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Databases · head to head

BigQuery vs Equals

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
Equals logo

Equals

Spreadsheets

A real spreadsheet with SQL result sets landing in its cells, now sold only on annual contracts starting at 24,000 USD

From
$24000/year
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.; Equals the price list now starts at 24,000 USD a year with no monthly billing and no self-serve signup, having offered a free plan in 2023 and 39 USD per seat in 2024, so any team that evaluated it previously is looking at a different product commercially.
  • They diverge on capability: BigQuery covers Serverless compute, Equals covers Connected sheets.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which BigQuery and Equals actually diverge.

Attributes where BigQuery and Equals differ
AttributeBigQueryEquals
Starting priceFree$24000/year
Pricing modelusage-basedPer year by connector count and refresh frequency
Free tierYesNo
PlatformsWeb, Cloud APIWeb, API
CategoryDatabasesSpreadsheets
Founded2008Unknown

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 Equals

  • Connected sheets
  • Real spreadsheet mechanics
  • Cell references into SQL
  • Equals Warehouse
  • Views
  • Writeback

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

Equals

  • A revenue operations team that wants software metrics computed in a spreadsheet rather than defined in a BI semantic layernot BigQuery
  • Analysts who need a SQL result and manual assumptions in the same file, with the assumptions visible as cellsnot BigQuery
  • Joining Stripe, Salesforce and a production database without building a warehouse and pipeline firstnot BigQuery
  • Distributing a recurring numbers pack to Slack, email or a wall display without rebuilding it as a dashboardnot 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.

Equals

  • The price list now starts at 24,000 USD a year with no monthly billing and no self-serve signup, having offered a free plan in 2023 and 39 USD per seat in 2024, so any team that evaluated it previously is looking at a different product commercially.
  • Your data is copied out of your infrastructure by design, into a Snowflake warehouse that Equals operates, with Fivetran holding non-SQL source data in transit, and no self-hosted or bring-your-own-warehouse option is documented.
  • The financial function category contains only IRR, NPV and PMT, with no XIRR, XNPV, FV, PV, RATE or NPER, and there is no LAMBDA, LET, OFFSET or INDIRECT, so a substantial class of existing spreadsheet models cannot be ported without rework.
  • No documentation of importing or exporting Excel files exists anywhere in the docs index; the supported file sources are CSV and Google Sheets and the only documented export is dashboards as PDF, which is a notable gap for a product positioning itself as a successor to Excel.
  • Data freshness is a pricing lever rather than a setting, at six hours on Essential, one hour on Business and fifteen minutes on Enterprise, and the feature that removes row limits is itself gated above the entry tier, so the Essential row ceiling is neither published nor unlimited.

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

Equals

$24000/year
  • Essential$24000/year
    • Unlimited viewers and unlimited editors
    • 3 connectors
    • Data sync every 6 hours
  • Business$36000/year
    • Unlimited viewers and unlimited editors
    • 5 connectors or a warehouse connection
    • Data sync every hour
  • Enterprise$60000/year
    • Unlimited viewers and unlimited editors
    • Unlimited connectors or a warehouse connection
    • Data sync every 15 minutes
  • Connector add-ons$199/month
    • 199 USD per month for each additional database or import script connector
    • 699 USD per month for each additional connector of any other type
    • Available on Business and Enterprise

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 Equals if

  • You need connected sheets.
  • You work on Web, API.
  • You also want real spreadsheet mechanics.
Equals review

Questions people ask

Is BigQuery or Equals better?
Neither clearly leads. BigQuery starts at Free and Equals at $24000/year, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Equals?
BigQuery has a free tier; the other does not. Paid plans start at Free for BigQuery and $24000/year for Equals.
Does BigQuery or Equals run on more platforms?
BigQuery runs on Web, Cloud API. Equals runs on Web, API.
Can I use BigQuery for free?
Yes. BigQuery has a free tier, so you can try it without paying. Equals starts at $24000/year.
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 Equals is typically brought in for.
What can BigQuery do that Equals cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Equals covers Connected sheets, Real spreadsheet mechanics, Cell references into SQL, Equals Warehouse.

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.

Equals: Is it a real spreadsheet or a modelling tool with a grid?

A real spreadsheet. A1 references, absolute reference locking, pivot tables and spilling array formulas all work, and SQL results land in ranges that ordinary cells reference.

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.

Equals: What does it actually cost?

24,000 USD a year for Essential, 36,000 for Business and 60,000 for Enterprise, billed annually. There is no monthly option and no self-serve signup; the trial is negotiated per customer.

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.

Equals: Are seats charged?

No. Viewers and editors are unlimited on every plan. The meters are connector count, refresh frequency and AI spend, with additional connectors at 199 or 699 USD a month.

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.

Equals: Does my data stay in my own warehouse?

No. Connecting more than one source syncs data into a Snowflake warehouse that Equals runs, and non-SQL sources pass through Fivetran. There is no documented self-hosted option.

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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