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Databases · head to head

BigQuery vs TiDB

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
TiDB logo

TiDB

Databases

Apache 2.0 distributed SQL database with MySQL wire compatibility and a separate columnar replica for analytical queries.

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.; TiDB a production cluster needs several placement driver, storage and SQL nodes before it is fault tolerant, so the minimum viable footprint is far larger than a MySQL server and TiDB is never the economical choice for a small database.
  • They diverge on capability: BigQuery covers Serverless compute, TiDB covers MySQL wire compatibility.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and TiDB actually diverge.

Attributes where BigQuery and TiDB differ
AttributeBigQueryTiDB
Pricing modelusage-basedfreemium
PlatformsWeb, Cloud APICloud, AWS, Azure, Google Cloud Platform, Self-managed
Founded20082015

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 TiDB

  • MySQL wire compatibility
  • Horizontal write scaling
  • Distributed ACID transactions
  • TiFlash columnar replica
  • Automatic rebalancing
  • Raft replication
  • Apache 2.0 licence
  • Online schema change

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

TiDB

  • A MySQL workload that has hit the write ceiling of a single primary and would otherwise need an application-level sharding layernot BigQuery
  • Reporting that must run against current transactional data, where the columnar replica removes the delay and the cost of an ETL pipelinenot BigQuery
  • Multi-region deployments needing a single logical database with automatic failover rather than manual primary promotionnot BigQuery
  • Migrating off a sharded MySQL estate where the sharding logic in the application has become the main source of bugs and operational toilnot 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.

TiDB

  • A production cluster needs several placement driver, storage and SQL nodes before it is fault tolerant, so the minimum viable footprint is far larger than a MySQL server and TiDB is never the economical choice for a small database.
  • Every transaction takes a timestamp from the placement driver and crosses the network to storage nodes, so simple point queries are slower than on single-node MySQL and latency-sensitive paths need to be measured, not assumed.
  • MySQL compatibility is at the wire and dialect level but not complete; stored procedures, triggers and events are not supported, so an application that pushed logic into the database cannot simply be repointed.
  • The columnar replica is an extra full copy of the data on its own nodes, so hybrid analytics roughly doubles storage and adds hardware that must be sized and paid for separately.
  • Operating it well requires cluster-specific expertise in TiUP or the Kubernetes operator, region hot spots, and rebalancing behaviour, so the licence is free but the running cost includes an engineer who understands distributed storage.

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

TiDB

Free
  • ServerlessFree
    • 5GB storage
    • 50M request units
    • Free forever tier
  • Dedicated$250/month
    • Dedicated resources
    • SLA guarantees
    • Enterprise support

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 TiDB if

  • You need mysql wire compatibility.
  • You want to start without paying.
  • You work on Cloud, AWS, Azure, Google Cloud Platform, Self-managed.
  • You also want horizontal write scaling.
TiDB review

Questions people ask

Is BigQuery or TiDB better?
Neither clearly leads. BigQuery starts at Free and TiDB at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or TiDB?
BigQuery starts at Free and TiDB at Free.
Does BigQuery or TiDB run on more platforms?
BigQuery runs on Web, Cloud API. TiDB runs on Cloud, AWS, Azure, Google Cloud Platform, Self-managed.
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 TiDB is typically brought in for.
What can BigQuery do that TiDB cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. TiDB covers MySQL wire compatibility, Horizontal write scaling, Distributed ACID transactions, TiFlash columnar replica.

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.

TiDB: Is TiDB a drop-in replacement for MySQL?

At the protocol and dialect level it is close, and most applications connect unchanged. Stored procedures, triggers and events are not supported, and latency characteristics differ, so it needs testing rather than assumption.

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.

TiDB: What licence is it under?

Apache 2.0, for both TiDB and the underlying TiKV storage engine. TiKV is a graduated CNCF project, which is a meaningful governance signal in a market where several competitors moved to source-available licences.

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.

TiDB: Do I need TiFlash?

Only for analytical queries. It is an optional columnar replica; without it TiDB is a distributed transactional database. With it you get analytics on live data at the cost of an additional full copy.

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.

TiDB: Is the managed cloud the same software?

TiDB Cloud runs the same engine, with the control plane, scaling and operational tooling provided as a service. The entry tier is metered differently from a dedicated cluster, so the cost model rather than the engine is what changes.

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.

TiDB: When is TiDB the wrong choice?

When the database is small enough for one server, when latency on single-row lookups is the primary constraint, or when the application depends on MySQL stored procedures and triggers.

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