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Databases · head to head

BigQuery vs Firestore

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
Firestore logo

Firestore

Databases

Flexible, scalable NoSQL cloud database from Firebase

From
Free
Rated
-
Full reviewWebsite

The short version

  • 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.; Firestore the no-cost Spark plan caps Standard edition at 50,000 document reads, 20,000 writes and 20,000 deletes per day
  • They diverge on capability: BigQuery covers Serverless compute, Firestore covers Document Model.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Firestore actually diverge.

Attributes where BigQuery and Firestore differ
AttributeBigQueryFirestore
Pricing modelusage-basedfreemium
PlatformsWeb, Cloud APIWeb, Ios, Android, Flutter
Founded20082011

Identical on both: starting price (Free), free tier (Yes), 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 Firestore

  • Document Model
  • Real-time Updates
  • Offline Support
  • ACID Transactions
  • Expressive Queries
  • Multi-region
  • Security Rules
  • Firebase Auth

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

Firestore

  • Storing structured application data with realtime listenersnot BigQuery
  • Backing mobile and web apps with a serverless document databasenot BigQuery
  • Building offline first apps that sync when connectivity returnsnot 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.

Firestore

  • The no-cost Spark plan caps Standard edition at 50,000 document reads, 20,000 writes and 20,000 deletes per day
  • The Spark plan caps storage at 1 GiB and network egress at 10 GiB per month
  • Charging is per document read, so a query returning many documents bills for every one of them
  • Going beyond the free thresholds requires the pay as you go Blaze plan billed at Google Cloud rates with no fixed monthly ceiling

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

Firestore

Free
  • SparkFree
    • 1GB storage
    • 50K reads/day
    • 20K writes/day
  • BlazeFree
    • Pay as you go
    • Unlimited operations
    • Multi-region

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 Firestore if

  • You need document model.
  • You want to start without paying.
  • You work on Web, Ios, Android, Flutter.
  • You also want real-time updates.
Firestore review

Questions people ask

Is BigQuery or Firestore better?
Neither clearly leads. BigQuery starts at Free and Firestore at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Firestore?
BigQuery starts at Free and Firestore at Free.
Does BigQuery or Firestore run on more platforms?
BigQuery runs on Web, Cloud API. Firestore runs on Web, Ios, Android, Flutter.
Can I use BigQuery for free?
Both have a free tier, so you can try either at no cost before committing.
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 Firestore is typically brought in for.
What can BigQuery do that Firestore cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Firestore covers Document Model, Real-time Updates, Offline Support, ACID Transactions.

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.

Firestore: What are the free limits on Cloud Firestore?

The Spark Plan includes 1 GiB of stored data, 50,000 reads per day, 20,000 writes per day, and 20,000 deletes per day at no cost.

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.

Firestore: What happens when I exceed the Spark Plan free tier?

Exceeding the free tier requires upgrading to the Blaze Plan, which bills based on actual usage through Google Cloud pricing. Charges apply for reads, writes, deletes, and data storage.

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.

Firestore: Can I use Firestore without a credit card?

Yes, you can use the Spark Plan indefinitely without a credit card. To use the Blaze Plan (pay-as-you-go), a credit card is required.

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.

Firestore: Is there a trial period for Cloud Firestore?

No trial period is specified. The Spark Plan free tier serves as the trial, with no time limit.

Source
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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