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Databases · head to head

BigQuery vs Preset

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
Preset logo

Preset

Business Intelligence

Managed Apache Superset

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.; Preset limited SQL IDE advanced features compared to specialized query tools
  • They diverge on capability: BigQuery covers Serverless compute, Preset covers Managed Superset.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Preset actually diverge.

Attributes where BigQuery and Preset differ
AttributeBigQueryPreset
Pricing modelusage-basedUnknown
PlatformsWeb, Cloud APIWeb, Cloud
CategoryDatabasesBusiness Intelligence
Founded20082019

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 Preset

  • Managed Superset
  • Auto-scaling
  • Enterprise Security
  • Custom Branding
  • API Access
  • Snowflake
  • BigQuery
  • 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 Preset
  • Bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster runningnot Preset
  • Event and clickstream analytics ingested continuously through the Storage Write API and queried without a load windownot Preset
  • Analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portabilitynot Preset

Preset

  • Self-service analyticsnot BigQuery
  • Data explorationnot BigQuery
  • Ad-hoc reportingnot BigQuery
  • Collaborative analysisnot BigQuery
  • Embedded analyticsnot 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.

Preset

  • Limited SQL IDE advanced features compared to specialized query tools
  • Viewer licenses add substantial cost for embedded analytics deployments
  • Dataset-centric approach requires preprocessing by data teams for some use cases

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

Preset

Free

No published plan breakdown. See the Preset 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 Preset if

  • You need managed superset.
  • You want to start without paying.
  • You work on Web, Cloud.
  • You also want auto-scaling.
Preset review

Questions people ask

Is BigQuery or Preset better?
Neither clearly leads. BigQuery starts at Free and Preset at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Preset?
BigQuery starts at Free and Preset at Free.
Does BigQuery or Preset run on more platforms?
BigQuery runs on Web, Cloud API. Preset runs on Web, Cloud.
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 Preset is typically brought in for.
What can BigQuery do that Preset cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Preset covers Managed Superset, Auto-scaling, Enterprise Security, Custom Branding.

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.

Preset: Is Preset free?

Preset offers a free tier for small teams called Starter with 5 users and no credit card required. Paid plans start at $25 per user per month.

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.

Preset: Can I export my data from Preset?

Yes. Preset uses Apache Superset and the founders contribute over 75% of commits to the open-source project, enabling migration to Superset without vendor lock-in.

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.

Preset: Does Preset include embedded analytics?

Yes. Embedded dashboards are available on Professional and Enterprise plans, with viewer licenses starting at $500 per month for 50 licenses.

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.

Preset: What is the enterprise pricing for Preset?

Enterprise plans are custom quoted. The median buyer pays $35,495 per year.

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.

Preset: Does Preset support AI-powered analytics?

Yes. As of 2026, Preset includes an AI Chatbot and MCP (Model Context Protocol) integration for building charts and dashboards via natural language.

Source
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