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Spreadsheets · head to head

Apache Superset vs BigQuery

Apache Superset logo

Apache Superset

Spreadsheets

Modern data exploration and visualization platform

From
Free
Rated
-
Full reviewWebsite
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

The short version

  • Each has a real cost: Apache Superset no commercial pricing; open-source project; 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.
  • They diverge on capability: Apache Superset covers 40+ Visualizations, BigQuery covers Serverless compute.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Apache Superset and BigQuery actually diverge.

Attributes where Apache Superset and BigQuery differ
AttributeApache SupersetBigQuery
Pricing modelopen-sourceusage-based
PlatformsWeb, Self-hosted, DockerWeb, Cloud API
CategorySpreadsheetsDatabases
Founded19992008

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 Apache Superset

  • 40+ Visualizations
  • SQL IDE
  • Semantic Layer
  • Caching
  • Security
  • PostgreSQL
  • MySQL
  • Presto

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

What people use each for

The jobs each tool is most often brought in to do.

Apache Superset

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

BigQuery

  • A warehouse for an organisation already on Google Cloud, where identity, logging and billing are consolidated in the same placenot Apache Superset
  • Bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster runningnot Apache Superset
  • Event and clickstream analytics ingested continuously through the Storage Write API and queried without a load windownot Apache Superset
  • Analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portabilitynot Apache Superset

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Apache Superset

  • No commercial pricing; open-source project
  • Commercial hosting available via Preset.io (separate vendor)

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.

Pricing, plan by plan

Apache Superset

Free
  • Open SourceFree
    • Full Features
    • Self-hosted
    • Community Support

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

Which should you pick?

Choose Apache Superset if

  • You need 40+ visualizations.
  • You want to start without paying.
  • You work on Web, Self-hosted, Docker.
  • You also want sql ide.
Apache Superset review

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

Questions people ask

Is Apache Superset or BigQuery better?
Neither clearly leads. Apache Superset starts at Free and BigQuery at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Superset or BigQuery?
Apache Superset starts at Free and BigQuery at Free.
Does Apache Superset or BigQuery run on more platforms?
Apache Superset runs on Web, Self-hosted, Docker. BigQuery runs on Web, Cloud API.
Can I use Apache Superset for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Superset best used for?
Apache Superset is most often used for self-service analytics, data exploration, ad-hoc reporting, collaborative analysis. Of those, self-service analytics and data exploration are not what BigQuery is typically brought in for.
What can Apache Superset do that BigQuery cannot?
Apache Superset covers 40+ Visualizations, SQL IDE, Semantic Layer, Caching. BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering.

Answered from the vendors’ own pages

Apache Superset: What does Apache Superset cost?

Apache Superset is open-source software available free under the Apache 2.0 license. There are no licensing costs, subscription fees, or per-seat charges for using Superset itself.

Source
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.

Apache Superset: Is there a paid version of Apache Superset?

Apache Superset itself is free and open-source. Preset.io offers commercial Superset hosting with premium support, but this is a separate vendor service, not an official paid tier of Superset.

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.

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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