Project Management migration

Migrate from Taiga to Jira

Field-level mapping, validation, and rollback between Taiga and Jira. We move data and schema; workflows are rebuilt natively in Jira.

Taiga logo

Taiga

Source

Jira

Destination

Jira logo

Compatibility

80%

8 of 10

objects map 1:1 between Taiga and Jira.

Complexity

BStandard

Timeline

2-3 weeks

Rollback included Accuracy guarantee Field-level validation

Overview

What this migration involves

Taiga and Jira share a similar Scrum and Kanban vocabulary, but their underlying data models differ enough to require careful mapping. Taiga uses a flat hierarchy with User Stories, Tasks, and standalone Issues under a Project; Jira nests Stories under Epics, splits Tasks into Subtasks or linked Issues, and organizes work into Sprints within a Project. No native Taiga-to-Jira import path exists — Taiga exports project-level JSON that Jira's CSV and JSON importers do not accept, and the official Atlassian documentation lists only Trello, Asana, GitHub, and CSV as valid Jira import sources. We work around this by parsing Taiga's export format directly, building a field-level transformer that maps status, priority, severity, points, and custom attributes into Jira's issue schema, then loading through Jira's REST API or bulk import endpoint. We handle Taiga's hard 30-record page limit on the extraction side by looping until the API returns an empty page. Automations, custom workflows, and Taiga notifications do not migrate as code; we deliver a written inventory of every rule requiring rebuild in Jira.

Field-level fidelity

Every standard and custom field arrives verified.

Schema-aware mapping

AI proposes the map; you confirm before any record moves.

Relationships preserved

Parent–child, lookups, and ownership stay linked.

Full activity history

Calls, emails, meetings — with original timestamps.

Attachments & notes

Documents, uploads, and inline notes move with the record.

Why teams make this switch

Two sides of the same decision

Leaving

Taiga logo

Taiga

What's pushing teams away

  • The lack of a mature marketplace or plugin ecosystem means teams needing time tracking, resource management, or advanced reporting often outgrow Taiga and migrate to Jira or Linear.
  • Performance degrades noticeably on self-hosted instances with large projects, and the cloud-hosted option lacks enterprise-grade SLA guarantees and dedicated support tiers.
  • The API documentation is sparse and the record pagination limit of 30 per request makes automated migrations and integrations brittle without custom workaround code.
  • Teams needing native integration with CI/CD pipelines, feature flags, or customer success tooling find Taiga's ecosystem insufficient compared to platforms like Shortcut or Linear.
  • The roadmap cadence and community contribution model mean that bug fixes and feature requests move slowly, frustrating teams used to faster release cycles.

Choosing

Jira logo

Jira

What's pulling them in

  • Industry-standard tool with deep Git integration and sprint reporting that engineering teams already know, reducing onboarding friction for new hires.
  • Highly customizable workflows and status schemes let business teams model complex approval chains without writing code.
  • Strong ecosystem of Atlassian Marketplace apps means specialized capabilities like time tracking or portfolio management are one install away.
  • Free tier with up to 10 users and unlimited issues gives small teams a no-cost entry point to validate the platform before committing budget.
  • Visibility features — boards, backlog grooming, sprint reports, and dashboards — give leadership a shared view of what is planned, in progress, blocked, and done.

Object mapping

How Taiga objects map to Jira

Each row shows how a Taiga object lands in Jira, including any object-level transformations, lookup resolution, or schema-design dependencies.

Typical mapping — final map is confirmed during the sample migration step.

Taiga

Project

maps to

Jira

Project

1:1
Fully supported

Taiga Projects map directly to Jira Projects. We preserve the project name, description, and member list. Each Taiga project requires a Jira project key (e.g., PROJ) that we define during scoping; the key becomes the prefix for all Jira issue IDs migrated from that project. Taiga project settings including wiki, module configuration (Kanban vs Scrum), and custom attributes export with the project and require field-level mapping decisions before Jira import.

Taiga

Milestone / Sprint

maps to

Jira

Sprint

1:1
Fully supported

Taiga Milestones map to Jira Sprints. We preserve the sprint name, start date, end date, and sprint order within the project. Closed Sprints migrate with their Completed status intact. Open Sprints that have not yet closed are created as future sprints in Jira with the original start/end dates. If a Taiga project uses multiple active sprints simultaneously (Kanban with swimlanes treated as sprint-equivalents), we map these to Jira sprints within a shared board.

Taiga

Epic

maps to

Jira

Epic

1:1
Fully supported

Taiga Epics map to Jira Epics (available in Jira Software from the free plan onward). Epic status, color tag, and description preserve. In Taiga, Epics are top-level backlog items that group User Stories; in Jira, Epics function identically with an Epic Link custom field on Stories. We set the Epic Name field on Jira Epic issues and map the Epic Link field on child Stories during migration. Where a Taiga Epic has no child Stories, we create a standalone Jira Epic and flag it for review.

