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Databases · head to head

Microsoft SQL Server vs Amazon Redshift ML

Microsoft SQL Server logo

Microsoft SQL Server

Databases

Enterprise-grade relational database management system

From
Free
Rated
-
Full reviewWebsite
Amazon Redshift ML logo

Amazon Redshift ML

Machine Learning

SQL statements in Redshift that train models on SageMaker and return them as functions

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: 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; Amazon Redshift ML training is billed by SageMaker separately from Redshift, so a feature that looks like a free SQL statement produces a second line item on a different part of the bill that the analyst who ran it usually cannot see.
  • They diverge on capability: Microsoft SQL Server covers T-SQL, Amazon Redshift ML covers CREATE MODEL in SQL.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Microsoft SQL Server and Amazon Redshift ML differ
AttributeMicrosoft SQL ServerAmazon Redshift ML
Pricing modelUnknownusage-based
PlatformsWindows Server, Linux (RHEL, SUSE, Ubuntu), Docker, AzureWeb
CategoryDatabasesMachine Learning
Founded19892006

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 Microsoft SQL Server

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

Only in Amazon Redshift ML

  • CREATE MODEL in SQL
  • Automatic model selection
  • Local inference
  • Bring your own model
  • Algorithm selection
  • Cost ceiling controls
  • Existing warehouse security
  • Batch and interactive scoring

What people use each for

The jobs each tool is most often brought in to do.

Microsoft SQL Server

  • Transaction processingnot Amazon Redshift ML
  • Data storagenot Amazon Redshift ML
  • Application backendnot Amazon Redshift ML
  • Reportingnot Amazon Redshift ML
  • Data analyticsnot Amazon Redshift ML

Amazon Redshift ML

  • Adding a churn or propensity score to an existing dashboard where the data is already in Redshift and nobody needs a bespoke modelnot Microsoft SQL Server
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Microsoft SQL Server
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Microsoft SQL Server
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Microsoft SQL Server

Where each one falls short

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

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

Amazon Redshift ML

  • Training is billed by SageMaker separately from Redshift, so a feature that looks like a free SQL statement produces a second line item on a different part of the bill that the analyst who ran it usually cannot see.
  • Autopilot searches many candidate models by default and the duration and cost of CREATE MODEL scale with the data size and the MAX_CELLS setting, so an unconstrained statement against a large table is an expensive accident rather than an experiment.
  • Local inference runs on the Redshift cluster itself, so scoring millions of rows competes for the resources the warehouse exists to provide, and the remote inference alternative adds a per-batch network call plus an hourly SageMaker endpoint charge that persists whether or not anyone queries it.
  • The supported problem types are limited to what the exposed algorithms cover, so anything involving text, images, sequences, a custom loss function or a bespoke evaluation metric is out of scope and has to be built conventionally.
  • There is no retraining schedule, drift detection or model registry, so a model created by a statement stays exactly as trained until somebody remembers to recreate it, and nothing in the warehouse will report that its accuracy has decayed.

Pricing, plan by plan

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

Amazon Redshift ML

Free
  • Free TrialFree
    • 2-month trial
    • 750 DC2.Large hours
  • On-Demand$0.25/hour
    • Per-node pricing
    • SageMaker training

Which should you pick?

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

Choose Amazon Redshift ML if

  • You need create model in sql.
  • You want to start without paying.
  • You also want automatic model selection.
Amazon Redshift ML review

Questions people ask

Is Microsoft SQL Server or Amazon Redshift ML better?
Neither clearly leads. Microsoft SQL Server starts at Free and Amazon Redshift ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Microsoft SQL Server or Amazon Redshift ML?
Microsoft SQL Server starts at Free and Amazon Redshift ML at Free.
Does Microsoft SQL Server or Amazon Redshift ML run on more platforms?
Microsoft SQL Server runs on Windows Server, Linux (RHEL, SUSE, Ubuntu), Docker, Azure. Amazon Redshift ML runs on Web.
Can I use Microsoft SQL Server for free?
Both have a free tier, so you can try either at no cost before committing.
What is Microsoft SQL Server best used for?
Microsoft SQL Server is most often used for transaction processing, data storage, application backend, reporting. Of those, transaction processing and data storage are not what Amazon Redshift ML is typically brought in for.
What can Microsoft SQL Server do that Amazon Redshift ML cannot?
Microsoft SQL Server covers T-SQL, ACID Compliance, Advanced Security, In-memory OLTP. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

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
Amazon Redshift ML: Does it require SageMaker?

Yes. Redshift ML is an interface; the training happens in SageMaker and needs an IAM role and an S3 bucket for the intermediate data.

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
Amazon Redshift ML: Is there an extra charge?

The SQL interface is part of Redshift, but the training runs as a SageMaker job charged at SageMaker rates, and a remote inference endpoint is billed for as long as it exists.

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
Amazon Redshift ML: What kinds of model can it build?

Regression, binary and multiclass classification through the automatic path, plus direct use of XGBoost, linear learner, multilayer perceptron and K-means. Anything beyond structured tabular prediction is out of scope.

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
Amazon Redshift ML: Can I use a model I trained myself?

Yes, through the bring-your-own-model path, either compiled into the cluster for local inference or called as a remote SageMaker endpoint.

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
Amazon Redshift ML: Does it retrain automatically?

No. Retraining means running CREATE MODEL again, on a schedule you build yourself, and nothing in the product monitors whether it is needed.

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