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Machine Learning · head to head

Google Vertex AI vs Terra

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
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

  • Only Terra has a free tier, so it costs nothing to try first.
  • Each has a real cost: Google Vertex AI vendor lock-in to Google Cloud 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: Google Vertex AI covers AutoML, 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 Google Vertex AI and Terra actually diverge.

Attributes where Google Vertex AI and Terra differ
AttributeGoogle Vertex AITerra
Starting priceOn requestFree
Pricing modelUnknownOpen source, no licence fee
Free tierNoYes
PlatformsCloud, WebWeb, Google Cloud, Microsoft Azure
CategoryMachine LearningResearch
Founded2008Unknown

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 Google Vertex AI

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

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.

Google Vertex AI

  • 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 Google Vertex AI
  • A consortium sharing controlled-access human data across institutions where the data cannot be copied to each sitenot Google Vertex AI
  • A health system analysing patient genomic data under a HIPAA business associate agreementnot Google Vertex AI
  • A federal or federally funded project needing FedRAMP Moderate authorisation for its analysis environmentnot Google Vertex AI

Where each one falls short

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

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

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

Google Vertex AI

On request

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

  • You need automl.
  • You work on Cloud, Web.
  • You also want custom training.
Google Vertex AI 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 Google Vertex AI or Terra better?
Neither clearly leads. Google Vertex AI starts at On request and Terra at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Google Vertex AI or Terra?
Terra has a free tier; the other does not. Paid plans start at On request for Google Vertex AI and Free for Terra.
Does Google Vertex AI or Terra run on more platforms?
Google Vertex AI runs on Cloud, Web. Terra runs on Web, Google Cloud, Microsoft Azure.
Can I use Terra for free?
Yes. Terra has a free tier, so you can try it without paying. Google Vertex AI starts at On request.
What is Google Vertex AI best used for?
Google Vertex AI 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 Google Vertex AI do that Terra cannot?
Google Vertex AI covers AutoML, Custom training, Feature Store, Model monitoring. Terra covers No platform licence fee, Cloud pass-through billing, WDL workflow execution, Notebook environments.

Answered from the vendors’ own pages

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
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.

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
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.

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
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.

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
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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