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AWS Software / SaaS United States

Perplexity

Enterprise Knowledge Search

Approximately 10 million monthly active users rely on Perplexity to learn new concepts, solve challenges, and find answers. As Perplexity matured and its number of ML models grew, it needed massive compute power to serve users.

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100,000+
By using Amazon SageMaker HyperPod, Perplexity transfers data among different GPUs much faster, which has reduced ML model training time by up to 40 percent.
For example, Perplexity has begun to use Claude 2 through Amazon Bedrock to incorporate advanced capabilities for coding, math, and reasoning into its service.
In fact, the company can handle spike periods with 10,000 concurrent users and over 100,000 queries per hour without compromising latency or affecting the user experience.

Reported impact

Business outcome

Building on its successes, Perplexity is poised to break new ground in generative AI. As part of its forward-looking strategy, the company will experiment with AWS Trainium, a high-performance ML training accelerator, to further improve training throughput. Key reported metrics include reduction in training time Up to 40%; Up to 40%; queries per hour supported 100,000+.

  • Customer Experience
  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Learn how generative AI startup Perplexity performs model training faster and more efficiently using Amazon SageMaker HyperPod. As a transformative force, generative artificial intelligence (AI) encompasses machine learning (ML) algorithms capable of generating new content, from images to text, by learning from vast amounts of data. Perplexity, a company that is currently building one of the world’s first conversational answer engines, is using the power of generative AI to help users find relevant knowledge. Faced with the challenge of optimizing its models for accuracy and precision, Perplexity needed a robust solution capable of handling its computational requirements. With a vision to elevate the user experience, Perplexity has turned to Amazon Web Services (AWS). Source data enters the Perplexity workflow through its application services, processed with Amazon SageMaker HyperPod, Amazon SageMaker, Amazon EC2 P4de Instances.

Data flow

Source data enters the Perplexity workflow through its application services, processed with Amazon SageMaker HyperPod, Amazon SageMaker, Amazon EC2 P4de Instances.

Solution architecture

5 components · 1 layer
  1. Compute
    • Amazon SageMaker HyperPod Provides clustered infrastructure for large-scale foundation model training.
    • Amazon SageMaker Supports model training, fine-tuning, evaluation, or managed inference for the customer workload.
    • Amazon EC2 P4de Instances Provides GPU infrastructure for large-scale training workloads.
    • Amazon EC2 P5 Instances Provides accelerated GPU compute for large AI training or inference jobs.
    • Amazon Bedrock Hosts the foundation models and inference APIs that power the generative AI features.

Architecture clues

  • Because Amazon SageMaker HyperPod automatically monitors cluster health and remediates GPU failures, our developers are able to focus on model building instead of spending time on managing and optimizing the underlying i
  • In fact, the company can handle spike periods with 10,000 concurrent users and over 100,000 queries per hour without compromising latency or affecting the user experience.
  • Perplexity also launched an API to provide users access to its large language models, which runs entirely on AWS and has been optimized by Amazon SageMaker HyperPod.
  • To expand its knowledge base and provide more accurate answers for its users, Perplexity has also adopted Amazon Bedrock, a fully managed service that offers a choice of high-performing foundation models from leading AI

Evidence from the source

100,000+
By using Amazon SageMaker HyperPod, Perplexity transfers data among different GPUs much faster, which has reduced ML model training time by up to 40 percent.
For example, Perplexity has begun to use Claude 2 through Amazon Bedrock to incorporate advanced capabilities for coding, math, and reasoning into its service.
In fact, the company can handle spike periods with 10,000 concurrent users and over 100,000 queries per hour without compromising latency or affecting the user experience.
Perplexity also hosts the publicly available Llama 2 model on Amazon EC2 P5 Instances and uses Amazon SageMaker HyperPod to fine-tune the open-source model using its own data.
Up to 40%
Transparency Evidence & methodology 95/100 reference value · 90% confidence · 2 sources

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 value95/100
  • 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 specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    6 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence90%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 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 transparency9/10
    2 source references 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

    Perplexity customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/perplexity-case-study/
    Source type
    Vendor-published customer story
    Published
    Mar 1, 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
  2. LinkedIn

    Perplexity | LinkedIn

    Third-party
    https://www.linkedin.com/company/perplexity-ai/

    The public company page includes numberOfEmployees value 1408 and a PostalAddress with streetAddress 115 Sansome St, addressLocality San Francisco, and addressCountry US.

    Source type
    structured profile
    Supports
    • Region

Date evidence

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

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