Connectors / Integration
Connect On-Premise Jira and Jira Cloud — Sync Projects, Issues, and Teams Automatically
Automate bidirectional data flow between your Jira Server or Data Center instance and Jira Cloud so every team stays current without manual effort.
Jira + Jira Cloud integration
Many organizations run both a self-hosted Jira instance (Server or Data Center) and Jira Cloud at the same time — whether during a phased migration, to support separate business units, or to meet compliance requirements. Keeping issues, projects, sprints, and status updates synchronized across both environments manually is error-prone and slow. Tray.ai makes it straightforward to build reliable, automated pipelines that bridge your Jira and Jira Cloud deployments so nothing gets missed.
Development teams, product owners, and operations staff often work across both platforms, creating silos that slow delivery and obscure visibility. When a bug is logged in one instance but the fix is tracked in another, engineers waste time hunting for context and managers lose confidence in their reporting. Automating the sync of issues, comments, attachments, and workflow transitions between Jira and Jira Cloud lets organizations handle coexistence and migration without disrupting day-to-day work. Tray.ai gives you granular control over which projects, issue types, and fields flow between systems — so you can enforce governance while keeping every stakeholder in the tool they know best.
Automate & integrate Jira + Jira Cloud
Automating Jira and Jira Cloud business processes or integrating data is made easy with Tray.ai.
Use case
Bidirectional Issue Sync During Cloud Migration
During a phased Atlassian Cloud migration, teams in Jira Server and Jira Cloud need to collaborate on the same backlog without losing context. Tray.ai keeps issues mirrored across both instances in real time, picking up status changes, comments, and assignee updates wherever they originate. You can migrate at your own pace while maintaining a single source of truth across both platforms.
- Eliminate duplicate issue creation and manual copy-paste between instances
- Preserve full comment and attachment history during migration windows
- Let mixed teams work in their preferred environment without coordination overhead
Use case
Cross-Instance Sprint and Release Tracking
When product and engineering teams are split across Jira Server and Jira Cloud, sprint planning and release readiness reporting get fragmented fast. Tray.ai pulls sprint data from both instances into unified dashboards or downstream reporting tools, giving leadership a complete picture of delivery progress. Sprint completions in either environment can automatically trigger notifications or update shared roadmap tools.
- Unified sprint velocity metrics across both Jira environments
- Automated release readiness reports that pull from both instances
- Less time spent reconciling duplicated sprint boards manually
Use case
Priority Escalation Workflows Across Instances
When a critical issue is escalated in one Jira instance, the corresponding ticket in the other should immediately reflect the updated priority and trigger the right notifications. Tray.ai detects priority changes or severity labels in either system and propagates them right away, so SLA timelines are respected across both environments. On-call teams and project leads always see the latest priority regardless of which platform they use.
- Instant priority propagation prevents SLA breaches caused by stale data
- Automated stakeholder notifications triggered from either instance
- Consistent escalation audit trails maintained across both platforms
Use case
Automated Parent-Child Issue Linking Across Instances
Enterprise delivery often involves epics managed in Jira Cloud while sub-tasks or implementation stories live in an on-premise Jira instance. Tray.ai creates and maintains cross-instance links so progress on child issues automatically rolls up to the parent epic, keeping roadmap views accurate. When a sub-task closes in Jira Server, the update appears on the Cloud epic immediately.
- Real-time rollup of child issue status to parent epics in the other instance
- No manual linking or status updates required by engineers
- Accurate epic completion percentages for executive reporting
Use case
Compliance and Audit Log Replication
Organizations in regulated industries often run Jira Server on-premise to satisfy data residency requirements while enabling Cloud access for distributed teams. Tray.ai replicates issue audit events, field change histories, and approval transitions from Jira Cloud back to the on-premise instance to maintain a complete compliance record. Audit logs are timestamped and archived automatically — no manual export steps needed.
- Automated audit log replication meets data residency and compliance mandates
- Eliminate manual CSV exports and audit reconciliation processes
- Full field-level change history preserved in the on-premise system of record
Use case
Unified Customer-Facing and Internal Issue Tracking
Support and customer success teams often use Jira Cloud for customer-facing ticket management while engineering teams use Jira Server for internal development tracking. Tray.ai links customer tickets in Cloud to engineering issues in Server, automatically syncing resolution status and internal notes back to the customer-facing ticket when work is completed. Engineers don't have to context-switch between tools to close the loop.
