VM images. It is useful for short-lived projects, proofs of concept, and onboarding new team members. Users can still add packages or customize the environment as needed.
Azure VM flexibility and control
Because it runs as an Azure Virtual Machine, users retain OS-level control for custom drivers, libraries, and system configuration. It supports a wide range of VM sizes, including GPU-capable instances, which helps match compute to workload needs. It also fits scenarios that require installing proprietary software or specific versions not available in managed services. This approach aligns with IaaS patterns where teams manage the guest OS and runtime.
Integrates with Azure services
The VM can connect to other Azure services for storage, networking, identity, and monitoring using standard Azure mechanisms. This supports enterprise patterns such as using virtual networks, role-based access control, and managed disks. It can be used alongside Azure-native data and ML services when teams want a VM-based development environment. Integration is typically achieved through Azure configuration rather than product-specific connectors.
User manages patching and upkeep
As an IaaS VM, the customer remains responsible for OS updates, security patching, and ongoing maintenance. Tooling versions can drift over time, and teams may need to manage reproducibility across multiple VMs. Operational tasks such as hardening, backup strategy, and vulnerability management are not fully abstracted. This can increase overhead compared with more managed platform offerings.
Not a managed ML platform
The product provides an environment, but it does not inherently deliver end-to-end MLOps capabilities such as managed pipelines, model registry, or governed deployment workflows. Teams often need to assemble additional services or tooling for collaboration, CI/CD, and production serving. For regulated environments, governance and lineage typically require extra configuration and complementary products. As a result, it may be best suited for development and experimentation rather than full lifecycle management by itself.
Cost and sprawl risk
VM-based usage can lead to higher costs if instances are left running, especially for GPU-enabled sizes. Organizations can also accumulate multiple customized images and VMs, making standardization and access control harder. Without strong policies, resource sprawl can complicate budgeting and security reviews. Cost management depends on disciplined shutdown schedules, sizing, and governance controls.
Plan & Pricing
Pricing model: Pay-as-you-go (Azure VM compute + related Azure resources) Free tier/trial:
- Data Science Virtual Machine (DSVM) image/software plan: Free ("Software plans start at Free" for Windows and Ubuntu DSVM editions).
- Azure free account: $200 credit for 30 days (can be used toward VMs). Example costs:
- DSVM (software/image): Free (no additional software charge).
- DS14 v2 VM (example used in Azure documentation, US West 2): $1.196 per machine (used in Azure Machine Learning pricing example). Discount options:
- Azure Savings Plan for compute (1- or 3-year commitments).
- Azure Reserved Virtual Machine Instances (1- or 3-year reservations).
- Azure Hybrid Benefit (reuse existing Windows/SQL Server licenses for savings).
- Dev/Test pricing (discounted rates for Visual Studio subscribers).
- Spot Virtual Machines (deep discounts for interruptible workloads).
Seller details
Microsoft Corporation
Redmond, Washington, United States
1975
Public
https://www.microsoft.com/
https://x.com/Microsoft
https://www.linkedin.com/company/microsoft/
