Google Cloud Software / SaaS Australia

Atlassian

IT Operations Automation

Atlassian is scaling its multi-model artificial intelligence capabilities to millions of users by using Google Cloud and Claude to orchestrate powerful agentic workflows.

Atlassian is scaling its multi-model artificial intelligence capabilities to millions of users by using Google Cloud and Claude to orchestrate powerful agentic workflows.
Five million executed agents and a future built on context
Improved latency across workflows
Scaled to 5M+ custom AI agents in one month

Reported impact

Business outcome

is a frontier AI company whose mission is to steer the trajectory of AI to advance human progress. We are best known for building Claude, the intelligence platform trusted by millions of people and businesses worldwide.

  • Cost Reduction
  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Atlassian is scaling its multi-model artificial intelligence capabilities to millions of users by using Google Cloud and Claude to orchestrate powerful agentic workflows. Over the last two years, the rapid adoption of AI has presented a unique mix of customer demands and internal transformations. In its early days of using generative AI, Atlassian relied on a single-vendor model architecture. However, true to its 20-year history as a provider of open solutions built on integration, Atlassian quickly realized that a one-size-fits-all approach wouldn't address the needs of its diverse customers. To address the challenges, Atlassian built an internal AI model gateway on Gemini Enterprise as the primary routing path, providing the infrastructure to seamlessly manage and interconnect multiple AI workloads. Atlassian scales its AI framework using Google Kubernetes Engine (GKE) alongside Google Cloud's high-performance GPUs and TPUs.

Solution architecture

6 components · 3 layers
  1. Compute
    • Gemini Flash Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
    • Google Kubernetes Engine (GKE) Runs containerized or virtualized application workloads with the elasticity needed for production scale.
    • Google Cloud GPUs Provides the cloud foundation that hosts the customer's data, application, and AI workloads.
  2. Serving
    • Google Workspace Provides the collaboration surface where teams review outputs and work with AI assistance.
  3. Orchestration
    • Gemini Enterprise Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
    • Agent Platform Coordinates multi-agent workflows, tool calling, and governed automation across the application.

Architecture clues

  • Atlassian is scaling its multi-model artificial intelligence capabilities to millions of users by using Google Cloud and Claude to orchestrate powerful agentic workflows.
  • Atlassian's Agent Platform is powered by multiple foundation models using Google Cloud AI infrastructure, assigning specific models to specific tasks.
  • To meet customers where they are, Atlassian needed to build a flexible, model-agnostic platform that could scale efficiently while adhering to specific compliance, governance, and regulatory requirements.

Evidence from the source

Atlassian's Agent Platform is powered by multiple foundation models using Google Cloud AI infrastructure, assigning specific models to specific tasks. Claude is used in complex, long-running developer or agentic use case
Defaulted custom agent creation to Gemini Flash
For example, migrating the custom agent platform to run natively on Gemini Flash models helped Atlassian achieve an exceptional balance of performance and affordability. The company has successfully scaled its ecosystem
Gemini Flash models serve as the out-of-the-box default model for Atlassian's custom agent platform within Rovo Studio. Gemini Flash provides the broad reach and speed required to handle unlimited general-purpose agent w
To address the challenges, Atlassian built an internal AI model gateway on Gemini Enterprise as the primary routing path, providing the infrastructure to seamlessly manage and interconnect multiple AI workloads. Atlassia
Transparency Evidence & methodology 61/100 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 value61/100
  • 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 specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    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
    5 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. Google Cloud

    Atlassian customer story

    First-party
    https://cloud.google.com/customers/atlassian-anthropic
    Source type
    Vendor-published customer story
    Published
    Sep 25, 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

  • Publisher catalog date Sep 25, 2026 · Google Cloud · 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.