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Databases · head to head

BigQuery vs Cortex

BigQuery logo

BigQuery

Databases

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

From
Free
Free plan
Yes
Visit BigQueryFull review
Cortex logo

Cortex

Developer Tools

Internal developer portal that scores service ownership and maturity rather than just cataloguing services

From
On request
Free plan
No
Visit CortexFull review

The short version

  • BigQuery starts at $6.25/mo; Cortex prices on request.
  • Only BigQuery lists serverless compute; only Cortex lists service catalogue.
  • Prices and features on this page were last checked on 31 August 2026.

Where they differ

Only the attributes on which BigQuery and Cortex actually diverge.

Attributes where BigQuery and Cortex differ
AttributeBigQueryCortex
Starting priceFreeOn request
Pricing modelUsage-basedQuote
Free tierYesNo
CategoryDatabasesDeveloper Tools

Identical on both: platforms (Web, API).

Not yet recorded: founded for Cortex.

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 Cortex

  • Service catalogue
  • Scorecards
  • Initiatives
  • Self-service templates
  • Query language
  • Eng intelligence
  • Integrations layer
  • On-call and ownership mapping

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 Cortex
  • Bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster runningnot Cortex
  • Event and clickstream analytics ingested continuously through the Storage Write API and queried without a load windownot Cortex
  • Analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portabilitynot Cortex

Cortex

  • A platform team that needs to prove to leadership how far a Kubernetes or framework migration has actually progressed across hundreds of servicesnot BigQuery
  • An organisation where ownership of production services is genuinely unknown after reorganisations or acquisitionsnot BigQuery
  • Enforcing production readiness standards on new services at creation time rather than at incident reviewnot BigQuery
  • Replacing a self-hosted Backstage that is consuming a full-time engineer or two just to stay upgradednot 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

Cortex

  • Pricing is quote only, so a platform team cannot build a business case or compare against the true cost of self-hosted Backstage without entering a sales cycle first
  • The catalogue is only as accurate as the metadata you supply, so an organisation with inconsistent repository conventions spends its first quarter cleaning data before any scorecard means anything
  • Scorecards create political friction: publishing a per-team grade turns an engineering standard into a performance metric, and teams will game the rules rather than fix the underlying issue
  • It is closed source and hosted, so organisations with strict data residency or air-gap requirements have limited options compared with running Backstage themselves
  • Value scales with organisation size, so a company with fifty engineers and thirty services will find the catalogue tells them nothing they did not already know while still paying an enterprise contract

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

Cortex

On request
  • CortexOn request
    • Service catalogue and ownership mapping
    • Scorecards and initiatives
    • Self-service templates

Which should you pick?

Choose BigQuery if

  • You need serverless compute.
  • You want to start without paying.
  • You work on Web, API.
  • You also want separation of storage and compute.
BigQuery review

Choose Cortex if

  • You need service catalogue.
  • You work on Web, API.
  • You also want scorecards.
Cortex review

Questions people ask

Is BigQuery or Cortex better?
Neither clearly leads. BigQuery starts at Free and Cortex at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Cortex?
BigQuery has a free tier; the other does not. Paid plans start at Free for BigQuery and On request for Cortex.
Does BigQuery or Cortex run on more platforms?
Both run on Web, API, so platform support will not decide this one for you.
Can I use BigQuery for free?
Yes. BigQuery has a free tier, so you can try it without paying. Cortex 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 Cortex is typically brought in for.
What can BigQuery do that Cortex cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Cortex covers Service catalogue, Scorecards, Initiatives, Self-service templates.

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.

Cortex: How is Cortex different from Backstage?

Backstage is an open source framework you build and staff. Cortex is a hosted product with scorecards and initiatives out of the box and no platform team required to keep it running.

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.

Cortex: Is pricing published?

No. Cortex requires a sales conversation and prepares a custom proposal.

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.

Cortex: Can it self-host?

It is offered as a hosted product; on-premise arrangements are handled through sales rather than published.

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.

Cortex: What is the main adoption risk?

Bad or missing service metadata. The catalogue and every scorecard on top of it inherit whatever quality your repositories already have.

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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