Taiga

User Story

maps to

Jira

Story

1:1
Fully supported

Taiga User Stories map to Jira Story issues. The story subject becomes the Jira Summary (required). Story description, status, points (story points to a numeric custom field), assignee, milestone reference, tags, and custom attributes migrate. Taiga's us epic link field maps to Jira's Epic Link field. We resolve the target Epic ID during migration so the Epic Link is populated at import time rather than requiring post-migration manual linking.

Taiga

Task

maps to

Jira

Subtask or Story

1:many
Fully supported

Taiga Tasks nested under a User Story map to Jira Subtasks. Subtasks inherit the parent Story's project, Epic Link (if set), and sprint assignment. Taiga Tasks that are not children of a User Story (top-level tasks) map to Jira Story issues — we treat them as Stories in Jira because Jira does not have standalone top-level Tasks without a parent. During scoping, the customer chooses whether all Tasks become Subtasks or whether free-standing Tasks become Stories.

Taiga

Issue

maps to

Jira

Bug, Task, or Story

lossy
Fully supported

Taiga Issues are standalone work items outside the sprint backlog, with type, severity, priority, and status. We map Taiga issue type to Jira Issue Type (Bug, Task, Story) based on the customer's naming convention. Taiga severity and priority map to Jira Priority (Highest, High, Medium, Low) and to a custom Jira severity field if the customer's workflow requires both. Closed issues from Taiga migrate with their final status and resolution preserved in Jira's Resolution field.

Taiga

Custom Attributes

maps to

Jira

Custom Fields

1:1
Mapping required

Taiga allows custom attributes on User Stories, Tasks, and Issues per project. We extract the attribute definition (name, type, and options) and create matching Jira custom fields before migration. Text attributes map to Jira Text Field (single line), long text to Text Area (multi-line), number to Number field, date to Date field, and dropdown or multi-select options to Jira Select (single) or Multi-select fields. Enum values are preserved as picklist options in Jira.

Taiga

Wiki Pages

maps to

Jira

Confluence Pages or Jira Description

1:1
Mapping required

Taiga project wikis contain Markdown-formatted pages organized in a tree. We export the page tree and convert Markdown to Confluence Storage Format (XHTML) if the customer has Confluence. If Confluence is not in scope, page content migrates as Jira issue description with Markdown preserved. Links between wiki pages require manual re-linking post-migration because Taiga's internal wiki links do not have Jira equivalents. The page tree structure is documented as a navigation map for the admin to reproduce.

Taiga

Tags / Labels

maps to

Jira

Labels

1:1
Mapping required

Taiga's free-form tags on User Stories, Tasks, and Issues extract as label strings and apply as Jira Labels. Taiga supports multiple tags per artifact; Jira Labels is a multi-value text field that accepts the equivalent. Where Taiga uses structured tags as a pseudo-category system (e.g., frontend, backend, ux), we recommend the customer review the label vocabulary post-migration to consolidate duplicates and enforce a controlled vocabulary in Jira.

Taiga

Attachments

maps to

Jira

Attachments

1:1
Mapping required

Taiga stores attachments as file reference URLs pointing to the Taiga media storage. We retrieve attachments from Taiga's media URL if the instance is cloud-hosted and accessible, then upload them to Jira via the Jira REST API attachment endpoint (10MB per-file limit on Jira Cloud). Taiga's attachment metadata (filename, uploader, upload date, comment) becomes the Jira attachment comment. Self-hosted Taiga instances with local file storage require the customer to provide media file access during migration.

Gotchas + challenges

What specifically takes care here

Platform-specific issues from each side, plus the pair-specific challenges that don't show up on either platform's page on its own.

Taiga logo

Taiga gotchas

High

REST API hard-codes 30 records per page

High

Import only accepts Trello, Jira, Asana, and GitHub

Medium

Docker self-hosted v5 to v6 migration can lose data silently

Medium

Taiga export is instance-specific JSON, not portable CSV

Low

Custom CSS / taiga-ui v3 to v4 style overrides break after migration

Jira logo

Jira gotchas

High

Unsupported workflow validators silently skipped during migration

High

Custom fields converted to flat text labels when migrating to non-Jira platforms

Medium

Historical status-change timestamps lost when exporting without a Marketplace plugin

Medium

Attachment import failures from oversized files and JQL reference corruption

Medium

Points-based API rate limits enforced on Jira Cloud apps from March 2026

Pair-specific challenges

  • No native Taiga-to-Jira import path exists

    Taiga's project export produces a JSON format specific to its own import pipeline, which only accepts Trello, Asana, GitHub, and Jira JSON. Jira's CSV and JSON importers explicitly do not accept Taiga's format. Stack Overflow discussions from teams attempting Taiga-to-Jira migration confirm that the JSON export is difficult to reformat into Jira's required schema. We work around this by parsing Taiga's export JSON directly, building a field-level transformer that maps Taiga's artifact fields (status, priority, severity, points, custom attributes) into Jira issue field names, and loading via Jira's REST API or bulk CSV import. This is a custom code path, not a platform-native import, which is why no off-the-shelf tool handles this migration.

