How Omnicom Uses AWS to Cut Infrastructure Costs 90% and Build an AI-Powered Marketing Platform

Omnicom Group, the global marketing network serving 5,000+ clients across 70+ countries, migrated 75 petabytes of data from 9 data centers to AWS and built an AI-powered marketing platform using Amazon Bedrock, SageMaker, and Bedrock AgentCore. The migration cut compute infrastructure costs by 90% while enabling real-time processing of 400 billion daily events and AI-driven creative content generation.

Outcomes

90%Reduction in compute infrastructure costs
75 petabytesData migrated
400BDaily events processed

Tools & Technologies

1AB
Amazon Bedrock
Fully managed service for accessing foundation models from leading AI companies via AWS.
2AR
Amazon Redshift
Cloud data warehouse optimized for running fast SQL analytics on large datasets at scale.
3AB
Amazon Bedrock AgentCore
Managed infrastructure for deploying and orchestrating AI agents at enterprise scale
4AS
Amazon SageMaker
Managed machine learning service for building, training, and deploying ML models in the cloud.

AI Categories

Challenge

Omnicom operated 9 separate data centers managing 9,000+ data sources and 400 billion daily events, with infrastructure complexity consuming resources that should have been directed toward client campaigns and marketing innovation, while limiting the company's ability to build AI capabilities at scale.

Solution

Omnicom migrated 75 petabytes from 9 data centers to AWS using Amazon S3 and Redshift, then built an AI-powered marketing platform on Amazon SageMaker, Bedrock, Amazon Nova, and Bedrock AgentCore — enabling real-time processing of 400B daily events and AI-driven creative content generation at 90% lower infrastructure cost.

Full Story

Omnicom operates one of the world's largest marketing and communications networks, serving over 5,000 clients across 70+ countries with 75,000 employees. The company had accumulated 9 separate data centers over decades of organic growth and acquisitions — each a siloed operational burden requiring dedicated infrastructure management. Teams spent excessive time and budget keeping infrastructure running rather than developing campaigns and serving clients. Managing 9,000+ data sources while processing 400 billion daily events across fragmented infrastructure created both cost inefficiency and analytical limitations.

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