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Databases · head to head

BigQuery vs Dundas 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
Dundas BI logo

Dundas BI

Business Intelligence

Flexible business intelligence platform

From
$500/month
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.; Dundas BI the published Embedded BI Package starts from about $4,738.70 USD per month billed annually for 8 core capacity
  • They diverge on capability: BigQuery covers Serverless compute, Dundas BI covers White-labeling.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where BigQuery and Dundas BI differ
AttributeBigQueryDundas BI
Starting priceFree$500/month
Pricing modelusage-basedsubscription
Free tierYesNo
PlatformsWeb, Cloud APIWeb, Embedded, Mobile
CategoryDatabasesBusiness Intelligence
Founded20081992

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 Dundas BI

  • White-labeling
  • Embedded Analytics
  • Data Preparation
  • Custom Visualizations
  • API
  • SQL Server
  • Oracle
  • PostgreSQL

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

Dundas BI

  • Embedding dashboards and analytics inside another applicationnot BigQuery
  • Self service business intelligence and ad hoc reportingnot BigQuery
  • Building custom data visualisations against open BI APIsnot 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.

Dundas BI

  • The published Embedded BI Package starts from about $4,738.70 USD per month billed annually for 8 core capacity
  • That figure reflects a limited time 35 percent promotional discount rather than list price
  • Licensing is by CPU core capacity rather than by user, so cost scales with server hardware
  • Dundas is now part of insightsoftware following acquisition
  • The page states that a pricing plan is worked out together with the vendor rather than published as a rate card

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

Dundas BI

$500/month
  • Professional$500/month
    • Full Platform
    • Embedding
    • Support
  • EnterpriseFree
    • Unlimited Users
    • Multi-tenant
    • Premium 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 Dundas BI if

  • You need white-labeling.
  • You work on Web, Embedded, Mobile.
  • You also want embedded analytics.
Dundas BI review

Questions people ask

Is BigQuery or Dundas BI better?
Neither clearly leads. BigQuery starts at Free and Dundas BI at $500/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Dundas BI?
BigQuery has a free tier; the other does not. Paid plans start at Free for BigQuery and $500/month for Dundas BI.
Does BigQuery or Dundas BI run on more platforms?
BigQuery runs on Web, Cloud API. Dundas BI runs on Web, Embedded, Mobile.
Can I use BigQuery for free?
Yes. BigQuery has a free tier, so you can try it without paying. Dundas BI starts at $500/month.
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 Dundas BI is typically brought in for.
What can BigQuery do that Dundas BI cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Dundas BI covers White-labeling, Embedded Analytics, Data Preparation, Custom Visualizations.

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.

Dundas BI: How much does Dundas BI cost?

Dundas BI does not publish specific pricing. The vendor emphasizes 'smarter licensing models to scale appropriately and achieve ROI rapidly' and offers 'custom plans that fit your needs' rather than standardized tiers. Customers must contact Dundas directly for pricing quotes tailored to their enterprise or embedded analytics use case.

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.

Dundas BI: What licensing options does Dundas BI offer?

Dundas BI describes their approach as flexible licensing designed to scale based on deployment needs. Specific licensing models and per-seat or concurrent-user costs are determined through custom quotes.

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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