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Databases · head to head

BigQuery vs Grow

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
Grow logo

Grow

Business Intelligence

No-code BI for growing companies

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.; Grow the pricing URL serves marketing content with no plan, rate, minimum or cost driver published anywhere on it
  • They diverge on capability: BigQuery covers Serverless compute, Grow covers No-code Setup.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Grow actually diverge.

Attributes where BigQuery and Grow differ
AttributeBigQueryGrow
Starting priceFree$500/month
Pricing modelusage-basedsubscription
Free tierYesNo
PlatformsWeb, Cloud APIWeb, Mobile, Tv
CategoryDatabasesBusiness Intelligence
Founded20082014

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 Grow

  • No-code Setup
  • Unlimited Dashboards
  • Data Blending
  • Alerts
  • Embedding
  • Salesforce
  • HubSpot
  • QuickBooks

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

Grow

  • No-code dashboards and business intelligence for teams without analystsnot BigQuery
  • Blending data from more than 100 sources including BigQuery, PostgreSQL, Salesforce and HubSpotnot BigQuery
  • ETL, warehousing and visualisation in a single platformnot 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.

Grow

  • The pricing URL serves marketing content with no plan, rate, minimum or cost driver published anywhere on it
  • The only calls to action are Get Started Now and Watch Demo; there is no self-serve rate card
  • The vendor advertises unlimited users with no per-seat charges, which means the price driver is not disclosed at all

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

Grow

$500/month
  • Starter$500/month
    • Unlimited Users
    • 75+ Integrations
    • Support
  • Pro$1000/month
    • Advanced Features
    • Custom Integrations
    • Training

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 Grow if

  • You need no-code setup.
  • You work on Web, Mobile, Tv.
  • You also want unlimited dashboards.
Grow review

Questions people ask

Is BigQuery or Grow better?
Neither clearly leads. BigQuery starts at Free and Grow at $500/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Grow?
BigQuery has a free tier; the other does not. Paid plans start at Free for BigQuery and $500/month for Grow.
Does BigQuery or Grow run on more platforms?
BigQuery runs on Web, Cloud API. Grow runs on Web, Mobile, Tv.
Can I use BigQuery for free?
Yes. BigQuery has a free tier, so you can try it without paying. Grow 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 Grow is typically brought in for.
What can BigQuery do that Grow cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Grow covers No-code Setup, Unlimited Dashboards, Data Blending, Alerts.

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.

Grow: How much does Grow cost?

Grow does not publish pricing on their website. They offer a request-a-free-trial model that requires contacting their sales team. Pricing is determined by contacting sales through their contact form or trial request process.

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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