Databricks Software / SaaS United States

OpenAI

Data Lake / Data Warehouse Modernization · Real-time Analytics · Risk Management

OpenAI’s data platform powers one of the fastest-scaling products in tech history. ChatGPT, the OpenAI API, and the internal tools used by thousands of OpenAI employees require a foundation that can withstand sudden growth, run governed analytics across multiple business units, and operate across multiple clouds simultaneously.

$400K/month Storage costs eliminated by marketing team
Since 2024, OpenAI has leveraged Databricks in Azure and AWS, with usage data landing in Delta tables governed by Unity Catalog .

Reported impact

Business outcome

OpenAI achieved measurable business impact on Databricks, including $400K/month Storage costs eliminated by marketing team; Since 2024, OpenAI has leveraged Databricks in Azure and AWS, with usage data landing in Delta tables governed by Unity Catalog ..

  • Cost Reduction
  • Productivity Improvement
  • Risk Reduction

Solution

OpenAI’s data platform powers one of the fastest-scaling products in tech history. ChatGPT, the OpenAI API, and the internal tools used by thousands of OpenAI employees require a foundation that can withstand sudden growth, run governed analytics across multiple business units, and operate across multiple clouds simultaneously. That foundation has grown up around Databricks. Since 2024, OpenAI has leveraged Databricks in Azure and AWS, with usage data landing in Delta tables governed by Unity Catalog . Databricks Jobs and Data Warehouses transform that data through layered pipelines to serve product analytics, finance, security, and trust, and safety teams.

Data flow

Operational, customer, and transactional data lands in Databricks and is transformed through managed pipelines stored in Delta Lake under Unity Catalog governance before being served through Databricks SQL, Genie

Solution architecture

6 components · 4 layers
  1. Storage
    • Delta Lake Stores OpenAI's curated datasets for analytics, machine learning, and operational use cases.
  2. Serving
    • Databricks SQL Serves dashboards, interactive analysis, and governed SQL workloads for business users at OpenAI.
    • Genie Lets nontechnical teams at OpenAI query governed data in natural language.
    • Databricks Data Warehouses Provides warehouse compute for cross-functional reporting and governed analytics.
  3. Orchestration
    • Databricks Jobs Runs scheduled transformation jobs that publish datasets for downstream teams.
  4. Governance
    • Unity Catalog Provides access control, governance, and lineage across OpenAI's shared data products and AI workloads.

Architecture clues

  • Cloud: AWS, Azure
  • Key product: Delta Lake
  • Key product: Unity Catalog
  • Medallion architecture
  • Multi-cloud deployment
  • Real-time or streaming data processing

Evidence from the source

$400K/month Storage costs eliminated by marketing team
Databricks also supports AI-assisted investigations: Codex agents can query governed security data through SQL APIs.
OpenAI’s Security team uses Databricks to detect cybersecurity threats across high-volume infrastructure, cloud, and vendor audit logs.
OpenAI’s data platform powers one of the fastest-scaling products in tech history.
Since 2024, OpenAI has leveraged Databricks in Azure and AWS, with usage data landing in Delta tables governed by Unity Catalog .
Transparency Evidence & methodology 95/100 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 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
    2 metrics 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
    5 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. Databricks

    OpenAI customer story

    First-party
    https://www.databricks.com/customers/openai
    Source type
    Vendor-published customer story
    Published
    Jul 28, 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 Jul 28, 2026 · Databricks · 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.