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Databases · head to head

BigQuery vs Quantum Metric

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
Quantum Metric logo

Quantum Metric

Business Intelligence

Continuous product design platform

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.; Quantum Metric only enterprise plans are offered and the pricing page publishes no rate, no session volume tier and no minimum
  • They diverge on capability: BigQuery covers Serverless compute, Quantum Metric covers Session Replay.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Quantum Metric actually diverge.

Attributes where BigQuery and Quantum Metric differ
AttributeBigQueryQuantum Metric
Starting priceFreeOn request
Pricing modelusage-basedsubscription
Free tierYesNo
PlatformsWeb, Cloud APIWeb, Mobile
CategoryDatabasesBusiness Intelligence
Founded20082015

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 Quantum Metric

  • Session Replay
  • Opportunity Analysis
  • Anomaly Detection
  • Real-time Alerts
  • Impact Scoring
  • 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 Quantum Metric
  • Bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster runningnot Quantum Metric
  • Event and clickstream analytics ingested continuously through the Storage Write API and queried without a load windownot Quantum Metric
  • Analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portabilitynot Quantum Metric

Quantum Metric

  • Session replay and digital experience analytics for large web and mobile propertiesnot BigQuery
  • Quantifying friction and conversion loss in checkout and signup flowsnot BigQuery
  • Streaming behavioural insights into a data warehousenot 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.

Quantum Metric

  • Only enterprise plans are offered and the pricing page publishes no rate, no session volume tier and no minimum
  • The page states plans are built around your business, with the only routes being a personalised discussion, a live demo or product tours
  • There is no self-serve tier, free plan or trial published

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

Quantum Metric

On request
  • CustomFree
    • Full Platform
    • Real-time 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 Quantum Metric if

  • You need session replay.
  • You work on Web, Mobile.
  • You also want opportunity analysis.
Quantum Metric review

Questions people ask

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

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.

Quantum Metric: How much does Quantum Metric cost?

Quantum Metric uses custom pricing based on annual session volume, number of digital properties monitored, and product add-ons. Exact costs require contacting the sales team.

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.

Quantum Metric: Does Quantum Metric offer a free trial?

The pricing page does not mention a free trial option. Interested parties must request a demo to discuss pricing and obtain a custom quote.

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.

Quantum Metric: What factors affect Quantum Metric pricing?

Pricing scales with digital properties (websites and applications monitored), session volume (data collected and analyzed), and customer success tier selected. Add-on products like employee experience, data enrichment, and data streaming have separate pricing considerations.

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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