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Databases · head to head

Amazon RDS vs BigQuery

Amazon RDS logo

Amazon RDS

Databases

Set up, operate, and scale a relational database in the cloud

From
Free
Rated
-
Full reviewWebsite
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

The short version

  • Each has a real cost: Amazon RDS no super-user access or direct host connectivity limits advanced customization; 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.
  • They diverge on capability: Amazon RDS covers Multiple DB Engines, BigQuery covers Serverless compute.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Amazon RDS and BigQuery actually diverge.

Attributes where Amazon RDS and BigQuery differ
AttributeAmazon RDSBigQuery
PlatformsAWS Cloud, Multi-AZ, Multi-regionWeb, Cloud API
Founded20062008

Identical on both: starting price (Free), pricing model (usage-based), 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 Amazon RDS

  • Multiple DB Engines
  • Automated Backups
  • Multi-AZ Deployment
  • Read Replicas
  • Encryption
  • Performance Insights
  • Automatic Scaling
  • MySQL

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

What people use each for

The jobs each tool is most often brought in to do.

Amazon RDS

  • Transaction processingnot BigQuery
  • Data storagenot BigQuery
  • Application backendnot BigQuery
  • Reportingnot BigQuery
  • Data analyticsnot BigQuery

BigQuery

  • A warehouse for an organisation already on Google Cloud, where identity, logging and billing are consolidated in the same placenot Amazon RDS
  • Bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster runningnot Amazon RDS
  • Event and clickstream analytics ingested continuously through the Storage Write API and queried without a load windownot Amazon RDS
  • Analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portabilitynot Amazon RDS

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Amazon RDS

  • No super-user access or direct host connectivity limits advanced customization
  • Pricing unpredictable and expensive compared to GCP alternatives with equivalent features
  • Limited access to system procedures and tables requiring advanced permissions
  • No Oracle RAC (Real Application Clusters) support for high-availability Oracle deployments

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.

Pricing, plan by plan

Amazon RDS

Free
  • On-Demand Instances$undefined/per second
  • Reserved Instances$undefined/mo
  • Database Savings Plans$undefined/mo

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

Which should you pick?

Choose Amazon RDS if

  • You need multiple db engines.
  • You want to start without paying.
  • You work on AWS Cloud, Multi-AZ, Multi-region.
  • You also want automated backups.
Amazon RDS review

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

Questions people ask

Is Amazon RDS or BigQuery better?
Neither clearly leads. Amazon RDS starts at Free and BigQuery at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Amazon RDS or BigQuery?
Amazon RDS starts at Free and BigQuery at Free.
Does Amazon RDS or BigQuery run on more platforms?
Amazon RDS runs on AWS Cloud, Multi-AZ, Multi-region. BigQuery runs on Web, Cloud API.
Can I use Amazon RDS for free?
Both have a free tier, so you can try either at no cost before committing.
What is Amazon RDS best used for?
Amazon RDS is most often used for transaction processing, data storage, application backend, reporting. Of those, transaction processing and data storage are not what BigQuery is typically brought in for.
What can Amazon RDS do that BigQuery cannot?
Amazon RDS covers Multiple DB Engines, Automated Backups, Multi-AZ Deployment, Read Replicas. BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering.

Answered from the vendors’ own pages

Amazon RDS: What is included in the AWS Free Tier for RDS?

For signups before July 15, 2025: 750 hours per month of single-AZ database instance usage (12 months), 20 GB General Purpose SSD storage monthly, 20 GB automated backup storage monthly, available engines include MySQL, MariaDB, PostgreSQL, SQL Server Express Edition. For signups after July 15, 2025: choice between Free Plan or Paid Plan, $100 in credits plus up to $100 additional credits for activating foundational services, credits valid 12 months. Free Tier unavailable in AWS GovCloud (US) and China (Beijing) regions.

Source
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.

Amazon RDS: How is data transfer priced in RDS?

Same Availability Zone (EC2 to RDS) is free. Multi-AZ replication is free. Cross-AZ within same region is 0.01 USD per GB in and out. Cross-region snapshots and backups follow standard data transfer charges.

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.

Amazon RDS: What database engines are supported by RDS?

Aurora, MySQL, PostgreSQL, MariaDB, Oracle, SQL Server, and IBM Db2. Pricing varies by engine.

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.

Amazon RDS: What cost components are included in RDS monthly pricing?

DB instance hours (billed in 1-second increments, 10-minute minimum), storage per GB per month, I/O requests (Aurora and magnetic storage only), provisioned IOPS per month, backup storage, and data transfer fees.

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.

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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