  • Taiga REST API hard-caps at 30 records per page

    Taiga's REST API returns a maximum of 30 records per request regardless of pagination parameters. A Taiga community post and developer forum discussions confirm this hard limit. We handle this by implementing cursor-based looping over each object type (Projects, Epics, User Stories, Tasks, Issues) and accumulating results until the API returns an empty page. Without this loop, bulk exports silently drop all records beyond page 30, which is a data-loss risk for projects with hundreds of artifacts. We validate record counts against the Taiga JSON export before beginning Jira load.

  • Jira workflows and automations do not migrate from Taiga

    Neither Taiga nor Jira exposes automation rules in a portable format that can transfer between platforms. Taiga has basic project-level notification rules and custom status workflows; Jira has Jira Automation for Jira with triggers, conditions, branching logic, and JQL conditions. We do not migrate these as code. We audit every Taiga project notification rule and Jira automation currently in use, document them in a written inventory with recommended Jira equivalents, and hand off the rebuild to the customer's admin team. Custom Jira workflows (status transitions, post-functions, validators) similarly require manual rebuild.

  • Jira project key requirements constrain bulk import

    Jira generates issue keys using the project key prefix plus an auto-incremented number (e.g., PROJ-1, PROJ-2). The project key cannot be changed after creation and cannot be the same as an existing project key in the Jira instance. When migrating multiple Taiga projects into a single Jira project, we consolidate by mapping each Taiga project to a separate Jira project with a unique key. If the customer wants to merge multiple Taiga projects into one Jira project, we map all artifact IDs to the same Jira project key and drop the original Taiga identifiers in favor of Jira's auto-generated keys.

  • Taiga Docker v5 to v6 self-hosted upgrades can silently destroy data

    Community posts document that upgrading a self-hosted Taiga Docker instance from v5 to v6 on a new server can result in zero visible data with no errors in container logs. The official upgrade documentation covers the migration steps but does not include data verification checkpoints. If the source Taiga instance is a self-hosted Docker deployment, we require a verified JSON export and record count validation before any extraction begins. We recommend the customer confirms data completeness against the JSON export before granting read access for API extraction.

Migration approach

Six steps for a successful Taiga to Jira data migration

  1. Discovery and data audit

    We audit every Taiga project in scope: record counts per object type (Epics, User Stories, Tasks, Issues, Milestones), custom attribute definitions, attachment volume and total storage size, wiki page count, and project settings (Scrum vs Kanban per module). We identify the target Jira Cloud or Data Center instance and verify project key availability. We document the full Taiga role-permission matrix for mapping to Jira groups and project roles. The discovery output is a written migration scope with record counts, a Jira schema design, and a mapping table for every custom field.

  2. Extraction with pagination loop

    We extract from Taiga via paginated REST API calls, looping through 30-record pages for each object type until the API returns an empty page. We accumulate results in a staging store and validate total record counts against the Taiga JSON export if available. For self-hosted Taiga Docker instances, we require a JSON export as a fallback backup in case the API extraction encounters authentication or network issues. We extract wiki pages and attachment URLs in parallel with artifact extraction.

  3. Schema creation in Jira

    We create the destination Jira project(s), define project keys, and provision custom fields to match Taiga's custom attribute schema. We configure the Jira workflow, status values, and issue type scheme to approximate Taiga's workflow before any data loads. If the customer uses multiple Taiga projects, we create one Jira project per Taiga project or consolidate per the scoping decision. The Jira schema is deployed to a staging or sandbox environment first for validation before production load.

  4. Data transformation and sandbox import

    We transform Taiga's JSON export into Jira-compatible CSV (for bulk import) or JSON (for REST API load) format. This includes mapping Taiga status to Jira status, Taiga severity to Jira Priority or a custom field, Taiga story points to a Jira numeric custom field, and Taiga tags to Jira Labels. Custom attributes transform according to the type mapping defined during schema creation. We run a sandbox import into the customer's Jira staging environment, reconcile record counts, spot-check 25-50 records per object type against the Taiga source, and document any mapping corrections before production.