- Customers receive timely updates without engineering teams changing their workflow
- Resolution status flows automatically from Jira Server to Jira Cloud tickets
- Fewer missed handoffs and duplicate tickets between support and engineering
Challenges Tray.ai solves
Common obstacles when integrating Jira and Jira Cloud — and how Tray.ai handles them.
Challenge
Field Schema Mismatch Between Jira Server and Jira Cloud
Jira Server and Jira Cloud often have divergent custom field configurations, field IDs, and option values — especially after years of independent configuration. Mapping fields between instances without accounting for these differences results in failed syncs, missing data, or incorrectly populated fields that erode trust in the integration.
How Tray.ai helps
Tray.ai's visual data mapper lets you define field-level transformations between the two instances, including value translation tables for dropdowns and status names. You can maintain separate mapping configurations per project pair and update them without redeploying the entire workflow, so schema drift stays manageable over time.
Challenge
Preventing Infinite Sync Loops
In a bidirectional sync, an update in Jira Server triggers an update in Jira Cloud, which triggers a webhook back to Jira Server — an infinite loop that floods both systems with spurious updates and burns through API rate limits fast.
How Tray.ai helps
Tray.ai workflows support conditional logic and stateful flags that detect whether an update originated from the sync automation itself. By storing a sync-source identifier on each issue and checking it at the start of every workflow run, tray.ai breaks the loop before any duplicate update is written, keeping both instances stable and API usage low.
Challenge
API Authentication Management Across Two Instances
Jira Server uses different authentication mechanisms (basic auth, PAT, or OAuth 1.0) than Jira Cloud (OAuth 2.0, API tokens). Managing credentials, token rotation, and permission scopes across two separate authentication models adds operational complexity and creates potential security gaps.
How Tray.ai helps
Tray.ai stores credentials for both Jira and Jira Cloud in a secure, centralized credential vault with role-based access controls. Authentication is configured once per connector and reused across all workflows. Tray.ai's connector framework handles the protocol differences between Server and Cloud authentication behind the scenes — you never write auth logic in your workflows.
Templates
Pre-built workflows for Jira and Jira Cloud you can deploy in minutes.
Real-Time Bidirectional Issue Sync: Jira ↔ Jira Cloud
Jira 
Jira Cloud This template watches for new and updated issues in both Jira Server/Data Center and Jira Cloud, then creates or updates the corresponding mirror issue in the opposite instance. Field mappings, status transitions, comments, and assignees are synchronized automatically, with loop-prevention logic to avoid infinite update cycles.
New Jira Cloud Issue → Create Linked Issue in Jira Server
Jira Cloud Jira When a new issue is created in Jira Cloud (for example, by a customer-facing team), this template automatically creates a linked tracking issue in the designated Jira Server project and stores the bidirectional link in both records. Field defaults and custom field mappings are configurable per project.
Priority Escalation Propagation Between Jira Instances
Jira Jira Cloud Detects when an issue priority or severity changes to a critical level in either Jira instance and immediately updates the corresponding mirror issue in the other instance, then sends a Slack or email notification to the assigned team and on-call engineer.
Scheduled KPI Aggregation from Jira and Jira Cloud to Data Warehouse
Jira Jira Cloud On a configurable schedule, this template queries open and closed issue metrics from both Jira Server and Jira Cloud, normalizes the results into a common schema, and loads them into a data warehouse or BI tool such as BigQuery, Snowflake, or Google Sheets for unified engineering reporting.
Jira Server Issue Closure → Resolve Linked Jira Cloud Ticket
Jira Jira Cloud When an engineering issue in Jira Server is marked Done or Resolved, this template automatically transitions the linked customer-facing or tracking ticket in Jira Cloud to the appropriate resolved state and appends a resolution comment, closing the loop for support and product teams.
Cross-Instance Sprint Completion Report
Jira Jira Cloud At the end of each sprint in either Jira instance, this template collects sprint metrics — completed stories, carry-over issues, and velocity — from both Jira and Jira Cloud and compiles a unified sprint report delivered to a Confluence page, Slack channel, or email distribution list.
How Tray.ai makes this work
Jira + Jira Cloud runs on the full Tray.ai platform
Intelligent iPaaS
Integrate and automate across 700+ connectors with visual workflows, error handling, and observability.
Learn more →Agent Builder
Build AI agents that read, write, and take action in Jira and Jira Cloud — with guardrails, audit, and human-in-the-loop.
Learn more →Agent Gateway for MCP
Expose Jira + Jira Cloud actions as governed MCP tools — observable, rate-limited, authenticated.
Learn more →Ship your Jira + Jira Cloud integration.
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