MongoDB Software / SaaS global

Okta

IT Operations Automation · Security and Compliance

Okta Inbox needed a more scalable and lower-overhead way to power natural-language application access requests without maintaining separate vector infrastructure or slowing down product iteration.

With more than 18,000 customers globally, Okta has developed integrations for more than 7,000 applications.
“It receives thousands of requests through our chatbots, and we no longer need to manually process thousands of text embeddings to resolve those queries.”
“We’re also on track to reduce operating costs by 30% compared to the self-hosted solution.”

Reported impact

Business outcome

Okta simplified its identity-governance search stack, improved developer focus on product innovation, and is on track to reduce operating costs by 30% while preserving a strong customer experience.

  • Cost Reduction
  • Customer Experience
  • Speed to Market

Solution

To modernize Okta Inbox, Okta replaced a home-grown vector workflow that depended on custom machine learning models and a dedicated Kubernetes cluster with MongoDB Atlas and MongoDB Vector Search on Atlas. The product helps end users request access to applications in natural language, so the system has to understand phrases that describe the same tool in very different ways. Previously, embeddings and search logic were managed separately, which increased infrastructure cost and reduced room for experimentation. Okta moved embeddings into Atlas collections and used MongoDB Vector Search on Atlas to query them directly alongside application and ticketing data. This gave the engineering team a single operational data layer for structured records plus semantic retrieval, enabling automated app matching and ticket creation from chatbot requests. By removing extra infrastructure, the team could iterate faster while keeping the platform resilient as adoption grew.

Data flow

User requests arrive through chatbots, embeddings and application metadata are stored together in MongoDB Atlas, MongoDB Vector Search on Atlas retrieves the best matching app from those embeddings, and Okta Inbox automatically raises the access ticket for downstream processing.

Solution architecture

2 components · 2 layers
  1. Storage
    • MongoDB Atlas Stores Okta Inbox application data and embeddings in one resilient operational data layer.
  2. Serving
    • MongoDB Vector Search on Atlas Matches natural-language access requests to the correct applications and supports automated ticket routing.

Architecture clues

  • Embeddings are now stored directly in MongoDB Atlas collections.
  • Okta Inbox automatically raises tickets after matching the right application.
  • Okta previously managed embeddings on a dedicated Kubernetes cluster.
  • The platform uses natural-language vector search to identify requested apps.

Evidence from the source

“It receives thousands of requests through our chatbots, and we no longer need to manually process thousands of text embeddings to resolve those queries.”
“MongoDB Atlas provided one efficient data layer that simplified these operations.”
“MongoDB Vector Search on Atlas was the answer to our problems. It simplifies a lot of the work that goes into making Okta Inbox super user-friendly for customers.”
“We’re also on track to reduce operating costs by 30% compared to the self-hosted solution.”
Transparency Evidence & methodology 90/100 reference value · 89% 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 value90/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    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 confidence89%
  • 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
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference 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. MongoDB

    Okta customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/okta
    Source type
    Vendor-published customer story
    Published
    Mar 17, 2026
    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

  • Published on source page Mar 17, 2026 · MongoDB · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.