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Databases · head to head

BigQuery vs MongoDB

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
MongoDB logo

MongoDB

Technology

The developer data platform

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.; MongoDB 16 MB maximum document size limits large single objects
  • They diverge on capability: BigQuery covers Serverless compute, MongoDB covers Document model.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and MongoDB actually diverge.

Attributes where BigQuery and MongoDB differ
AttributeBigQueryMongoDB
Pricing modelusage-basedUnknown
PlatformsWeb, Cloud APICloud (Atlas), Self-hosted, Multi-cloud (AWS, Google Cloud, Azure)
CategoryDatabasesTechnology
Founded20082007

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

  • Document model
  • Distributed architecture
  • ACID transactions
  • Real-time analytics
  • Full-text search
  • Time series data
  • Geospatial queries
  • Aggregation framework

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

MongoDB

  • Mobile applicationsnot BigQuery
  • Content managementnot BigQuery
  • Real-time analyticsnot BigQuery
  • IoT applicationsnot BigQuery
  • Gaming backendsnot 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.

MongoDB

  • 16 MB maximum document size limits large single objects
  • No native JOIN support for relational data operations
  • Higher memory usage due to storing field names with each document
  • Eventual consistency in distributed deployments can cause data stale reads

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

MongoDB

Free
  • M0Free
    • 512 MB storage
    • Learning and exploration
  • M2$9/month
    • 2 GB storage
    • Development and testing
  • M5$25/month
    • 5 GB storage
  • M10+$56.94/month
    • Dedicated clusters for production

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 MongoDB if

  • You need document model.
  • You want to start without paying.
  • You work on Cloud (Atlas), Self-hosted, Multi-cloud (AWS, Google Cloud, Azure).
  • You also want distributed architecture.
MongoDB review

Questions people ask

Is BigQuery or MongoDB better?
Neither clearly leads. BigQuery starts at Free and MongoDB at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or MongoDB?
BigQuery starts at Free and MongoDB at Free.
Does BigQuery or MongoDB run on more platforms?
BigQuery runs on Web, Cloud API. MongoDB runs on Cloud (Atlas), Self-hosted, Multi-cloud (AWS, Google Cloud, Azure).
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 MongoDB is typically brought in for.
What can BigQuery do that MongoDB cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. MongoDB covers Document model, Distributed architecture, ACID transactions, Real-time analytics.

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.

MongoDB: Does MongoDB offer a free tier?

Yes. MongoDB Atlas offers an M0 free tier with 512 MB storage for learning and exploration, plus paid options starting at $9/month for M2 with 2 GB storage.

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.

MongoDB: Can I self-host MongoDB?

Yes. You can run MongoDB Community Edition on your own servers, or use MongoDB Enterprise Advanced for self-managed production deployments with enterprise features.

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.

MongoDB: What is the maximum document size in MongoDB?

Documents are limited to 16 MB. For documents exceeding this limit, you can use MongoDB's GridFS API to store files larger than the maximum size.

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.

MongoDB: Does MongoDB support ACID transactions?

Yes. MongoDB supports ACID transactions within a single document by default, and multi-document ACID transactions are available for replica sets and sharded clusters in MongoDB 4.0+.

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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