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Machine Learning · head to head

AWS SageMaker vs Terra

AWS SageMaker logo

AWS SageMaker

Machine Learning

Build, train, and deploy machine learning models at scale

From
Free
Rated
-
Full reviewWebsite
Terra logo

Terra

Research

Open biomedical research platform for genomic analysis, priced only by the cloud you consume

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: AWS SageMaker vendor lock-in to AWS ecosystem makes migration to other platforms difficult; Terra the absence of a licence fee hides the real cost driver: an idle notebook virtual machine left running overnight, or a workspace bucket full of intermediate workflow outputs, quietly consumes grant money with no vendor invoice to prompt a review.
  • They diverge on capability: AWS SageMaker covers Jupyter notebooks, Terra covers No platform licence fee.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where AWS SageMaker and Terra differ
AttributeAWS SageMakerTerra
Pricing modelUnknownOpen source, no licence fee
PlatformsWebWeb, Google Cloud, Microsoft Azure
CategoryMachine LearningResearch
Founded2006Unknown

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

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 Terra

  • No platform licence fee
  • Cloud pass-through billing
  • WDL workflow execution
  • Notebook environments
  • Controlled-access data
  • Workspace sharing
  • Compliance posture
  • Multi-cloud

What people use each for

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

AWS SageMaker

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

Terra

  • A genomics lab running whole-genome alignment and variant calling pipelines without provisioning or maintaining a local clusternot AWS SageMaker
  • A consortium sharing controlled-access human data across institutions where the data cannot be copied to each sitenot AWS SageMaker
  • A health system analysing patient genomic data under a HIPAA business associate agreementnot AWS SageMaker
  • A federal or federally funded project needing FedRAMP Moderate authorisation for its analysis environmentnot 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

Terra

  • The absence of a licence fee hides the real cost driver: an idle notebook virtual machine left running overnight, or a workspace bucket full of intermediate workflow outputs, quietly consumes grant money with no vendor invoice to prompt a review.
  • Cost governance is entirely the customer responsibility, and grant-funded groups without a cloud engineer routinely overspend before anyone reads the Google or Azure bill in enough detail to find the cause.
  • It assumes genuine bioinformatics competence: writing or adapting WDL workflows, managing data tables and debugging failed tasks are all on you, so a wet lab without computational staff cannot use it productively.
  • Feature parity between the Google Cloud and Azure deployments is not identical, so a decision on cloud provider constrains which capabilities and datasets you can reach and is expensive to reverse later.
  • It is a research platform rather than a regulated clinical system: it is not a validated GxP environment and does not carry the qualification package a diagnostic or submission workflow would require, so clinical use needs additional infrastructure and validation you provide yourself.

Pricing, plan by plan

AWS SageMaker

Free

No published plan breakdown. See the AWS SageMaker review.

Terra

Free
  • Terra platformFree
    • No charge for the platform, administration, security controls or frontline support
    • Workspaces, workflow execution and notebook environments included
    • Available on Google Cloud and Microsoft Azure
  • Cloud consumption$undefined/month
    • Google Cloud or Azure charges passed through at cost with no markup
    • Running notebook virtual machines around 0.22 USD per hour, roughly 0.027 USD per hour when paused
    • Storage billed continuously on workspace buckets and intermediate workflow outputs

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 Terra if

  • You need no platform licence fee.
  • You want to start without paying.
  • You work on Web, Google Cloud, Microsoft Azure.
  • You also want cloud pass-through billing.
Terra review

Questions people ask

Is AWS SageMaker or Terra better?
Neither clearly leads. AWS SageMaker starts at Free and Terra at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, AWS SageMaker or Terra?
AWS SageMaker starts at Free and Terra at Free.
Does AWS SageMaker or Terra run on more platforms?
AWS SageMaker runs on Web. Terra runs on Web, Google Cloud, Microsoft Azure.
Can I use AWS SageMaker for free?
Both have a free tier, so you can try either at no cost before committing.
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 Terra is typically brought in for.
What can AWS SageMaker do that Terra cannot?
AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. Terra covers No platform licence fee, Cloud pass-through billing, WDL workflow execution, Notebook environments.

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
Terra: Is Terra really free?

The platform is. There is no licence fee. Everything you pay is Google Cloud or Azure compute, storage and egress, passed through at cost.

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
Terra: What actually drives the bill?

Running virtual machines and stored data. Pausing notebooks drops a virtual machine from about 0.22 to about 0.027 dollars an hour, and deleting intermediate workflow outputs is the other main saving.

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
Terra: Can I use it with protected health information?

Yes. Terra signs HIPAA business associate agreements and GDPR data processing agreements, and the Azure deployment holds FedRAMP Moderate authorisation.

Terra: Is it validated for clinical or GxP use?

No. It is a research platform. Regulated clinical or submission workflows need validation and controls you build yourself.

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