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Head-to-head · Low-Code Machine Learning Platforms Software

SAS Viya vs Huawei ModelArts

  • Updated Sep 2026
  • Both researched from official sources
  • 1 check side by side
Higher score SAS Viya #5 in Low-Code Machine Learning Platforms Software 7.1/10 Pricing on request · 14-day trial ✓ 0 of 2 features Visit SAS Viya

SAS Viya leads on 0 checks, Huawei ModelArts on 0, and 1 is even. Who comes out ahead on the 1 yes/no, price and count check where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreSAS Viya · 7.1/10

SAS Viya scores higher on our rubric for low-code machine learning platforms software: 7.1 against 6.2 out of 10; our editors rank them #5 and #9.

SAS Viya is the better fit for enterprises governing ML across environments. Huawei ModelArts is the better fit for huawei Cloud users seeking visual ML lifecycle tools.

  • SAS Viya fits best

    Enterprises governing ML across environments

  • Huawei ModelArts fits best

    Huawei Cloud users seeking visual ML lifecycle tools

Advertiser disclosure: iTechGuides is reader-supported. We may earn a commission when you click some links. It never changes our verdict. How we rank.

Side by side

Feature SAS Viya 7.1/10 Visit ↗ Huawei ModelArts 6.2/10 Visit ↗
At a glance
Editor score 7.1 6.2
Ranking #5 in Low-Code Machine Learning Platforms Software #9 in Low-Code Machine Learning Platforms Software
Best for Enterprises governing ML across environments Huawei Cloud users seeking visual ML lifecycle tools
Pricing model Paid Paid
Starting price Not published Not published
Free plan — Not published
Free trial — —
Deployment Cloud, Self-hosted Cloud
Platforms Web Web
Support Email, Live chat, Phone, Tickets, Community, Docs Tickets, Docs
Built for Enterprise, Mid-market Small business, Mid-market, Enterprise
Features SAS Viya 0/2 · Huawei ModelArts 0/2
Automated ML Not published Not published
Visual workflows Not published Not published
Specs
Deployment targets Not published Not published
Supported model types Not published Not published
Data source connectors Not published Not published
Access platforms Not published Not published
Our review
Pros
  • Combines visual and code-based modeling with automated ML and forecasting
  • Supports validation, monitoring, retraining, explainability, and fairness testing
  • Deploys models across cloud, on-premises, in-database, and edge environments
  • Low-code workflows span data, training, evaluation, and deployment
  • Tracks experiments, model versions, datasets, and training results
  • Supports real-time, batch, and edge inference deployment
Cons
  • Pricing requires contacting sales
  • No free plan is offered
  • Its broad governance and deployment scope may exceed simpler ML needs
  • Deployment is cloud-only
  • Compute costs vary by specification, node count, duration, and region
  • Resource planning is required for dedicated pools, Lite Clusters, or Lite Servers
Our verdict

SAS Viya brings data management, analytics, and AI into a platform for organizations building and governing predictive models. Its mix of visual and code-based statistical modeling, automated machine learning, forecasting, and model…

Read the review →

Huawei ModelArts is Huawei Cloud’s cloud-based AI development platform for AI developers, data scientists, and organizations building or operating machine-learning or foundation-model applications. It covers data preparation, notebook…

Read the review →
  1. SAS ViyaLow-Code Machine Learning Platforms Software 7.1Pricing on request · 14-day trial
  2. Huawei ModelArtsLow-Code Machine Learning Platforms Software 6.2Paid

Strengths and trade-offs

  • SAS Viya — where it wins

    • Combines visual and code-based modeling with automated ML and forecasting
    • Supports validation, monitoring, retraining, explainability, and fairness testing
    • Deploys models across cloud, on-premises, in-database, and edge environments

    Where it doesn't

    • Pricing requires contacting sales
    • No free plan is offered
    • Its broad governance and deployment scope may exceed simpler ML needs
  • Huawei ModelArts — where it wins

    • Low-code workflows span data, training, evaluation, and deployment
    • Tracks experiments, model versions, datasets, and training results
    • Supports real-time, batch, and edge inference deployment

    Where it doesn't

    • Deployment is cloud-only
    • Compute costs vary by specification, node count, duration, and region
    • Resource planning is required for dedicated pools, Lite Clusters, or Lite Servers

More comparisons

Reviewed by iTechGuides Editors · Editorial team · Updated Sep 2026

Last updated · How we research and update