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Databases · head to head

BigQuery vs Cube

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
Cube logo

Cube

Business Intelligence

AI-native analytics platform with semantic layer and governed access

From
Free
Rated
-
Full reviewWebsite

The short version

  • 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.; Cube per-developer licensing can be expensive for large analytics teams
  • They diverge on capability: BigQuery covers Serverless compute, Cube covers Analytics Chat.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Cube actually diverge.

Attributes where BigQuery and Cube differ
AttributeBigQueryCube
Pricing modelusage-basedPer-developer seats with volume pricing
PlatformsWeb, Cloud APICloud, Self-hosted
CategoryDatabasesBusiness Intelligence
Founded2008Unknown

Identical on both: starting price (Free), free tier (Yes), 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 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 Cube

  • Analytics Chat
  • Workbooks
  • Dashboards
  • Embedded Analytics
  • AI Integrations
  • Core Data APIs
  • Caching and pre-aggregations

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

Cube

  • Building governed semantic data models for analyticsnot BigQuery
  • Embedding analytics into customer-facing productsnot BigQuery
  • Enabling natural language data queries for teamsnot BigQuery
  • Creating conversational dashboards with AI assistancenot 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.

Cube

  • Per-developer licensing can be expensive for large analytics teams
  • Additional cost for Explorer and Viewer roles beyond developers
  • Semantic layer approach requires upfront modeling investment
  • Smaller connector ecosystem than dedicated BI platforms
  • May be overengineered for simple reporting needs

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

Cube

Free
  • FreeFree
    • For hobbyists and personal projects
    • Basic data source connection
    • Semantic modeling
  • Starter$40/developer/month
    • Extended agent limits
    • Premium LLMs
    • Unlimited workbooks
  • Premium$80/developer/month
    • All Starter features
    • Embedded dashboards
    • Embedded analytics chat
  • Enterprise$null/custom
    • 99.990% uptime SLA
    • Dedicated single-tenant installation
    • Bring Your Own Cloud

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 Cube if

  • You need analytics chat.
  • You want to start without paying.
  • You work on Cloud, Self-hosted.
  • You also want workbooks.
Cube review

Questions people ask

Is BigQuery or Cube better?
Neither clearly leads. BigQuery starts at Free and Cube at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Cube?
BigQuery starts at Free and Cube at Free.
Does BigQuery or Cube run on more platforms?
BigQuery runs on Web, Cloud API. Cube runs on Cloud, Self-hosted.
Can I use BigQuery for free?
Both have a free tier, so you can try either at no cost before committing.
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 Cube is typically brought in for.
What can BigQuery do that Cube cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Cube covers Analytics Chat, Workbooks, Dashboards, Embedded Analytics.

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.

Cube: What is the cost per developer on Cube?

Starter plan costs $40/developer/month. Premium adds embedded analytics at $80/developer/month. Explorer and Viewer roles cost $40 and $20/month respectively.

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.

Cube: What uptime SLAs does Cube offer?

Premium plan offers 99.950% uptime SLA. Enterprise plan provides 99.990% uptime SLA with dedicated support.

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.

Cube: Can I use Cube for free?

Yes, the Free plan includes basic data source connection, semantic modeling, workbooks, and dashboards for hobbyists and personal projects.

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.

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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