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Databases · head to head

BigQuery vs StarRocks

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
StarRocks logo

StarRocks

Databases

Apache 2.0 MPP analytical database built for joins on open table formats

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.; StarRocks self-hosting is a genuine operations job: frontend and backend node roles, tablet distribution, compaction and materialised view refresh all need an owner, and there is no small-team-friendly single-binary mode.
  • They diverge on capability: BigQuery covers Serverless compute, StarRocks covers Cost-based optimiser.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which BigQuery and StarRocks actually diverge.

Attributes where BigQuery and StarRocks differ
AttributeBigQueryStarRocks
Pricing modelusage-basedOpen source, no licence fee
PlatformsWeb, Cloud APILinux, Docker, Kubernetes
Founded2008Unknown

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
  • BigQuery ML
  • Storage Write API
  • BI Engine
  • BigQuery Omni

Only in StarRocks

  • Cost-based optimiser
  • Lakehouse query engine
  • Primary key tables
  • Shared-data mode
  • MySQL wire protocol

Both cover

  • Materialised views

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

StarRocks

  • Customer-facing analytics where queries join a fact table to several dimensions and must return in well under a secondnot BigQuery
  • Querying an Iceberg lakehouse directly without copying data into a proprietary warehouse formatnot BigQuery
  • Replacing a ClickHouse deployment that has become unmanageable because every new question needs another denormalised tablenot BigQuery
  • Real-time analytics fed by change data capture where rows must be updated in place rather than appendednot 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.

StarRocks

  • Self-hosting is a genuine operations job: frontend and backend node roles, tablet distribution, compaction and materialised view refresh all need an owner, and there is no small-team-friendly single-binary mode.
  • CelerData is by far the dominant contributor despite Linux Foundation stewardship, so the practical roadmap risk is the same as any single-vendor open source project.
  • It inherits a MySQL-flavoured SQL dialect from its Doris ancestry, so queries written for PostgreSQL, Snowflake or Trino need rewriting rather than porting.
  • Ecosystem support is thinner than ClickHouse or Trino: fewer client libraries, fewer managed hosting options and a much smaller pool of engineers who have run it in production.
  • Memory pressure under concurrent large joins is a common production failure, and the tuning knobs for query memory limits are unforgiving compared with a cloud warehouse that just scales.

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

StarRocks

Free
  • StarRocksFree
    • Apache 2.0 licence
    • Linux Foundation governance
    • No usage or node limits
  • CelerData Cloud$undefined/year
    • Managed StarRocks from the primary contributor
    • BYOC and serverless deployment options
    • Enterprise support and SLAs

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 StarRocks if

  • You need cost-based optimiser.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes.
  • You also want lakehouse query engine.
StarRocks review

Questions people ask

Is BigQuery or StarRocks better?
Neither clearly leads. BigQuery starts at Free and StarRocks at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or StarRocks?
BigQuery starts at Free and StarRocks at Free.
Does BigQuery or StarRocks run on more platforms?
BigQuery runs on Web, Cloud API. StarRocks runs on Linux, Docker, Kubernetes.
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 StarRocks is typically brought in for.
What can BigQuery do that StarRocks cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. StarRocks covers Cost-based optimiser, Lakehouse query engine, Primary key tables, Shared-data mode. Both handle Materialised views.

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.

StarRocks: Is StarRocks open source?

Yes, Apache 2.0, governed under the Linux Foundation since 2023.

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.

StarRocks: How does it differ from ClickHouse?

StarRocks is built for joins across a star schema with a cost-based optimiser; ClickHouse is fastest on denormalised single tables.

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.

StarRocks: Who maintains it?

CelerData, formerly StarRocks Inc, is the dominant contributor and sells the managed service.

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.

StarRocks: Can it query Iceberg tables directly?

Yes, along with Hudi, Delta Lake, Hive and Paimon, with a local cache for repeat queries.

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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