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Databases · head to head

BigQuery vs Vitess

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
Vitess logo

Vitess

Databases

Scalable database clustering system for horizontal scaling of MySQL

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.; Vitess vTGate scatter queries without sharding key incur significant performance penalties
  • They diverge on capability: BigQuery covers Serverless compute, Vitess covers Horizontal Sharding.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Vitess actually diverge.

Attributes where BigQuery and Vitess differ
AttributeBigQueryVitess
Pricing modelusage-basedUnknown
PlatformsWeb, Cloud APILinux, macOS, Docker, Kubernetes
Founded20082010

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 Vitess

  • Horizontal Sharding
  • Connection Pooling
  • Query Routing
  • Online Schema Changes
  • Shard Management
  • Replication Management
  • Automated Failover
  • MySQL

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

Vitess

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

Vitess

  • VTGate scatter queries without sharding key incur significant performance penalties
  • Foreign key constraints not enforced across shards, requiring application-level integrity handling
  • Single primary per keyspace limits multi-region write capabilities
  • Distributed transactions without proper sharding key routing suffer performance degradation

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

Vitess

Free

No published plan breakdown. See the Vitess review.

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 Vitess if

  • You need horizontal sharding.
  • You want to start without paying.
  • You work on Linux, macOS, Docker, Kubernetes.
  • You also want connection pooling.
Vitess review

Questions people ask

Is BigQuery or Vitess better?
Neither clearly leads. BigQuery starts at Free and Vitess at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Vitess?
BigQuery starts at Free and Vitess at Free.
Does BigQuery or Vitess run on more platforms?
BigQuery runs on Web, Cloud API. Vitess runs on Linux, macOS, 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 Vitess is typically brought in for.
What can BigQuery do that Vitess cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Vitess covers Horizontal Sharding, Connection Pooling, Query Routing, Online Schema Changes.

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.

Vitess: Is Vitess free to use?

Yes. Vitess is completely free and open source under the Apache 2.0 license. It is a graduated CNCF project with no licensing costs or pricing tiers.

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.

Vitess: What databases does Vitess support?

Vitess supports MySQL and MariaDB as backend databases. It acts as a middleware layer that adds sharding and orchestration capabilities on top of these databases.

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.

Vitess: Does Vitess require Kubernetes to run?

No. Vitess can run on Kubernetes using the Vitess Operator, but it can also be deployed on traditional infrastructure. Kubernetes integration is optional and provides additional automation benefits.

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.

Vitess: How does Vitess handle cross-shard transactions?

Vitess supports distributed transactions across shards, but they require queries to be routed through the sharding key. Transactions without a proper sharding key can result in slower performance.

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.

Vitess: Does Vitess enforce foreign key constraints?

Vitess does not enforce foreign key constraints across shards by default. Referential integrity must be managed at the application layer, though per-database support can be enabled with limitations.

Source
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