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Databases · head to head

BigQuery vs Chartio

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
Chartio logo

Chartio

Business Intelligence

Cloud-based data exploration (discontinued)

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.; Chartio product was discontinued and sunset on March 1, 2022 after company acquisition by Atlassian
  • They diverge on capability: BigQuery covers Serverless compute, Chartio covers Visual Query Builder.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Chartio actually diverge.

Attributes where BigQuery and Chartio differ
AttributeBigQueryChartio
Starting priceFreeOn request
Pricing modelusage-basedUnknown
Free tierYesNo
PlatformsWeb, Cloud APIWeb
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 Chartio

  • Visual Query Builder
  • Interactive Dashboards
  • Data Blending
  • Collaboration
  • Embedding
  • PostgreSQL
  • MySQL
  • 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 Chartio
  • Bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster runningnot Chartio
  • Event and clickstream analytics ingested continuously through the Storage Write API and queried without a load windownot Chartio
  • Analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portabilitynot Chartio

Chartio

  • Drag and drop chart building over SQL databasesnot BigQuery
  • Shared business dashboards for non-technical teamsnot BigQuery
  • Exploring warehouse data without writing SQLnot 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.

Chartio

  • Product was discontinued and sunset on March 1, 2022 after company acquisition by Atlassian
  • No current pricing available as service no longer operates
  • Users were migrated to alternative Atlassian solutions

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

Chartio

On request
  • DiscontinuedFree
    • Service ended March 2022

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 Chartio if

  • You need visual query builder.
  • You also want interactive dashboards.
Chartio review

Questions people ask

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

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.

Chartio: Is Chartio still available?

No, Chartio was discontinued on March 1, 2022. The product was acquired by Atlassian and the service was shut down. Users were offered migration to alternative solutions.

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.

Chartio: What should I use instead of Chartio after its discontinuation?

Atlassian, which acquired Chartio, recommends users migrate to other data visualization and business intelligence tools within the Atlassian ecosystem or compatible alternatives.

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.

Chartio: Can I still access Chartio's pricing information?

No, historical pricing is not relevant as Chartio ceased operations in March 2022. Any archived pricing information would not reflect current market options.

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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