  5. Attachment migration

    We download files from Taiga's media storage URL for each attachment, verify the file is accessible, and upload to Jira via the Jira REST API attachment endpoint. Files over 10MB on Jira Cloud are flagged for the customer to decide whether to split or exclude. Attachment comments (uploader, timestamp, description) are preserved as Jira attachment comments. Self-hosted Taiga instances with local storage require the customer to provide direct file system access or a media URL endpoint.

  6. Production migration and cutover

    We freeze write access to the source Taiga project(s) during the production cutover window. We run a delta extraction for any records modified during testing, apply the final transformation, and load into the production Jira instance. We validate record counts per object type post-load against the extraction counts. We deliver the automation and workflow inventory document to the customer's admin team. We support a 72-hour hypercare window for reconciliation issues. Wiki page link updates in Confluence (if applicable) require a separate pass after Confluence is provisioned.

Platform deep dives

Context on both ends of the pair

Taiga logo

Taiga

Source

Strengths

  • Free and open-source under AGPL-3.0 with no per-seat licensing constraints on self-hosted deployments.
  • Native dual-mode support for both Scrum and Kanban in a single project without requiring a plugin.
  • Clean, minimal UI that is faster to onboard non-technical stakeholders compared to Jira.
  • Active community forum and documentation covering self-hosted Docker deployment and upgrades.
  • Built-in import pipeline for Trello, Jira, Asana, and GitHub as source platforms.

Weaknesses

  • No native bulk export API — all data retrieval goes through paginated REST calls with a low default page size.
  • Sparse third-party integrations and no Zapier/Make connector, limiting automated workflow options.
  • Custom attribute system varies per project, requiring field-level mapping work in any migration to a structured target.
  • No native time-tracking module — teams needing billable hours must use third-party tools or the wiki as a workaround.
  • Support on the free cloud tier is community-only, which can delay resolution of data-loss incidents during migration.
Jira logo

Jira

Destination

Strengths

  • Deeply customizable workflows and status schemes with no hard limits on workflow complexity or number of custom statuses.
  • Strong agile ceremony support: sprint planning, backlog grooming, velocity tracking, and burndown charts for Scrum teams.
  • Industry-standard developer tool with native Git integration linking commits, pull requests, and deployments to issues.
  • Large Atlassian Marketplace with thousands of plugins extending time tracking, portfolio management, and reporting capabilities.
  • Free tier available for up to 10 users with unlimited issues, enabling evaluation before committing to a paid plan.

Weaknesses

  • Excessive configurability creates a steep learning curve; cross-team consistency is hard to maintain without strict governance.
  • Performance degrades with large backlogs, complex custom fields, and heavily nested issue hierarchies.
  • Reporting requires additional configuration or paid plugins; out-of-the-box analytics are limited for business users.
  • Jira lacks native sprint management, requiring Jira Software for true agile team features.
  • Teams outside engineering resist adoption due to UI complexity, leaving the all-in-one promise unfulfilled for cross-functional organizations.

Complexity grading

How hard is this migration?

Standard Project Management migration. 3 of 8 objects need a mapping; the rest are 1:1.

B

Overall complexity

Standard migration

Derived from compatibility, mapping clarity, API constraints, and data volume across Taiga and Jira.

  • Object compatibility

    B

    3 of 8 objects need a mapping; the rest are 1:1.

  • Field mapping clarity

    C

    Field mapping is derived from defaults — final spec confirmed during the sample migration.

  • Timeline complexity

    B

    8-object category — typical timelines run 2–7 days end-to-end.

  • API constraints

    B

    Taiga: Not publicly documented in official docs.

  • Data volume sensitivity

    B

    Taiga doesn't expose a bulk API — REST + parallelization used for high-volume runs.

Estimator

Estimate your Taiga to Jira migration cost

Rule-based pricing — no per-record fees, no manual quotes. Migrations over 2M records are scoped individually.

Step 1

What are you migrating?

Pick a category, then your source and destination platforms.

Category

FAQ

Frequently asked questions about Taiga to Jira data migrations

Answers to the questions buyers ask most during Taiga to Jira migration scoping. Not seeing yours? Book a call.

Can't find your answer?

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Small migrations under 2,000 total artifacts (User Stories, Tasks, Issues) with no custom attributes complete in two to three weeks. Medium migrations with two to five Taiga projects, custom attribute schemas, and attachment libraries move to three to four weeks. Large migrations with six or more projects, extensive wiki pages, and cross-project sprint recalculation require four to six weeks. The timeline includes extraction with pagination loop, schema design, sandbox import and validation, production load, and 72-hour hypercare.

Adjacent paths

Related migrations to explore

Ready when you are

Move from Taiga.
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