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DNAnexus vs Google Vertex AI

DNAnexus logo

DNAnexus

Research

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

From
On request
Rated
-
Full reviewWebsite
Google Vertex AI logo

Google Vertex AI

Machine Learning

Unified ML platform to build, deploy, and scale AI models

From
On request
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: 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.; Google Vertex AI vendor lock-in to Google Cloud ecosystem makes migration to other platforms difficult
  • They diverge on capability: DNAnexus covers Governed data storage, Google Vertex AI covers AutoML.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which DNAnexus and Google Vertex AI actually diverge.

Attributes where DNAnexus and Google Vertex AI differ
AttributeDNAnexusGoogle Vertex AI
Pricing modelquoteUnknown
PlatformsWeb, LinuxCloud, Web
CategoryResearchMachine Learning
FoundedUnknown2008

Identical on both: starting price (On request), free tier (No), 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 DNAnexus

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

Only in Google Vertex AI

  • AutoML
  • Custom training
  • Feature Store
  • Model monitoring
  • Prediction serving
  • BigQuery
  • Cloud Storage
  • TensorFlow

What people use each for

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

DNAnexus

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

Google Vertex AI

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

Where each one falls short

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

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.

Google Vertex AI

  • Vendor lock-in to Google Cloud ecosystem makes migration to other platforms difficult
  • Requires familiarity with Google Cloud Platform infrastructure and concepts
  • Cost can escalate quickly with large training and inference workloads

Pricing, plan by plan

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

Google Vertex AI

On request

No published plan breakdown. See the Google Vertex AI review.

Which should you pick?

Choose DNAnexus if

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

Choose Google Vertex AI if

  • You need automl.
  • You work on Cloud, Web.
  • You also want custom training.
Google Vertex AI review

Questions people ask

Is DNAnexus or Google Vertex AI better?
Neither clearly leads. DNAnexus starts at On request and Google Vertex AI at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, DNAnexus or Google Vertex AI?
DNAnexus starts at On request and Google Vertex AI at On request.
Does DNAnexus or Google Vertex AI run on more platforms?
DNAnexus runs on Web, Linux. Google Vertex AI runs on Cloud, Web.
What is DNAnexus best used for?
DNAnexus is most often used for a clinical diagnostics laboratory that needs a clia and cap aligned environment for production sequencing pipelines without building the compliance evidence itself, a pharma sponsor analysing genomic endpoints in a registrational programme where 21 cfr part 11 records are required, a biobank or consortium giving hundreds of external researchers governed access to a dataset too large to distribute, a diagnostics vendor packaging its pipeline as a versioned app so partner laboratories can run it without receiving the source code. Of those, a clinical diagnostics laboratory that needs a clia and cap aligned environment for production sequencing pipelines without building the compliance evidence itself and a pharma sponsor analysing genomic endpoints in a registrational programme where 21 cfr part 11 records are required are not what Google Vertex AI is typically brought in for.
What can DNAnexus do that Google Vertex AI cannot?
DNAnexus covers Governed data storage, Workflow execution, App building, Controlled collaboration. Google Vertex AI covers AutoML, Custom training, Feature Store, Model monitoring.

Answered from the vendors’ own pages

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.

Google Vertex AI: What is the pricing model for Google Vertex AI?

Vertex AI uses a pay-as-you-go model with no upfront costs or lock-in fees. Costs vary by service: training is billed by compute resources and time (30-second increments), online predictions by machine type per hour, and batch predictions by compute time or per-record for specific AutoML types.

Source
DNAnexus: Is validated use included?

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

Google Vertex AI: What types of data can Vertex AI handle?

Vertex AI supports image, video, text, and tabular data types with tools for uploading, storing, and managing large datasets.

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.

Google Vertex AI: Does Vertex AI support custom model training?

Yes. Vertex AI supports both AutoML for automated machine learning and custom training code in Python, R, and other languages.

Source
DNAnexus: Does it host UK Biobank?

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

Google Vertex AI: What deployment options are available in Vertex AI?

Vertex AI supports online predictions for real-time use cases and batch predictions for large-scale processing.

Source
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