AWS Software / SaaS Global

Elastic

Enterprise Knowledge Search · Security and Compliance

Elastic aims to help customers find information faster while running their applications smoothly and protecting against cyberthreats. Operating from the premise that search functionality is fundamental to critical business processes, Elastic offers solutions in search, observability, and security, built on Amazon Web Services (AWS).

Reported impact

Business outcome

In the future, Elastic will provide its customers access to even more FMs through Amazon Bedrock. For example, Elastic plans to use Amazon Bedrock to access additional high-performing models that support image recognition, voice-to-text conversion, and multilingual capabilities. “Using AWS, we give our customers the right tools to get the job done,” says Spiteri.

Solution

Elastic aims to help customers find information faster while running their applications smoothly and protecting against cyberthreats. Operating from the premise that search functionality is fundamental to critical business processes, Elastic offers solutions in search, observability, and security, built on Amazon Web Services (AWS). Elastic AI Assistant helps security operations teams with alert triage and investigation, provides suggestions for optimal code efficiency, and converts queries from other platforms into Elastic syntax. ESRE uses retrieval-augmented generation (RAG) to fetch company data to enrich prompts and responses while considering the privacy and permissions of the underlying content.

Solution architecture

1 components · 1 layer
  1. Compute
    • Amazon Bedrock Hosts the foundation models and generative AI application logic.

Architecture clues

  • Customers using Amazon Bedrock through Elastic adjust to data residency requirements by establishing connectors from multiple AWS Regions .
  • Elastic, an AWS Partner , supports conventional keyword and text-based searches as well as vector search capabilities using its AI-powered Elasticsearch Relevance Engine (ESRE), wh

Evidence from the source

Elastic aims to help customers find information faster while running their applications smoothly and protecting against cyberthreats.
Elastic, an AWS Partner , supports conventional keyword and text-based searches as well as vector search capabilities using its AI-powered Elasticsearch Relevance Engine (ESRE), which runs on AWS.
For example, Elastic plans to use Amazon Bedrock to access additional high-performing models that support image recognition, voice-to-text conversion, and multilingual capabilities.
In the future, Elastic will provide its customers access to even more FMs through Amazon Bedrock.
Operating from the premise that search functionality is fundamental to critical business processes, Elastic offers solutions in search, observability, and security, built on Amazon Web Services (AWS).
“The number-one challenge security teams face today is figuring out the meaning behind all the alerts that are activated,” says James Spiteri, Elastic’s director of product management for security.
Transparency Evidence & methodology 52/100 reference value · 83% 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 value52/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth15/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity12/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence83%
  • 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 specificity6/10
    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

    Elastic customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/elastic-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.