Optimizing Costs and Performance for Generative AI Using Amazon SageMaker with Forethought Technologies
Learn how Forethought Technologies, a provider of generative AI solutions for customer service, reduced costs by up to 80 percent using Amazon SageMaker.
Key Metrics
80%
cost reduction using Amazon SageMaker Serverless Inference66%
cost reduction using Amazon SageMaker multi-model endpointsOverview
Forethought Technologies (Forethought), a customer service software provider, wanted to improve its machine learning (ML) costs and availability as it gained new customers. The company was already using Amazon Web Services (AWS) for ML model training and inference and wanted to be increasingly efficient and scalable with its small cloud infrastructure team.
To achieve its goals, Forethought migrated the inference and hosting of ML models to Amazon SageMaker, which is used to build, train, and deploy ML models for virtually any use case with fully managed infrastructure, tools, and workflows. Using Amazon SageMaker, Forethought improved availability and customer response times and reduced its ML costs by up to 80 percent.

About Forethought Technologies
Forethought Technologies is a startup in the United States providing a generative AI suite for customer service that uses machine learning to transform the customer support life cycle. The company powers over 30 million customer interactions a year.

By migrating to Amazon SageMaker multi-model endpoints, we reduced our costs by up to 66% while providing better latency and better response times for customers.
Jad Chamoun
Director of Core Engineering, Forethought TechnologiesGet Started
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