AWS Software / SaaS United Kingdom

Synthesia

Application Refactoring

Founded in 2017, Synthesia develops AI technology that customers can use to create instructional videos featuring stock or custom AI avatars.

30x improvement in ML model training throughput
456% recent user growth
For example, the compute-heavy workloads involved in ML model training and inference run on NVIDIA GPUs in AWS Europe Regions, which are powered virtually 100 percent by renewable...
The company adopted Amazon EC2 P5 Instances powered by NVIDIA H100 Tensor Core GPUs and Amazon EC2 P4 Instances powered by NVIDIA A100 Tensor Core GPUs accelerated model training...

Reported impact

Business outcome

Synthesia is now working toward near real-time, interactive video rendering for its customers. As the company continues to grow, it plans to create even more advanced 3D avatars using large-scale 3D datasets stored in its data lake.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Businesses looking to enrich their brand presence and communication with video content have traditionally faced steep production costs and a long process. Artificial intelligence (AI) technology startup Synthesia offers generative AI video creation as a service for customers to create realistic videos from text prompts in just minutes. Synthesia wanted to make its video-generation software as a service (SaaS), Synthesia Studio, even faster by improving its machine learning (ML) models and upgrading its computing infrastructure for training these models. The fast-growing company needed a scalable infrastructure with high performance, so it used NVIDIA GPU–accelerated instances on Amazon Web Services (AWS) to significantly speed up and scale its ML training to support a user base growth of 456 percent.

Data flow

It also uses Amazon EC2 G5 Instances powered by NVIDIA A10G Tensor Core GPUs for data processing and to optimize video rendering runtime. "We batch process datasets for new experiments, often running up to 100 instances at the same time," says Starck.

Solution architecture

5 components · 2 layers
  1. Compute
    • Amazon EKS Runs containerized services that deliver the modernized application workflows.
    • Amazon EC2 Provides scalable compute capacity for the modernized application workflows.
    • Amazon EC2 P5 Instances Provides GPU compute for the model training behind the modernized application workflows.
    • Amazon EC2 P4 Instances Provides GPU compute for the model training behind the modernized application workflows.
  2. Storage
    • Amazon S3 Stores source data, documents, or generated artifacts used by the modernized application workflows.

Architecture clues

  • As the company continues to grow, it plans to create even more advanced 3D avatars using large-scale 3D datasets stored in its data lake.
  • It also uses NVIDIA CUDA, a GPU-based parallel computing platform and programming model, for video rendering and inference.
  • Synthesia is now working toward near real-time, interactive video rendering for its customers.
  • The fast-growing company needed a scalable infrastructure with high performance, so it used NVIDIA GPU–accelerated instances on Amazon Web Services (AWS) to significantly speed up and scale its ML...
  • Using these managed services and NVIDIA GPU–powered compute instances, Synthesia reduced ML model training time for smaller voice models from days to hours.

Evidence from the source

"AWS has been embedded in our company from the beginning," says Jon Starck, CTO of Synthesia.
AWS provides a wealth of services that we wanted to adopt, and it has key initiatives for startups." For example, the company took advantage of the initial free compute capacity that AWS provides.
In 2023, it decided to optimize its ML pipeline on AWS.
Synthesia chose AWS because its fully managed services meet several critical requirements.
The fast-growing company needed a scalable infrastructure with high performance, so it used NVIDIA GPU–accelerated instances on Amazon Web Services (AWS) to significantly speed up and scale its ML training to support a user base growth of...
The two teams also had regular meetings, and Synthesia saw the value in AWS support as it scaled up rapidly.
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

    Synthesia customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/synthesia-case-study/
    Source type
    Vendor-published customer story
    Published
    Jul 25, 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.