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The role of AWS in building a SaaS product

Apr 12 · 2 min read

In this post, I would give you sneak peak of the cloud components involved in building the product, FaceQuest, and some insights about the infrastructure

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Overview of FaceQuest AWS Architecture

Functionality of part of the product hosted in AWS

  • detect the faces on the picture that is given to the API
  • accomplish the detection of faces, we would need some reference faces and hence the need to store the reference photos somewhere.

AWS components

Computation power to run the container

  1. In order to spend minimal effort in managing the infra and to focus on the application itself we choose Fargate — serverless compute engine managed by AWS.


  1. Less complex — You don’t have to worry about where you’ll deploy your containers, or how you’ll manage and scale them. Instead, you can focus on defining the right parameters for your containers (e.g. compute, storage, and networking) for a successful deployment.
  2. Cost effective (I believe)— Fargate spot charges less than half the price Fargate usage would, by leveraging the spare compute capacity for the fault tolerant applications. By design, FaceQuest supports fault tolerant workloads.

Image Storage

To learn more about this usecase and how we approached it visit here


  1. Cost effective — the costs were controlled because the worker processes downloading & processing the images run in AWS too
  2. Secure

If you would like to take a test drive of FaceQuest, the product that we are building, you can sign up for our Beta program.


Facial recognition as a Service

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By FaceQuest

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