Migration Playbook

Zammad Zendesk

Zammad to Zendesk: The Complete Migration Playbook

A 41-step runbook across six phases — track your progress, and open the right tool at every step.

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TL;DR

Zammad to Zendesk migration requires API-to-API extraction using Zammad's REST API and Zendesk's Ticket Import API. No native migration path exists between the two platforms.

There is no native import path, built-in connector, or vendor-provided migration tool for moving data from Zammad to Zendesk. The fundamental data-model gap centers on Zammad's articles (typed, sender-attributed messages per ticket) versus Zendesk's simpler comments model, which means channel metadata and sender semantics must be translated and partially flattened during migration. A successful migration requires custom API-to-API extraction via Zammad's REST API (or direct PostgreSQL access for self-hosted instances) and loading through Zendesk's Ticket Import API, with a purpose-built transformation layer to handle field mapping, attachment re-upload, timestamp preservation, and rate-limit management.

Read this first

Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.

There is no official reverse of Zammad's inbound Zendesk migrator

Zammad documents Zendesk → Zammad migration, but for Zammad → Zendesk you must use API-based import workflows. (docs.zammad.org)

Zendesk strictly enforces the closed status

Once a ticket is set to closed via the API, it can never be updated again. Ensure all comments, tags, and attachments are correct in the payload before setting this status.

Zendesk supports custom objects, but they're plan-gated with limits

3, 5, 30, or 50 custom objects depending on plan, up to 100 fields per object, and up to 5 or 10 lookup relationship fields per object. (support.zendesk.com)

Pagination limit

Zammad enforces a hard maximum of 100 objects per page. You cannot raise this limit. Use page and per_page parameters and iterate until the response returns fewer items than per_page. Adding expand=true includes related object names inline, reducing follow-up lookups.

Zendesk Ticket Import API constraints

Triggers don't fire on imported tickets. SLA metrics (first reply time, resolution time) are not calculated for imported tickets. No side conversation support on imported tickets. Requesters must be active — suspended users will cause the import to fail with a 422 Unprocessable Entity. (support.zendesk.com)

If you're building a new migration utility in mid-2026 or later, prefer Zendesk OAuth for

If you're building a new migration utility in mid-2026 or later, prefer Zendesk OAuth for authentication. Zendesk has announced that API tokens will be permanently deactivated on April 30, 2027. (developer.zendesk.com)

The runbook

Work top to bottom. Tick steps as you go — your progress is saved in this browser.

01 Discovery Establish why you are moving, what "done" means, and who signs off. 0/5

Objective A written scope with agreed success criteria, a named owner per workstream, and a budget approved by finance.

  1. Pull the real numbers out of Zammad

    Support ops 1 day

    Export counts for tickets (open and closed separately), contacts, organisations, attachments, macros, triggers, automations, views and SLA policies. Note the oldest ticket date — history depth drives the whole timeline. Estimating from memory is the single most common cause of a blown migration window.

    Data Profiler Get real record counts instead of estimating from memory
  2. Decide what history actually moves

    Support lead 2 days

    Agree a cut-off with the support lead: all history, last 24 months, or open tickets plus a read-only archive. Every extra year of closed tickets adds API time and cost without adding much agent value. Get this in writing — it is the decision people relitigate mid-cutover.

    A "move everything" default is what turns a two-week migration into a two-month one.

    COI & ROI Calculator Build the 36-month business case you will need for sign-off
  3. Confirm Zendesk can hold your support model

    Solution architect 2-3 days

    Walk your current workflow through Zendesk: multi-brand, business hours, SLA targets, CSAT, side conversations, public vs internal notes, and any channel you depend on (voice, chat, WhatsApp, social). List anything with no native equivalent — those are project risks, not configuration details.

  4. Build the business case

    Project sponsor 1-2 days

    Model licence delta, migration effort, agent retraining, and the cost of staying put (Cost of Inaction). Executives approve a number, not a plan, and you will be asked for it again at the go/no-go.

    Helpdesk Migration Planner Turn ticket volume into a dated Zammad → Zendesk timeline
  5. Name owners and set the go/no-go date

    Project manager 1 day

    One named owner each for data, configuration, integrations, and agent enablement, plus a decision-maker who can call a rollback. Put the go/no-go meeting in calendars now, 48 hours before the freeze.

Zammad → Zendesk specifics

Ecosystem breadth
Zendesk's marketplace has 1,500+ integrations. Zammad's integration ecosystem is narrower, relying heavily on its REST API for custom connections. Teams that need native connectors to Salesforce, Shopify, or Jira hit limits with Zammad first.
Managed SaaS operations
Zammad can be self-hosted, which means your team owns infrastructure, upgrades, and database maintenance. Moving to Zendesk shifts that operational burden to the vendor.
AI and automation
Zendesk's investment in AI — Intelligent Triage, AI Agents, Answer Bot — is tightly coupled with its ticketing data model. Zammad's AI features are newer and less mature.
Reporting and compliance
Zendesk Explore provides built-in analytics dashboards, CSAT surveys, SLA enforcement, and agent capacity metrics out of the box. Zammad's reporting is more limited and often requires custom queries or external BI tools.
Team scaling
Organizations growing past 20–30 agents often find Zendesk's role-based permissions, multi-brand support, and enterprise security features (SSO, audit logs, data locality) more aligned with their needs.

