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Databases · head to head

BigQuery vs Looker

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
Looker logo

Looker

Spreadsheets

Modern business intelligence platform by Google

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.; Looker no pricing published on website; quote-based model requires contacting Google Cloud sales
  • They diverge on capability: BigQuery covers Serverless compute, Looker covers LookML Data Modeling.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Looker actually diverge.

Attributes where BigQuery and Looker differ
AttributeBigQueryLooker
Starting priceFreeOn request
Pricing modelusage-basedUnknown
Free tierYesNo
PlatformsWeb, Cloud APIWeb, Cloud (Google Cloud Platform)
CategoryDatabasesSpreadsheets

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 Looker

  • LookML Data Modeling
  • Embedded Analytics
  • API Access
  • Version Control
  • Data Actions
  • BigQuery
  • Snowflake
  • Redshift

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

Looker

  • Business intelligence and interactive dashboards for data-driven decision makingnot BigQuery
  • Embedded analytics for integrating BI capabilities into third-party applicationsnot 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.

Looker

  • No pricing published on website; quote-based model requires contacting Google Cloud sales
  • Pricing typically based on user count, deployment type, and feature requirements with no transparency

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

Looker

On request

No published plan breakdown. See the Looker 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 Looker if

  • You need lookml data modeling.
  • You work on Web, Cloud (Google Cloud Platform).
  • You also want embedded analytics.
Looker review

Questions people ask

Is BigQuery or Looker better?
Neither clearly leads. BigQuery starts at Free and Looker at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Looker?
BigQuery has a free tier; the other does not. Paid plans start at Free for BigQuery and On request for Looker.
Does BigQuery or Looker run on more platforms?
BigQuery runs on Web, Cloud API. Looker runs on Web, Cloud (Google Cloud Platform).
Can I use BigQuery for free?
Yes. BigQuery has a free tier, so you can try it without paying. Looker 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 Looker is typically brought in for.
What can BigQuery do that Looker cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Looker covers LookML Data Modeling, Embedded Analytics, API Access, Version Control.

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.

Looker: How much does Looker cost?

Looker does not publish pricing on its website. The platform uses a custom quote model where organizations contact Google Cloud sales for personalized pricing. Pricing is typically based on factors including user count, deployment type (cloud-hosted versus self-hosted), and required feature set.

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.

Looker: What are Looker's pricing tiers?

Looker offers subscription-based tiers typically including Standard with core BI capabilities, Advanced with enhanced features and integrations, and Premium with enterprise-grade features. Specific pricing and feature distinctions require contacting Google Cloud sales.

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.

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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