AWS Software / SaaS United States

Vyond

Vyond aimed to enhance its enterprise-grade video creation solution, Vyond Studio, by introducing Vyond Go—a generative artificial intelligence (AI)-powered text-to-image service that produces high-quality videos in seconds. To facilitate hosting for multiple generative AI models and ensure scalability for a global customer base, the company chose to run Vyond Go on AWS.

1,000,000+ of global users adopting Vyond services
20 percent
20% savings with Amazon EC2 Inf2 instances
30 seconds

Reported impact

Business outcome

Millions of users and more than 20,000 businesses worldwide are using Vyond Studio and Vyond Go for swift text-to-video generation. Utilizing the capabilities of generative AI on AWS, these enterprises transform text prompts into videos in 30 seconds, saving weeks of work. “We're helping countless clients overcome the apprehension and cost associated with making high-quality enterprise videos,” states Harney.

  • Cost Reduction
  • Speed to Market

Solution

Vyond aimed to enhance its enterprise-grade video creation solution, Vyond Studio, by introducing Vyond Go—a generative artificial intelligence (AI)-powered text-to-image service that produces high-quality videos in seconds. To facilitate hosting for multiple generative AI models and ensure scalability for a global customer base, the company chose to run Vyond Go on AWS. Vyond replicated the AWS architecture used in Vyond Studio when implementing Vyond Go. To enhance scalability and cost efficiency, the company utilized to host several machine learning (ML) models, including Stable Diffusion, a generative AI model that produces unique stylistic images from text and image prompts. In addition, Vyond deployed Amazon EC2 Inf1 instances for other AI-powered features, such as visual content search, video-to-action, and background removal. Says Harney, “By adopting Amazon EC2 Inf2 instances for our generative AI workloads, we saw a saving of 20 percent.

Data flow

Data is stored in Amazon OpenSearch Service and processed by Amazon EC2 Inf2 instances, Amazon EC2 Inf1 instances.

Solution architecture

5 components · 3 layers
  1. Compute
    • Amazon EC2 Inf2 instances Run AI inference workloads with lower cost and better efficiency.
    • Amazon EC2 Inf1 instances Run AI inference for production media-generation features.
    • AWS Inferentia Accelerates deep-learning inference with purpose-built AWS silicon.
  2. Storage
    • Amazon OpenSearch Service Stores and retrieves indexed or vectorized content for search and RAG workflows.
  3. Orchestration
    • Amazon Elastic Kubernetes Service Runs containerized services and scales application workloads across clusters.

Architecture clues

  • Says Harney, “By adopting Amazon EC2 Inf2 instances for our generative AI workloads, we saw a saving of 20 percent.
  • To create video assets in seconds, Vyond uses Stable Diffusion, a text-to-image model, which Vyond hosts on Amazon Elastic Compute Cloud (Amazon EC2) Inf2 instances .
  • “We’re helping our customers quickly and easily turn any kind of communication into video and with complete control over the process and the finished product.

Evidence from the source

In 2021, —a global video creation platform based in the US with offices in Hong Kong and Taiwan—secured $50 million in growth capital to enhance its position in the enterprise video creation market.
The company helps businesses craft high-quality animated videos, fostering engagement and education among employees and customers.
To enhance scalability and cost efficiency, the company utilized to host several machine learning (ML) models, including Stable Diffusion, a generative AI model that produces unique stylistic images from text and image p
To facilitate hosting for multiple generative AI models and ensure scalability for a global customer base, the company chose to run Vyond Go on AWS.
Vyond aimed to enhance its enterprise-grade video creation solution, Vyond Studio, by introducing Vyond Go—a generative artificial intelligence (AI)-powered text-to-image service that produces high-quality videos in seco
Vyond replicated the AWS architecture used in Vyond Studio when implementing Vyond Go.
Transparency Evidence & methodology 100/100 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 value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 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

    Vyond customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/case-study-vyond
    Source type
    Vendor-published customer story
    Published
    Mar 19, 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.