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Machine Learning · head to head

AWS SageMaker vs Teradata

AWS SageMaker logo

AWS SageMaker

Machine Learning

Build, train, and deploy machine learning models at scale

From
Free
Free plan
Yes
Visit AWS SageMakerFull review
Teradata logo

Teradata

Databases

Long-established enterprise MPP data warehouse, rebranded in 2026 as the Autonomous Knowledge Platform, sold for cloud, on-premises and hybrid.

From
On request
Free plan
No
Visit TeradataFull review

The short version

  • AWS SageMaker starts at $0.04/mo; Teradata prices on request.
  • Only AWS SageMaker lists jupyter notebooks; only Teradata lists massively parallel architecture.
  • Where AWS SageMaker falls short: vendor lock-in to AWS ecosystem makes migration to other platforms difficult.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

Only the attributes on which AWS SageMaker and Teradata actually diverge.

Attributes where AWS SageMaker and Teradata differ
AttributeAWS SageMakerTeradata
Starting priceFreeOn request
Free tierYesNo
CategoryMachine LearningDatabases

Identical on both: platforms (Web).

Not yet recorded: pricing model for AWS SageMaker, founded for Teradata.

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in AWS SageMaker

  • Jupyter notebooks
  • Built-in algorithms
  • Automatic model tuning
  • One-click deployment
  • Model monitoring
  • S3
  • Lambda
  • Step Functions

Only in Teradata

  • Massively parallel architecture
  • Workload management
  • Mature cost-based optimiser
  • Cloud, on-premises and hybrid
  • Bulk load utilities
  • BTEQ scripting
  • In-database analytics
  • Enterprise Vector Store

What people use each for

The jobs each tool is most often brought in to do.

AWS SageMaker

  • Machine learningnot Teradata
  • Data analysisnot Teradata
  • Model trainingnot Teradata
  • Predictive analyticsnot Teradata

Teradata

  • A large existing Teradata estate where the practical question is which workloads to migrate first rather than whether to adoptnot AWS SageMaker
  • High-concurrency mixed workloads where hundreds of analysts and scheduled jobs contend and predictable prioritisation matters more than peak single-query speednot AWS SageMaker
  • Regulated reporting where the same query must produce the same answer for years and the audit trail of the existing implementation has valuenot AWS SageMaker
  • Hybrid deployments where regulatory or data-residency rules keep a portion of the warehouse on-premises while the rest moves to cloudnot AWS SageMaker

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

AWS SageMaker

  • Vendor lock-in to AWS ecosystem makes migration to other platforms difficult
  • Opaque pricing can lead to unexpected expenses like forgotten EBS volume charges
  • Does not include native job scheduling, requiring Lambda or EventBridge integration

Teradata

  • Licensing is negotiated rather than published, so there is no way to compare total cost against a consumption-priced warehouse without entering a sales cycle, and the comparison is only ever as good as the workload profile you gave them
  • The SQL dialect and the loading utilities are Teradata-specific, so every stored procedure, macro and BTEQ script written against the platform is migration debt that grows with each release you ship
  • Primary index choice determines data distribution, and a poorly chosen index concentrates rows on a few processing units, which surfaces as one slow query rather than an error and needs a specialist to diagnose
  • The skills market is contracting, so DBA and workload-management expertise is expensive to hire, hard to replace when someone retires, and increasingly hard to buy from consultancies whose own bench has moved to cloud warehouses
  • The 2026 renaming of Vantage, VantageCloud, ClearScape and QueryGrid split documentation, runbooks and vendor material across two naming systems, so searching for an error or a configuration now returns results for a product that is described under a different name

Pricing, plan by plan

AWS SageMaker

Free

No published plan breakdown. See the AWS SageMaker review.

Teradata

On request

No published plan breakdown. See the Teradata review.

Which should you pick?

Choose AWS SageMaker if

  • You need jupyter notebooks.
  • You want to start without paying.
  • You also want built-in algorithms.
AWS SageMaker review

Choose Teradata if

  • You need massively parallel architecture.
  • You also want workload management.
Teradata review

Questions people ask

Is AWS SageMaker or Teradata better?
Neither clearly leads. AWS SageMaker starts at Free and Teradata at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, AWS SageMaker or Teradata?
AWS SageMaker has a free tier; the other does not. Paid plans start at Free for AWS SageMaker and On request for Teradata.
Does AWS SageMaker or Teradata run on more platforms?
Both run on Web, so platform support will not decide this one for you.
Can I use AWS SageMaker for free?
Yes. AWS SageMaker has a free tier, so you can try it without paying. Teradata starts at On request.
What is AWS SageMaker best used for?
AWS SageMaker is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Teradata is typically brought in for.
What can AWS SageMaker do that Teradata cannot?
AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. Teradata covers Massively parallel architecture, Workload management, Mature cost-based optimiser, Cloud, on-premises and hybrid.

Answered from the vendors’ own pages

AWS SageMaker: What is AWS SageMaker used for?

AWS SageMaker is a machine learning service for building, training, and deploying ML models at scale. It provides tools for data preparation, model training, inference endpoints, and performance optimization.

Source
Teradata: Is Teradata only on-premises?

No. It is sold for cloud, on-premises and hybrid deployment, and the cloud offering is now branded Teradata Cloud. A large part of the installed base is still on-premises or hybrid.

AWS SageMaker: How is AWS SageMaker priced?

SageMaker uses pay-as-you-go pricing with no upfront costs or long-term commitments. Pricing starts at $0.04 per hour for basic notebook instances and scales based on instance type. ML Savings Plans offer up to 64% off with hourly spend commitments.

Source
Teradata: How does it compare to Snowflake or BigQuery?

On raw elasticity and cost transparency the cloud warehouses win. On mixed-workload concurrency management against a large existing query estate Teradata is still hard to replace, which is why migrations off it take years rather than quarters.

AWS SageMaker: Does AWS SageMaker have a free tier?

Yes, the free tier includes 250 hours of notebook usage, 50 hours of training, and 125 hours of hosting on ml.t3.medium instances during the first two months.

Source
Teradata: Why do organisations stay on it?

Because the cost of leaving is the estate, not the data. Thousands of procedures, scripts and extracts written in a proprietary dialect have to be rewritten and revalidated, and in regulated reporting that revalidation is the expensive part.

Teradata: What changed in the 2026 rebrand?

Vantage became the Autonomous Knowledge Platform, VantageCloud became Teradata Cloud, ClearScape Analytics became AI Studio and QueryGrid became Fabric. The underlying products are continuous with what came before.

Teradata: Can it handle AI and vector workloads?

It has added an Enterprise Vector Store and in-database analytics branded AI Studio. Whether that is preferable to moving the data into a purpose-built vector store depends on how much of your data already lives in the warehouse.

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