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Machine Learning · head to head

AWS SageMaker vs DNAnexus

AWS SageMaker logo

AWS SageMaker

Machine Learning

Build, train, and deploy machine learning models at scale

From
Free
Rated
-
Full reviewWebsite
DNAnexus logo

DNAnexus

Research

Regulated cloud platform for genomic and multiomic data, with GxP and FedRAMP coverage

From
On request
Rated
-
Full reviewWebsite

The short version

  • Only AWS SageMaker has a free tier, so it costs nothing to try first.
  • Each has a real cost: AWS SageMaker vendor lock-in to AWS ecosystem makes migration to other platforms difficult; DNAnexus no pricing is published, so an organisation cannot compare the total cost against running the same pipelines on its own cloud account until it is deep in a sales process.
  • They diverge on capability: AWS SageMaker covers Jupyter notebooks, DNAnexus covers Governed data storage.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where AWS SageMaker and DNAnexus differ
AttributeAWS SageMakerDNAnexus
Starting priceFreeOn request
Pricing modelUnknownquote
Free tierYesNo
PlatformsWebWeb, Linux
CategoryMachine LearningResearch
Founded2006Unknown

Identical on both: 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 DNAnexus

  • Governed data storage
  • Workflow execution
  • App building
  • Controlled collaboration
  • Compliance posture
  • Cohort browsing

What people use each for

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

AWS SageMaker

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

DNAnexus

  • A clinical diagnostics laboratory that needs a CLIA and CAP aligned environment for production sequencing pipelines without building the compliance evidence itselfnot AWS SageMaker
  • A pharma sponsor analysing genomic endpoints in a registrational programme where 21 CFR Part 11 records are requirednot AWS SageMaker
  • A biobank or consortium giving hundreds of external researchers governed access to a dataset too large to distributenot AWS SageMaker
  • A diagnostics vendor packaging its pipeline as a versioned app so partner laboratories can run it without receiving the source codenot 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

DNAnexus

  • No pricing is published, so an organisation cannot compare the total cost against running the same pipelines on its own cloud account until it is deep in a sales process.
  • Compute and storage are billed through the platform rather than at underlying cloud list price, so the compliance envelope carries a persistent margin on every terabyte and core hour, which grows with the science rather than staying fixed.
  • Data egress at genomic scale is slow and chargeable, so leaving the platform later is a real project rather than a contract decision, and that gravity weakens negotiating position at renewal.
  • The regulated and validated configurations sit in a higher commercial tier than the research configuration, so an organisation that starts in research mode and later needs GxP faces a repricing rather than a setting change.
  • Bioinformaticians used to running Nextflow on their own infrastructure find the platform abstractions constraining, and porting an existing pipeline into the app model is engineering work that does not carry over to any other platform.

Pricing, plan by plan

AWS SageMaker

Free

No published plan breakdown. See the AWS SageMaker review.

DNAnexus

On request
  • DNAnexus Platform$undefined/year
    • Quoted per organisation through a sales process
    • Storage and compute billed through the platform rather than at cloud list price
    • Regulated and validated configurations are a separate commercial tier

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 DNAnexus if

  • You need governed data storage.
  • You work on Web, Linux.
  • You also want workflow execution.
DNAnexus review

Questions people ask

Is AWS SageMaker or DNAnexus better?
Neither clearly leads. AWS SageMaker starts at Free and DNAnexus at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, AWS SageMaker or DNAnexus?
AWS SageMaker has a free tier; the other does not. Paid plans start at Free for AWS SageMaker and On request for DNAnexus.
Does AWS SageMaker or DNAnexus run on more platforms?
AWS SageMaker runs on Web. DNAnexus runs on Web, Linux.
Can I use AWS SageMaker for free?
Yes. AWS SageMaker has a free tier, so you can try it without paying. DNAnexus 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 DNAnexus is typically brought in for.
What can AWS SageMaker do that DNAnexus cannot?
AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. DNAnexus covers Governed data storage, Workflow execution, App building, Controlled collaboration.

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
DNAnexus: Why pay for DNAnexus instead of raw AWS?

For the compliance evidence. GxP, FedRAMP, CLIA, CAP and Part 11 coverage removes a validation programme most organisations would otherwise have to build and maintain themselves.

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
DNAnexus: Is validated use included?

No. Regulated and validated configurations sit in a separate commercial tier from ordinary research use.

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
DNAnexus: Can I get my data out?

Yes, but egress at genomic scale is chargeable and slow, so plan exit costs at the point of signing.

DNAnexus: Does it host UK Biobank?

Yes, DNAnexus hosts the UK Biobank research analysis platform, which has its own access approval and cost terms.

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