AWS Software / SaaS global

Qdrant

Enterprise Knowledge Search

Qdrant needed a scalable and affordable way to offer enterprise-grade vector search for unstructured data so customers could build generative AI and analytics applications without sacrificing low latency or ease of use.

saved more than 30 percent in total costs

Reported impact

Business outcome

Qdrant gives enterprises a managed AWS-hosted vector database for conversational retrieval and other unstructured-data use cases, while reducing its own total resource costs by more than 30 percent through fully managed AWS infrastructure.

  • Cost Reduction
  • Productivity Improvement
  • Scalability

Solution

Qdrant hosts its vector search database on AWS as a managed cloud service to help enterprises retrieve information from unstructured data for generative AI and analytics use cases. The platform indexes private data as vectors so ML and AI teams can run low-latency similarity queries for conversational search, facial recognition, anomaly detection, product recommendations, and other workloads. The commercial managed version launched on AWS in 2023 alongside its open-source offering and is distributed through AWS Marketplace, giving cloud adopters a preferred deployment option. By relying on fully managed AWS storage and compute infrastructure rather than operating everything itself, Qdrant can scale its database more easily and cut total resource costs by more than 30 percent while keeping performance and ease of use high.

Data flow

Enterprise unstructured data is indexed in Qdrant's managed vector database on AWS, queried through low-latency similarity search, and returned to AI and analytics applications that need conversational retrieval or other vector-search capabilities.

Solution architecture

0 products mentioned

Architecture clues

  • Commercial managed-cloud version launched on AWS in 2023 alongside the open-source database.
  • Supports use cases ranging from conversational search to anomaly detection and product recommendations.
  • Targets low-latency similarity search over large private datasets.

Evidence from the source

It helps enterprise solutions to connect private data and conversationally find information.
Since the beginning of 2023, Qdrant has also released a commercial version of the database as a managed cloud solution on AWS, and this version is available on AWS Marketplace.
The company created its vector search database to help enterprise companies with near-real-time data retrieval and to provide the tools to write generative AI solutions.
The company has saved more than 30 percent in total costs by hosting its solution on AWS infrastructure.
With its managed vector search database solution hosted on Amazon Web Services (AWS), Qdrant ... aims to help companies quickly and simply find the information they need.
Transparency Evidence & methodology 80/100 reference value · 86% 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 value80/100
  • 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 specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence86%
  • 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
    5 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 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

    Qdrant customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/qdrant-case-study/
    Source type
    Vendor-published customer story
    Published
    Jan 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. Prospeo

    Qdrant Revenue, Funding & Valuation

    Independent secondary
    https://prospeo.io/c/qdrant-revenue

    The page's Organization JSON-LD lists numberOfEmployees value 101.

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

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