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Machine Learning · head to head

BigQuery ML vs TiDB

BigQuery ML logo

BigQuery ML

Machine Learning

Machine learning in BigQuery using SQL

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 ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; 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 ML covers SQL-based ML, 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 ML and TiDB actually diverge.

Attributes where BigQuery ML and TiDB differ
AttributeBigQuery MLTiDB
Pricing modelusage-basedfreemium
PlatformsWebCloud, AWS, Azure, Google Cloud Platform, Self-managed
CategoryMachine LearningDatabases
Founded20082015

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

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 ML

  • SQL-based ML
  • AutoML Tables
  • Model export
  • Prediction functions
  • Feature preprocessing
  • BigQuery
  • Vertex AI
  • TensorFlow

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 ML

  • Training models in SQL without exporting datanot TiDB
  • Linear and logistic regression on warehouse datanot TiDB
  • K-means clustering and matrix factorisation for recommendationsnot TiDB
  • Time series forecasting with ARIMA_PLUSnot TiDB
  • Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot 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 ML
  • Reporting that must run against current transactional data, where the columnar replica removes the delay and the cost of an ETL pipelinenot BigQuery ML
  • Multi-region deployments needing a single logical database with automatic failover rather than manual primary promotionnot BigQuery ML
  • Migrating off a sharded MySQL estate where the sharding logic in the application has become the main source of bugs and operational toilnot BigQuery ML

Where each one falls short

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

BigQuery ML

  • Not available in BigQuery's Standard edition, so the cheapest tier cannot use it
  • Billed through BigQuery compute and storage rather than as its own product, so training cost tracks data scanned
  • Remote models incur extra Agent Platform charges on top
  • Externally trained model types such as boosted trees and AutoML run through Agent Platform rather than inside BigQuery

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 ML

Free
  • Free TierFree
    • 10GB storage
    • 1TB queries
  • On-Demand$5/TB
    • Pay per TB scanned
    • ML training costs

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 ML if

  • You need sql-based ml.
  • You want to start without paying.
  • You also want automl tables.
BigQuery ML 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 ML or TiDB better?
Neither clearly leads. BigQuery ML 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 ML or TiDB?
BigQuery ML starts at Free and TiDB at Free.
Does BigQuery ML or TiDB run on more platforms?
BigQuery ML runs on Web. TiDB runs on Cloud, AWS, Azure, Google Cloud Platform, Self-managed.
Can I use BigQuery ML for free?
Both have a free tier, so you can try either at no cost before committing.
What is BigQuery ML best used for?
BigQuery ML is most often used for training models in sql without exporting data, linear and logistic regression on warehouse data, k-means clustering and matrix factorisation for recommendations, time series forecasting with arima_plus. Of those, training models in sql without exporting data and linear and logistic regression on warehouse data are not what TiDB is typically brought in for.
What can BigQuery ML do that TiDB cannot?
BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. TiDB covers MySQL wire compatibility, Horizontal write scaling, Distributed ACID transactions, TiFlash columnar replica.

Answered from the vendors’ own pages

BigQuery ML: How much does Google Cloud BigQuery ML cost?

BigQuery ML pricing is not specified separately on Google Cloud's pricing page. It follows the same pay-as-you-go model as BigQuery, charging per terabyte of data scanned during analysis. Customers receive $300 in free credits and can use 20+ products free up to monthly limits.

Source
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 ML: Does Google Cloud offer a free trial?

Yes, new customers get $300 in free credits and all customers can use 20+ Google Cloud products free up to their monthly usage limits.

Source
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.

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.

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.

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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