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Databases · head to head

Apache Doris vs BigQuery

Apache Doris logo

Apache Doris

Databases

MPP analytical database with a MySQL wire protocol and sub-second aggregation on wide tables

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: Apache Doris two competing commercial vendors, VeloDB and SelectDB, were founded by overlapping core contributors, which makes the long-term governance and roadmap of the project harder to predict than a single-sponsor project.; 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: Apache Doris covers MySQL wire protocol, BigQuery covers Serverless compute.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Apache Doris and BigQuery actually diverge.

Attributes where Apache Doris and BigQuery differ
AttributeApache DorisBigQuery
Pricing modelOpen source, no licence feeusage-based
PlatformsLinux, Docker, KubernetesWeb, Cloud API
FoundedUnknown2008

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 Apache Doris

  • MySQL wire protocol
  • Aggregate and unique key models
  • Multi-catalogue federation
  • Routine load from Kafka
  • Compute storage separation
  • Inverted indexes
  • Workload groups

Only in BigQuery

  • Serverless compute
  • Separation of storage and compute
  • Two pricing models
  • Partitioning and clustering
  • BigQuery ML
  • Storage Write API
  • BI Engine
  • BigQuery Omni

Both cover

  • Materialised views

What people use each for

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

Apache Doris

  • A team whose MySQL read replica can no longer serve reporting queries and wants an OLAP engine its existing drivers already speaknot BigQuery
  • A real-time dashboard backend needing sub-second aggregation over billions of rows with hundreds of concurrent usersnot BigQuery
  • An ad or ecommerce platform that needs updates and deletes on analytical tables, which append-only OLAP engines handle badlynot BigQuery
  • A data team that wants one SQL endpoint over both internal tables and existing Hive or Iceberg tables in the lakenot BigQuery

BigQuery

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

Where each one falls short

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

Apache Doris

  • Two competing commercial vendors, VeloDB and SelectDB, were founded by overlapping core contributors, which makes the long-term governance and roadmap of the project harder to predict than a single-sponsor project.
  • A large share of design discussion, issue reports and documentation detail originates in Chinese, so teams that do not read it get a thinner picture of known problems and workarounds.
  • Operating a cluster means managing frontend and backend node roles, tablet balancing and compaction tuning, and compaction backlogs under heavy upsert load are a recurring production complaint.
  • The MySQL protocol compatibility is at the wire level, not full MySQL semantics, so queries and functions still need porting and the familiarity can mislead.
  • Managed cloud availability outside China and major clouds is limited compared with ClickHouse or Snowflake, so many Western adopters end up self-hosting whether they wanted to or not.

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

Apache Doris

Free
  • Apache DorisFree
    • Apache 2.0 licence with no usage restrictions
    • All engine features included
    • Community support via mailing list and Slack

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 Apache Doris if

  • You need mysql wire protocol.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes.
  • You also want aggregate and unique key models.
Apache Doris 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 Apache Doris or BigQuery better?
Neither clearly leads. Apache Doris 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, Apache Doris or BigQuery?
Apache Doris starts at Free and BigQuery at Free.
Does Apache Doris or BigQuery run on more platforms?
Apache Doris runs on Linux, Docker, Kubernetes. BigQuery runs on Web, Cloud API.
Can I use Apache Doris for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Doris best used for?
Apache Doris is most often used for a team whose mysql read replica can no longer serve reporting queries and wants an olap engine its existing drivers already speak, a real-time dashboard backend needing sub-second aggregation over billions of rows with hundreds of concurrent users, an ad or ecommerce platform that needs updates and deletes on analytical tables, which append-only olap engines handle badly, a data team that wants one sql endpoint over both internal tables and existing hive or iceberg tables in the lake. Of those, a team whose mysql read replica can no longer serve reporting queries and wants an olap engine its existing drivers already speak and a real-time dashboard backend needing sub-second aggregation over billions of rows with hundreds of concurrent users are not what BigQuery is typically brought in for.
What can Apache Doris do that BigQuery cannot?
Apache Doris covers MySQL wire protocol, Aggregate and unique key models, Multi-catalogue federation, Routine load from Kafka. BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Both handle Materialised views.

Answered from the vendors’ own pages

Apache Doris: Is Apache Doris really free?

Yes, it is Apache 2.0 with no usage restrictions. The commercial products are managed services from VeloDB and SelectDB.

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.

Apache Doris: Can I use my MySQL tools with it?

Yes, it implements the MySQL wire protocol, so clients and BI connectors attach without a new driver, though SQL semantics differ.

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.

Apache Doris: How does it compare with ClickHouse?

Doris handles updates and high concurrency more comfortably; ClickHouse is generally faster on raw single-query scan throughput and has far wider Western support.

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.

Apache Doris: Who maintains it?

The Apache Software Foundation project, with most committers employed by VeloDB or SelectDB.

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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