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Databases · head to head

BigQuery vs Immuta

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
Immuta logo

Immuta

Databases

Attribute-based access control and masking applied inside Snowflake, Databricks and BigQuery

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.; Immuta contracts commonly start around one hundred to two hundred thousand US dollars a year for mid-market deployments and exceed five hundred thousand at enterprise scale, which excludes most data teams without a regulatory mandate.
  • They diverge on capability: BigQuery covers Serverless compute, Immuta covers Attribute-based policy.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which BigQuery and Immuta actually diverge.

Attributes where BigQuery and Immuta differ
AttributeBigQueryImmuta
Starting priceFreeOn request
Pricing modelusage-basedquote
Free tierYesNo
PlatformsWeb, Cloud APIWeb, API, Cloud
Founded2008Unknown

Identical on both: user rating (Not yet rated), category (Databases).

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 Immuta

  • Attribute-based policy
  • Native enforcement
  • Dynamic masking
  • Row-level filtering
  • Purpose-based access
  • Sensitive data tagging
  • Audit logging
  • Multi-platform

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

Immuta

  • A bank whose Snowflake estate has grown to tens of thousands of roles that no one can review before an auditnot BigQuery
  • A healthcare analytics team that must let researchers query patient data with identifiers masked unless a specific purpose is recordednot BigQuery
  • A multinational applying different residency and access rules per jurisdiction to the same tables without duplicating datasetsnot BigQuery
  • An organisation running both Snowflake and Databricks that wants one policy set rather than two divergent implementationsnot 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.

Immuta

  • Contracts commonly start around one hundred to two hundred thousand US dollars a year for mid-market deployments and exceed five hundred thousand at enterprise scale, which excludes most data teams without a regulatory mandate.
  • Policy is only as good as the data classification underneath it, so an organisation with poorly tagged columns will spend months on classification before Immuta enforces anything useful.
  • Native enforcement means capability varies by platform, and a feature available on Snowflake may be absent or behave differently on BigQuery, which undermines the promise of one policy set everywhere.
  • Adding an access governance layer creates a new dependency in the path to data: a misconfigured policy silently returns fewer rows rather than erroring, and analysts can act on incomplete results without noticing.
  • It governs cloud data platforms, so personal data in operational databases, files and SaaS applications sits outside its scope and needs separate controls, meaning Immuta is rarely the whole answer.

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

Immuta

On request
  • Immuta Platform$undefined/year
    • Attribute-based policy authoring
    • Native enforcement in supported data platforms
    • Dynamic masking and row-level security

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 Immuta if

  • You need attribute-based policy.
  • You work on Web, API, Cloud.
  • You also want native enforcement.
Immuta review

Questions people ask

Is BigQuery or Immuta better?
Neither clearly leads. BigQuery starts at Free and Immuta at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Immuta?
BigQuery has a free tier; the other does not. Paid plans start at Free for BigQuery and On request for Immuta.
Does BigQuery or Immuta run on more platforms?
BigQuery runs on Web, Cloud API. Immuta runs on Web, API, Cloud.
Can I use BigQuery for free?
Yes. BigQuery has a free tier, so you can try it without paying. Immuta 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 Immuta is typically brought in for.
What can BigQuery do that Immuta cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Immuta covers Attribute-based policy, Native enforcement, Dynamic masking, Row-level filtering.

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.

Immuta: Does Immuta sit in the query path?

No. It compiles policies into the data platform's own native controls, so queries run at normal speed through your existing tools.

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.

Immuta: What does it cost?

Not published. Market data suggests roughly 100,000 to 200,000 US dollars a year for mid-market deployments and considerably more at enterprise scale.

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.

Immuta: Is Immuta still independent?

Yes. It remains independently owned, unlike several competitors in data access governance that have been acquired.

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.

Immuta: Does it work across more than one warehouse?

Yes, one policy set can target Snowflake, Databricks, BigQuery and Starburst, though enforcement capability varies by platform.

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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