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AWS Software / SaaS United States

Adobe

Application Refactoring

Rather than rely on open source models, Adobe decided to train its own foundational generative AI models tailored for creative use cases. "We want to be in control of the quality and capabilities our models need to have," explained Costin.

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20x scale up in model training achieved for enterprise scaling
As demand spiked, Adobe scaled up training by 20x in six months, aided by Amazon EC2 Reserved Instances .

Reported impact

Business outcome

family of generative AI models, including models that generate high-quality imagery, graphics, illustrations, designs, layouts, documents, marketing assets, and more.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

For over four decades, Adobe has been at the forefront of enabling creativity and digital experiences. As a trusted partner to enterprises, small businesses, and creators like graphic designers, illustrators, photographers, videographers, Adobe embraced generative AI to help its customers navigate this technological disruption. By training its own Firefly family of generative AI models, Adobe was able to integrate generative AI into its flagship creative software products, such as Photoshop, to serve creatives where they're working. "Deep integration is what got us this amazing usage in helping our customers," said Alexandru Costin vice president, Generative AI and Sensei at Adobe. Adobe created an AI superhighway on AWS to build an AI training platform and data pipelines to rapidly iterate models.

Data flow

Adobe created an AI superhighway on AWS to build an AI training platform and data pipelines to rapidly iterate models. Adobe built its solution with Amazon Elastic Compute Cloud (Amazon EC2) P5 and P4d instances powered by NVIDIA GPUs, Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Elastic Block Store (Amazon EBS) Amazon Elastic Fabric Adapter (EFA) . Adobe continuously monitors and synchronizes data across multiple sources, ensuring that its models are trained on the most up-to-date and relevant information.

Solution architecture

5 components · 2 layers
  1. Compute
    • Amazon EC2 P5 Instances Provides GPU compute for the model training behind the modernized application workflows.
    • Amazon EKS Runs containerized services that deliver the modernized application workflows.
    • Amazon Bedrock Runs the foundation model layer for the modernized application workflows.
  2. Storage
    • Amazon S3 Stores source data, documents, or generated artifacts used by the modernized application workflows.
    • Amazon FSx for Lustre Provides high-throughput file storage for the modernized application workflows.

Architecture clues

  • Adobe also used Amazon Simple Storage Service (Amazon S3) to serve as the data lake and primary repository for the vast troves of data.
  • Adobe built its solution with Amazon Elastic Compute Cloud (Amazon EC2) P5 and P4d instances powered by NVIDIA GPUs, Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Elastic Block Store (Amazon...
  • Adobe used Amazon FSx for Lustre high-performance file storage, for fast access to data and to make sure GPU resources are never left idle.
  • They implemented distributed training techniques like pipeline parallelism to efficiently utilize tensor cores.
  • With Firefly, Adobe does continuous testing to mitigate against any harmful biases and provides user feedback mechanisms to remediate concerns in real-time, fostering transparency and accountability.

Evidence from the source

Adobe also used Amazon Simple Storage Service (Amazon S3) to serve as the data lake and primary repository for the vast troves of data.
Adobe built its solution with Amazon Elastic Compute Cloud (Amazon EC2) P5 and P4d instances powered by NVIDIA GPUs, Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Elastic Block Store (Amazon EBS) Amazon Elastic Fabric Adapter...
Adobe created an AI superhighway on AWS to build an AI training platform and data pipelines to rapidly iterate models.
Adobe used Amazon FSx for Lustre high-performance file storage, for fast access to data and to make sure GPU resources are never left idle.
By building on AWS, Adobe could accelerate AI training rather than building from scratch, enabling the company to focus on its differentiators and not the heavy lifting.
The result is a cycle of continuous improvement, where human creativity and machine learning work in tandem, each informing and enhancing the other.
Transparency Evidence & methodology 91/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value91/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. AWS

    Adobe customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/adobe-case-study/
    Source type
    Vendor-published customer story
    Published
    Jul 30, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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