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Databases · head to head

BigQuery vs Power BI

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
Power BI logo

Power BI

Business Intelligence

Business analytics by Microsoft

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.; Power BI free tier cannot publish or share reports; Pro tier required for collaboration
  • They diverge on capability: BigQuery covers Serverless compute, Power BI covers AI-powered Insights.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Power BI actually diverge.

Attributes where BigQuery and Power BI differ
AttributeBigQueryPower BI
Pricing modelusage-basedUnknown
PlatformsWeb, Cloud APIWeb, Desktop, Mobile
CategoryDatabasesBusiness Intelligence
Founded20081975

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 Power BI

  • AI-powered Insights
  • Natural Language Queries
  • Real-time Dashboards
  • Paginated Reports
  • Mobile Apps
  • Excel
  • Azure
  • Dynamics 365

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

Power BI

  • Self-service analyticsnot BigQuery
  • Data explorationnot BigQuery
  • Ad-hoc reportingnot BigQuery
  • Collaborative analysisnot BigQuery
  • Embedded 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.

Power BI

  • Free tier cannot publish or share reports; Pro tier required for collaboration
  • Free tier cannot schedule automatic data refreshes
  • Offline capabilities limited to local Power BI Desktop; cloud service always requires internet
  • Data refresh capped at 8 times per day on Pro tier without Premium Per User

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

Power BI

Free
  • FreeFree
    • Local report creation in Power BI Desktop
    • Cannot publish or share
    • No scheduled refreshes
  • Power BI Pro$14/user/month
    • Publish and share reports
    • Up to 8 scheduled refreshes/day
    • Collaborate with other Pro users
  • Premium Per User$24/user/month
    • All Pro features
    • Up to 48 scheduled refreshes/day
    • Copilot integration

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 Power BI if

  • You need ai-powered insights.
  • You want to start without paying.
  • You work on Web, Desktop, Mobile.
  • You also want natural language queries.
Power BI review

Questions people ask

Is BigQuery or Power BI better?
Neither clearly leads. BigQuery starts at Free and Power BI at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Power BI?
BigQuery starts at Free and Power BI at Free.
Does BigQuery or Power BI run on more platforms?
BigQuery runs on Web, Cloud API. Power BI runs on Web, Desktop, Mobile.
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 Power BI is typically brought in for.
What can BigQuery do that Power BI cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Power BI covers AI-powered Insights, Natural Language Queries, Real-time Dashboards, Paginated Reports.

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.

Power BI: Can I use Power BI Desktop offline?

Power BI Desktop runs locally and can edit reports offline, but publishing to the service and refreshing cloud data sources requires internet connection. Offline reports show cached data from the last refresh.

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.

Power BI: What are the data refresh limits for each tier?

Power BI Premium Per User allows up to 48 scheduled refreshes per day, while Pro tier is limited to 8 scheduled refreshes per day. Free tier cannot schedule automatic refreshes.

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.

Power BI: Can I use Power BI Free with shared data sources?

Free tier users can create local reports in Power BI Desktop but cannot publish to the Power BI Service for collaboration. Publishing requires Power BI Pro ($14/user/month).

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.

Power BI: Is SSO available and on which plan?

SSO is available on Power BI Premium Per User ($24/user/month) and Fabric capacity plans through Azure AD integration.

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.

Power BI: What does Copilot require in Power BI?

Copilot for natural language queries and automatic report generation requires Power BI Premium Per User or Fabric capacity pricing, not available on Pro or Free tiers.

Source
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