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BigQuery ML vs Microsoft SQL Server

BigQuery ML logo

BigQuery ML

Machine Learning

Machine learning in BigQuery using SQL

From
Free
Rated
-
Full reviewWebsite
Microsoft SQL Server logo

Microsoft SQL Server

Databases

Enterprise-grade relational database management system

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; Microsoft SQL Server licensing and on-premises deployment costs are high compared to open-source alternatives, with Enterprise Edition exceeding $60,000 for minimum core requirements
  • They diverge on capability: BigQuery ML covers SQL-based ML, Microsoft SQL Server covers T-SQL.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery ML and Microsoft SQL Server actually diverge.

Attributes where BigQuery ML and Microsoft SQL Server differ
AttributeBigQuery MLMicrosoft SQL Server
Pricing modelusage-basedUnknown
PlatformsWebWindows Server, Linux (RHEL, SUSE, Ubuntu), Docker, Azure
CategoryMachine LearningDatabases
Founded20081989

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 Microsoft SQL Server

  • T-SQL
  • ACID Compliance
  • Advanced Security
  • In-memory OLTP
  • Columnstore Indexes
  • Always On Availability
  • Machine Learning Services
  • Azure

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 Microsoft SQL Server
  • Linear and logistic regression on warehouse datanot Microsoft SQL Server
  • K-means clustering and matrix factorisation for recommendationsnot Microsoft SQL Server
  • Time series forecasting with ARIMA_PLUSnot Microsoft SQL Server
  • Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Microsoft SQL Server

Microsoft SQL Server

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

Microsoft SQL Server

  • Licensing and on-premises deployment costs are high compared to open-source alternatives, with Enterprise Edition exceeding $60,000 for minimum core requirements
  • Performance monitoring toolset is insufficient for hybrid cloud environments requiring real-time analytics across multiple deployment types
  • Heavy I/O resource consumption can saturate disk volumes and degrade performance when processing large transaction workloads
  • Always On availability groups with up to 8 secondary replicas are limited to Enterprise edition only; Standard supports only basic availability groups with 2 replicas
  • CPU and memory scaling is capped at 4 sockets or 32 cores on Standard edition, limiting deployments requiring higher compute capacity

Pricing, plan by plan

BigQuery ML

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

Microsoft SQL Server

Free
  • ExpressFree
    • 4 cores maximum
    • 1.4 GB memory per instance
    • 50 GB database size limit
  • DeveloperFree
    • All Enterprise features
    • Non-production use only
  • Standard$3945/per 2-core pack
    • 32 core maximum per instance
    • 256 GB buffer pool memory
    • Basic availability groups with 2 replicas
  • Enterprise$15123/per 2-core pack
    • Unlimited scaling
    • Always On with up to 8 secondaries
    • Advanced security and HA features

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 Microsoft SQL Server if

  • You need t-sql.
  • You want to start without paying.
  • You work on Windows Server, Linux (RHEL, SUSE, Ubuntu), Docker, Azure.
  • You also want acid compliance.
Microsoft SQL Server review

Questions people ask

Is BigQuery ML or Microsoft SQL Server better?
Neither clearly leads. BigQuery ML starts at Free and Microsoft SQL Server at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery ML or Microsoft SQL Server?
BigQuery ML starts at Free and Microsoft SQL Server at Free.
Does BigQuery ML or Microsoft SQL Server run on more platforms?
BigQuery ML runs on Web. Microsoft SQL Server runs on Windows Server, Linux (RHEL, SUSE, Ubuntu), Docker, Azure.
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 Microsoft SQL Server is typically brought in for.
What can BigQuery ML do that Microsoft SQL Server cannot?
BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Microsoft SQL Server covers T-SQL, ACID Compliance, Advanced Security, In-memory OLTP.

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
Microsoft SQL Server: What is the pricing model for SQL Server?

SQL Server uses core-based licensing with per-2-core pack pricing. Enterprise Edition costs approximately $15,123 per 2-core pack (minimum 8 cores). Standard Edition costs approximately $3,945 per 2-core pack. Developer and Express editions are free. Software Assurance adds 25-35% annually for upgrades and support.

Source
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
Microsoft SQL Server: Does SQL Server run on Linux?

Yes. SQL Server 2017 and later run on Linux (Red Hat Enterprise Linux, SUSE Linux Enterprise Server, Ubuntu), Docker containers, and Windows with feature parity including Always On availability groups, Active Directory authentication, and encryption.

Source
Microsoft SQL Server: Is there a free edition of SQL Server?

Yes. SQL Server Express is free and includes all functionality of Enterprise edition for development and testing, with limits of 4 cores, 1,410 MB memory per instance, and 50 GB per database. Developer edition is also free for non-production use.

Source
Microsoft SQL Server: Can SQL Server be deployed offline?

Yes. SQL Server can be installed from offline media on machines without internet access. Microsoft provides complete offline installation packages for SQL Server, SSMS, and supporting components, making deployment in isolated or air-gapped environments feasible.

Source
Microsoft SQL Server: What high availability options does SQL Server provide?

SQL Server offers Always On availability groups (Enterprise only), Always On failover cluster instances, database mirroring, log shipping, and for disaster recovery, failover servers in Azure and Accelerated Database Recovery for faster recovery after failures.

Source
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