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Databases · head to head

BigQuery vs Databox

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
Databox logo

Databox

Databases

KPI dashboards that pull from the tools you already run on

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.; Databox data sources are the billing unit, so cost tracks the number of tools you connect rather than the value you get from them
  • They diverge on capability: BigQuery covers Serverless compute, Databox covers Pre-built dashboards.
  • Prices and features above were last checked on 25 September 2026.

Where they differ

Only the attributes on which BigQuery and Databox actually diverge.

Attributes where BigQuery and Databox differ
AttributeBigQueryDatabox
Pricing modelusage-basedsubscription
PlatformsWeb, Cloud APIWeb, Mobile, Tv
CategoryDatabasesUnknown
Founded20082012

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 Databox

  • Pre-built dashboards
  • Dashboard designer
  • Metric library
  • Goal tracking
  • Scorecards
  • Alerts
  • Scheduled reports
  • Client reporting

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

Databox

  • Watching marketing, sales and finance KPIs from separate SaaS tools on one shared dashboardnot BigQuery
  • Agencies reporting campaign performance to many clients without rebuilding a report per accountnot BigQuery
  • Putting a live KPI board on an office TV or a recurring email to a leadership teamnot BigQuery
  • Replacing a manually maintained spreadsheet that someone updates from tool exports each weeknot BigQuery
  • Tracking goals and getting alerted when a metric moves past a thresholdnot 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.

Databox

  • Data sources are the billing unit, so cost tracks the number of tools you connect rather than the value you get from them
  • The $71 Analyst plan is a single seat: adding even a second person means the $199 Core plan, a 2.8x jump
  • AI credits are metered monthly, from 50 on Free to 1,000 on Scale, so heavier AI use pushes you up a tier
  • Every published price assumes annual billing; monthly billing forfeits the advertised 20% saving
  • Free and Analyst plans sync daily and hourly respectively, so neither suits anything close to real-time monitoring
  • It is a dashboarding layer, not a warehouse: there is no transformation or modelling step for messy source data

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

Databox

Free
  • FreeFree
    • 3 data sources
    • 1 user
    • 50 AI credits per month
  • Analyst$71/month
    • 5 data sources
    • 1 user
    • 150 AI credits per month
  • Team - Core$199/month
    • 10 data sources
    • 3 users
    • 500 AI credits per month
  • Team - Scale$319/month
    • 30 data sources
    • 10 users
    • 1,000 AI credits per month

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 Databox if

  • You need pre-built dashboards.
  • You want to start without paying.
  • You work on Web, Mobile, Tv.
  • You also want dashboard designer.
Databox review

Questions people ask

Is BigQuery or Databox better?
Neither clearly leads. BigQuery starts at Free and Databox at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Databox?
BigQuery starts at Free and Databox at Free.
Does BigQuery or Databox run on more platforms?
BigQuery runs on Web, Cloud API. Databox runs on Web, Mobile, Tv.
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 Databox is typically brought in for.
What can BigQuery do that Databox cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Databox covers Pre-built dashboards, Dashboard designer, Metric library, Goal tracking.

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.

Databox: How much does Databox cost?

Databox has a free forever plan with 3 data sources and 1 user. Paid plans are Analyst at $71 per month, Team Core at $199 per month and Team Scale at $319 per month, with an Agency plan from $79 per month plus $20 client packs and a Custom tier on request. All prices are billed annually; monthly billing forfeits the 20% annual saving.

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.

Databox: Does Databox have a free plan or a free trial?

Both. The Free plan is free forever and covers 3 data sources, 1 user, 50 AI credits a month and daily syncing. Paid plans also offer a 14-day free trial with no credit card required.

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.

Databox: How many users does each Databox plan include?

Free and Analyst are single-user. Team Core includes 3 users and Team Scale includes 10. The Agency and Custom plans include unlimited users.

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.

Databox: How many tools does Databox integrate with?

Databox connects to more than 130 tools, spreadsheets, databases and APIs, including HubSpot, Salesforce, Google Analytics, Google and Facebook Ads, Shopify, Stripe, QuickBooks, Google Sheets and custom API sources.

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.

Databox: How often does Databox refresh its data?

The Free plan syncs daily. Analyst, Team Core, Team Scale, Agency and Custom plans all sync hourly.

Source
Databox: Who makes Databox?

Databox, Inc., founded in 2012 by Davorin Gabrovec, who is now President and Chief Product Officer. Pete Caputa became CEO in 2017 after a decade at HubSpot. The company is headquartered in Boston, Massachusetts, with its product and engineering team based in Ptuj, Slovenia.

Source
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