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Databases · head to head

BigQuery vs Google Vertex AI

BigQuery logo

BigQuery

Databases

Google Cloud's serverless analytical warehouse, billed either by bytes scanned per query or by reserved compute slots.

From
Free
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

  • Only BigQuery has a free tier, so it costs nothing to try first.
  • Each has a real cost: BigQuery on-demand billing charges for bytes read from every column a query references, so an unqualified select or a missing partition filter turns a routine query into a large bill, and the cost is discovered after the fact rather than at review time.; Google Vertex AI vendor lock-in to Google Cloud ecosystem makes migration to other platforms difficult
  • They diverge on capability: BigQuery covers Serverless compute, Google Vertex AI covers AutoML.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where BigQuery and Google Vertex AI differ
AttributeBigQueryGoogle Vertex AI
Starting priceFreeOn request
Pricing modelusage-basedUnknown
Free tierYesNo
PlatformsWeb, Cloud APICloud, Web
CategoryDatabasesMachine Learning

Identical on both: user rating (Not yet rated), founded (2008).

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 BigQuery

  • Serverless compute
  • Separation of storage and compute
  • Two pricing models
  • Partitioning and clustering
  • Materialised views
  • BigQuery ML
  • Storage Write API
  • BI Engine

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.

BigQuery

  • A warehouse for an organisation already on Google Cloud, where identity, logging and billing are consolidated in the same placenot Google Vertex AI
  • Bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster runningnot Google Vertex AI
  • Event and clickstream analytics ingested continuously through the Storage Write API and queried without a load windownot Google Vertex AI
  • Analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portabilitynot Google Vertex AI

Google Vertex AI

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

Where each one falls short

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

BigQuery

  • On-demand billing charges for bytes read from every column a query references, so an unqualified select or a missing partition filter turns a routine query into a large bill, and the cost is discovered after the fact rather than at review time.
  • There is no way to join tables that live in different regions, so a data estate split across regions for residency reasons has to be reconciled with copies and the storage and transfer that implies.
  • It is not built for point lookups; retrieving a single row has latency measured in hundreds of milliseconds or more, so BigQuery cannot serve an application's read path and always needs a second store in front of it.
  • Frequent small mutations run into DML concurrency limits and the cost of rewriting storage blocks, so a workload that updates individual rows continuously behaves badly compared with an append-only design.
  • The compute exists only inside Google Cloud, so while tables can be exported, the accumulated GoogleSQL, scheduled queries, authorised views, ML models and IAM structure do not move, and switching warehouses is a rewrite of the analytical layer.

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

BigQuery

Free
  • Free TierFree
    • 1TB queries/month
    • 10GB storage/month
    • Standard support
  • On-demand$6.25/TB
    • Pay per query
    • Pay per storage
    • All features

Google Vertex AI

On request

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

Which should you pick?

Choose BigQuery if

  • You need serverless compute.
  • You want to start without paying.
  • You work on Web, Cloud API.
  • You also want separation of storage and compute.
BigQuery 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 BigQuery or Google Vertex AI better?
Neither clearly leads. BigQuery starts at Free 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, BigQuery or Google Vertex AI?
BigQuery has a free tier; the other does not. Paid plans start at Free for BigQuery and On request for Google Vertex AI.
Does BigQuery or Google Vertex AI run on more platforms?
BigQuery runs on Web, Cloud API. Google Vertex AI runs on Cloud, Web.
Can I use BigQuery for free?
Yes. BigQuery has a free tier, so you can try it without paying. Google Vertex AI starts at On request.
What is BigQuery best used for?
BigQuery is most often used for a warehouse for an organisation already on google cloud, where identity, logging and billing are consolidated in the same place, bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster running, event and clickstream analytics ingested continuously through the storage write api and queried without a load window, analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portability. Of those, a warehouse for an organisation already on google cloud, where identity, logging and billing are consolidated in the same place and bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster running are not what Google Vertex AI is typically brought in for.
What can BigQuery do that Google Vertex AI cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Google Vertex AI covers AutoML, Custom training, Feature Store, Model monitoring.

Answered from the vendors’ own pages

BigQuery: How is BigQuery actually billed?

Storage is billed separately from compute. Compute is either on-demand, priced by the bytes a query reads from the referenced columns, or capacity-based, where you reserve autoscaling slots. Most cost surprises come from on-demand queries that scan more than expected.

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
BigQuery: How do I control query cost?

Partition and cluster tables so queries prune data, select only the columns needed, use materialised views for repeated aggregations, and set maximum bytes billed on queries so a runaway scan fails instead of billing.

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
BigQuery: Can I use it without being on Google Cloud?

The service only runs on Google Cloud. BigQuery Omni can query data held in S3 or Azure storage, but the compute is still Google's and the account relationship is still with Google.

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
BigQuery: Is it suitable for serving application queries?

No. Latency for single-row reads is far too high. BigQuery is an analytical warehouse and application read paths need a transactional database or a cache in front of it.

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
BigQuery: When should I move from on-demand to capacity pricing?

When on-demand spend becomes both large and predictable, or when unpredictable spend is a bigger problem than query queueing. The switch trades a variable bill for a fixed one plus contention between workloads.

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