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Databases · head to head

BigQuery vs Glassbox

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
Glassbox logo

Glassbox

Business Intelligence

Digital experience intelligence

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.; Glassbox no fixed pricing tiers published; custom quotes required based on package, data retention, and session volume
  • They diverge on capability: BigQuery covers Serverless compute, Glassbox covers Session Replay.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Glassbox actually diverge.

Attributes where BigQuery and Glassbox differ
AttributeBigQueryGlassbox
Starting priceFreeOn request
Pricing modelusage-basedsubscription
Free tierYesNo
PlatformsWeb, Cloud APIWeb, Mobile, Hybrid
CategoryDatabasesBusiness Intelligence
Founded20082010

Identical on both: 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 Glassbox

  • Session Replay
  • Struggle Detection
  • Journey Mapping
  • AI Insights
  • Voice of Customer
  • Adobe Analytics
  • Google Analytics
  • Salesforce

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

Glassbox

  • User experience analysisnot BigQuery
  • Bug reproductionnot BigQuery
  • Conversion optimizationnot BigQuery
  • Customer journey mappingnot BigQuery
  • Usability testingnot 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.

Glassbox

  • No fixed pricing tiers published; custom quotes required based on package, data retention, and session volume

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

Glassbox

On request
  • CustomFree
    • Full Platform
    • Mobile Analytics
    • Enterprise Support

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 Glassbox if

  • You need session replay.
  • You work on Web, Mobile, Hybrid.
  • You also want struggle detection.
Glassbox review

Questions people ask

Is BigQuery or Glassbox better?
Neither clearly leads. BigQuery starts at Free and Glassbox at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Glassbox?
BigQuery has a free tier; the other does not. Paid plans start at Free for BigQuery and On request for Glassbox.
Does BigQuery or Glassbox run on more platforms?
BigQuery runs on Web, Cloud API. Glassbox runs on Web, Mobile, Hybrid.
Can I use BigQuery for free?
Yes. BigQuery has a free tier, so you can try it without paying. Glassbox 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 Glassbox is typically brought in for.
What can BigQuery do that Glassbox cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Glassbox covers Session Replay, Struggle Detection, Journey Mapping, AI Insights.

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.

Glassbox: How much does Glassbox cost?

Glassbox uses custom pricing based on three factors: the selected package (Production Operation, Marketing/Business, or Product), data retention period, and session volume. The vendor encourages contacting sales to build customized packages.

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.

Glassbox: Does Glassbox offer add-ons for specific industries?

Yes, Glassbox offers specialized add-on solutions including fraud prevention, accessibility features, and call center solutions tailored for financial organizations.

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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