Create powerful, portable analytics applications

Connect to, and process, your data wherever it lives—in the cloud or in on-premises data stores—and deliver analytics where they're needed. Get support for a full range of R-based analytics, while taking advantage of big data statistics, predictive modeling, and machine-learning capabilities.

Create powerful, portable analytics applications

Create powerful, portable analytics applications

Create powerful, portable analytics applications

Create powerful, portable analytics applications

Create powerful, portable analytics applications

R Server features

Bring analytics to your data

Analyze your data wherever it lives—on-premises, in the cloud, or in a hybrid environment—without having to move it.

Choose the tools you prefer

Use the best tool and language for each data science process, including RStudio, R Tools for Visual Studio, SS*S, and R Client.

Build artificial intelligence-enabled apps

Enrich your R-based analytics applications by using industry-leading machine learning and artificial intelligence innovations from Microsoft.

Scale R analytics for big data

Scale analytics from individual servers to large clusters as your business needs change.

Experience enhanced, flexible deployment

Reduce time and error by deploying directly without model conversion. Easily deploy to a variety of platforms at scale and with robust security.

Access the latest innovations

Get the latest scalable capabilities from Microsoft, plus capitalize on open source innovation—including over 9,000 CRAN R packages.

Adapt to future needs

Ensure stability with a R analytics solution that scales and adapts to future technology and platform changes.

Get support you trust

Deploy with confidence knowing you have 24x7 support from Microsoft—whether in Hadoop, Spark, Linux, Windows, SQL Server, or Teradata environments.

Boost big data performance

When your data stores grow, Microsoft R Server can be deployed to perform at scale wherever your big data lives—including databases such as SQL Server 2016, Hadoop clusters, data warehouses, and even data stores in the cloud.

Back To Top
close-button