What challenges did Fotor face before AI implementation?

Reduce inference latency for high concurrency across a global user base. Improve automated asset tagging and content moderation. Integrate generative AI into existing image editing and design workflows.

What was the impact of the AI solution?

Processing time per request dropped from 10-20 seconds to 7-8 seconds. Fotor handles 300 concurrent requests per second. Hundreds of generative AI features were launched. Daily active users increased tenfold and user satisfaction rose 20%.

Source

Evidence: Medium50/100

Fotor: Generative AI image editing using Amazon Bedrock, SageMaker, and Rekognition

Use case typeContent generationUpdated Jul 1, 2024

Fotor uses Amazon Bedrock, Amazon SageMaker, Amazon Rekognition, Amazon SQS, Amazon SNS, and Amazon EC2 to power generative AI image editing and design tools for 600 million users worldwide. The solution reduces inference latency, automates image tagging, moderates user-generated content, and supports custom image and video model development.

Organization: Fotor
Industry: Consumer & Food
Location: China
Published: July 2024

Reported outcomes

Inference time per request: 20% to 65%

Time & speed

Concurrent requests: 300 requests per secondUser satisfaction: +20%

Catalog median for time & speed deployments: −50% across 295 reported metrics. Compare benchmarks →

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Inference time per request: 20-65% decrease

AWS SolutionsJul 1, 2024Customer storyInferred claimMedium evidence strength

reducing processing time per request from 10-20 seconds to 7-8 seconds

Normalized claim

Concurrent requests: 300 requests per second increase

AWS SolutionsJul 1, 2024Customer storyExplicit claimMedium evidence strength

enabling the processing of 300 concurrent requests per second

Normalized claim

Daily active users: 1,000% increase

AWS SolutionsJul 1, 2024Customer storyInferred claimMedium evidence strength

resulting in a tenfold increase in daily active users

Normalized claim

User satisfaction: 20% increase

AWS SolutionsJul 1, 2024Customer storyExplicit claimMedium evidence strength

resulted in a 20% increase in satisfaction among international users

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Fotor
Provider: AWS
Maturity: Unknown
Linked source: AWS Solutions

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 3

1Content generation
2Content review and revision
3Customer personalization

Amazon SageMaker asynchronous inference with SQS/SNS and EC2 scheduling to process high concurrency requests.
Amazon Bedrock LLMs for semantic expansion and image/visual annotation.
Amazon Rekognition for content moderation confidence scores.
Amazon SageMaker model training to develop proprietary image and video models.

Technologies

Amazon Bedrock Amazon SageMaker Amazon Rekognition Amazon SQS Amazon SNS Amazon EC2

Processing time per request dropped from 10-20 seconds to 7-8 seconds.
Fotor handles 300 concurrent requests per second.
Hundreds of generative AI features were launched.
Daily active users increased tenfold and user satisfaction rose 20%.

Architecture

Fotor runs its services across multiple AWS regions and uses Amazon SageMaker asynchronous inference integrated with Amazon SQS, Amazon SNS, and Amazon EC2 scheduling to process high-concurrency requests. It uses Amazon Bedrock for LLM-based image annotation and semantic expansion, Amazon Rekognition for moderation confidence scores, and Amazon SageMaker training for proprietary image and video model development.

Sources & evidence1

Evidence: Medium50/100Evidence strength