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Head-to-head · Data Science Analytics

Amazon SageMaker AI vs Apache Zeppelin

  • Updated Oct 2026
  • Both researched from official sources
  • 2 checks side by side
Higher score Amazon SageMaker AI #2 in Data Science Analytics 9.1/10 Paid ✓ 0 of 5 features Visit AWS
Apache Zeppelin #7 in Data Science Analytics 8.6/10 Open source ✓ 1 of 5 features Try Apache Zeppelin

Amazon SageMaker AI leads on 0 checks, Apache Zeppelin on 0, and 2 are even. Who comes out ahead on the 2 yes/no, price and count checks where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreAmazon SageMaker AI · 9.1/10
  • Most featuresApache Zeppelin · 1 of 5

Amazon SageMaker AI scores higher on our rubric for data science analytics: 9.1 against 8.6 out of 10; our editors rank them #2 and #7.

Amazon SageMaker AI is the better fit for teams running production ML on AWS. Apache Zeppelin is the better fit for teams using Spark and interactive notebooks.

  • Amazon SageMaker AI fits best

    Teams running production ML on AWS

  • Apache Zeppelin fits best

    Teams using Spark and interactive notebooks

Advertiser disclosure: iTechGuides is reader-supported. Vendors can pay for top positions in our rankings and for a place on other products' pages, and we may earn a commission when you click some links. Bought places stand at the number bought; this page and our disclosure say so. Paying vendors may get a fuller, more detailed review from our editors, and that review can change their score. How we rank.

Side by side

Feature Amazon SageMaker AI 9.1/10 Visit ↗ Apache Zeppelin 8.6/10 Visit ↗
At a glance
Editor score 9.1 8.6
Ranking #2 in Data Science Analytics #7 in Data Science Analytics
Best for Teams running production ML on AWS Teams using Spark and interactive notebooks
Pricing model Paid Free
Starting price Not checked yet Open source
Free plan — Not checked yet
Free trial — —
Deployment Cloud Self-hosted
Platforms Web Web, macOS, Linux
Support Docs Community, Docs
Integrations 6 integrations 14 integrations
Built for Small business, Mid-market, Enterprise Small business, Mid-market, Enterprise
Features Amazon SageMaker AI 0/5 · Apache Zeppelin 1/5
AutoML Not checked yet Not checked yet
Model deployment Not checked yet Not checked yet
GPU compute Not checked yet ✓ (best)
SQL workspace Not checked yet Not checked yet
Dashboarding Not checked yet Not checked yet
Specs
Deployment model Not checked yet Not checked yet
Primary interface Not checked yet Not checked yet
Our review
Pros
  • Managed training supports distributed workloads.
  • Pipelines, experiment tracking, and a model registry support repeatable ML workflows.
  • Real-time, serverless, asynchronous, and batch inference offer deployment choices.
  • Built-in Spark integration plus pluggable language and data interpreters
  • Real-time collaboration, notebook sharing, and Git-based versioning
  • REST API and Kubernetes interpreter pods with optional GPU requests
Cons
  • Usage-based charges across compute, storage, and data processing can be hard to forecast.
  • AWS Free Tier use lasts only during the first two months.
  • Its verified integrations are centered on AWS, limiting fit for other ecosystems.
  • Self-hosted deployment means teams manage their own environment
  • Notebook workflows may not suit teams seeking end-to-end model lifecycle tools
  • Support is through community and documentation
Our verdict

Amazon SageMaker AI brings model development, training, tracking, and deployment into a managed AWS service for data scientists, developers, and ML engineers. Its web-based Studio environment supports end-to-end model development, while…

Read the review →

Apache Zeppelin is a self-hosted, web-based notebook for interactive data analytics, visualization, and collaboration. It is aimed at teams that work in Spark and other notebook-based environments, from small groups to enterprise teams.…

Read the review →
  1. Amazon SageMaker AIData Science Analytics 9.1Paid
  2. Apache ZeppelinData Science Analytics 8.6Open source

Strengths and trade-offs

  • Amazon SageMaker AI — where it wins

    • Managed training supports distributed workloads.
    • Pipelines, experiment tracking, and a model registry support repeatable ML workflows.
    • Real-time, serverless, asynchronous, and batch inference offer deployment choices.

    Where it doesn't

    • Usage-based charges across compute, storage, and data processing can be hard to forecast.
    • AWS Free Tier use lasts only during the first two months.
    • Its verified integrations are centered on AWS, limiting fit for other ecosystems.

  • Apache Zeppelin — where it wins

    • Built-in Spark integration plus pluggable language and data interpreters
    • Real-time collaboration, notebook sharing, and Git-based versioning
    • REST API and Kubernetes interpreter pods with optional GPU requests

    Where it doesn't

    • Self-hosted deployment means teams manage their own environment
    • Notebook workflows may not suit teams seeking end-to-end model lifecycle tools
    • Support is through community and documentation
  • Amazon SageMaker AI9.1/10 · Paid

    A production-focused ML service with managed workflows and multiple inference options.

    Visit AWSFull verdict →
  • Apache Zeppelin8.6/10 · Open source

    A self-hosted notebook for teams working with Spark, interpreters, and shared analysis.

    Try Apache ZeppelinFull verdict →

More comparisons

All data science analytics comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update