Don't move on until

  • Record counts confirmed for tickets, contacts, organisations and macros
  • Success criteria signed off by the support lead
  • Freeze window provisionally booked with the business
02 Data Audit Find out what is actually in the data before you try to move it. 0/8

Objective A profiled, cleaned export with every quality defect either fixed at source or explicitly accepted.

  1. Take a full Zammad export and profile it

    Data engineer 1-2 days

    Export to CSV or JSON and profile every file: row counts, null rates per column, distinct values, and type consistency. Compare row counts against the API totals from Discovery — a gap here means your export is silently truncated, usually by pagination.

    Data Profiler Profile the Zammad export for nulls, outliers and type drift
  2. Validate file structure before anyone writes a transform

    Data engineer 1 day

    Check delimiters, quoting, encoding (expect UTF-8, watch for BOMs and Latin-1), duplicate headers, and embedded newlines in ticket bodies. Ticket descriptions with raw newlines and commas break naive CSV parsers and silently shift columns.

    A single unescaped quote in one ticket body can shift every subsequent column without any error.

    CSV Validator Catch broken headers and ragged rows in the raw export
  3. Inventory PII and set retention

    Compliance / DPO 2 days

    Scan for emails, phone numbers, payment card fragments, national IDs and anything else regulated in ticket bodies and custom fields — support tickets are where customers paste things they should not. Decide what gets migrated, masked, or dropped, and record the legal basis.

    Ticket bodies and attachments routinely contain card and ID data that never appears in a structured field.

    PII & Compliance Scanner Find regulated fields before they land in a new system
  4. Quantify duplicates, orphans and dead references

    Support ops 1-2 days

    Count duplicate contacts (same email, different casing), tickets whose requester no longer exists, organisations with no members, and attachments whose parent ticket is gone. Fix these in Zammad where you can — migrating them just moves the mess.

    Data Cleaner Strip empty rows, stray whitespace and dead columns
  5. Clean and normalise the export

    Data engineer 2 days

    Trim whitespace, drop empty rows and columns, normalise casing on emails and tags, and standardise every timestamp to UTC ISO 8601. Timezone drift is invisible at load time and shows up weeks later as SLA reports nobody can reconcile.

  6. Produce a masked copy for sandbox work

    Data engineer 0.5 day

    Generate a realistic but fake version of the export for testing and for any vendor who needs sample data. Loading real customer PII into a sandbox is a breach in most jurisdictions, and sandboxes are rarely covered by your DPA.

    PII Masker Generate a safe copy for sandbox and vendor testing
  7. Audit trails and time accounting

    Zendesk doesn't import these; archive separately

  8. Export your Zammad object definitions

    via GET /api/v1/object_manager_attributes to list all custom fields with their data types and options (docs.zammad.org)

Zammad → Zendesk specifics

Ticket Articles
GET /api/v1/ticket_articles/by_ticket/{ticket_id} — all articles for a ticket
Organizations
GET /api/v1/organizations?page={n}&per_page={n}
Attachments
GET /api/v1/ticket_attachment/{ticket_id}/{article_id}/{attachment_id}
Knowledge Base
GET /api/v1/knowledge_bases (categories and answers accessible via sub-endpoints)

Don't move on until

  • Export parses cleanly with no ragged rows or encoding errors
  • PII inventory complete and retention decisions recorded
  • Duplicate and orphan records quantified and triaged
03 Field Mapping Turn two schemas into one signed-off mapping spec. 0/8

Objective A reviewed field-level mapping covering every object, with an explicit decision for every field that has no target.

  1. Generate the first-pass Zammad → Zendesk field map

    Solution architect 2 days

    Start from an automated match on both schemas, then review every row by hand. Automated matching gets the obvious 70% right and is confidently wrong on the rest — especially anything named "type", "status" or "custom_field_1".

    Schema Mapper Opens pre-loaded with the Zammad → Zendesk field pair
  2. Map status, priority and channel values, not just field names

    Support lead 1-2 days

    Enumerate every value in each picklist on both sides and map them explicitly. Value-level mismatches are the defect class that survives all the way to production because the field itself mapped fine — a ticket that should be "Pending" arriving as "Open" reopens SLA clocks.

    Statuses with no target equivalent (on-hold, pending-customer) need a policy decision, not a best guess.

  3. Decide how custom fields land

    Solution architect 2 days

    Create the target custom fields first, matching type exactly (a dropdown mapped to free text can never be mapped back). Where Zendesk has no equivalent, decide between a new custom field, a tag, or a note appended to the ticket body — and record which.

  4. Resolve identity and threading

    Data engineer 1 day

    Decide how source IDs are preserved — most platforms will not let you set the primary key, so keep the original ID in a custom field. Without it, reconciliation becomes fuzzy matching and every future support question about an old ticket is unanswerable.

    Losing the original ticket ID makes reconciliation and rollback effectively impossible.

  5. Plan attachments, inline images and threading order

    Data engineer 1-2 days

    Confirm size limits, allowed MIME types, and whether inline images survive as attachments or need rehosting. Decide the comment ordering and author attribution rules: comments loaded out of order, or all attributed to the API user, destroy the conversation history agents rely on.

  6. Freeze and sign off the mapping spec

    Project manager 1 day

    Version the spec, walk the support lead through it row by row, and get explicit sign-off. Any change after this point goes through change control — mid-flight mapping edits are how partial loads happen.

  7. Create matching fields in Zendesk

    ticket fields, user fields, or organization fields. Note that Zendesk field types may not map 1:1 (e.g., Zammad's richtext has no direct equivalent in Zendesk custom fields; convert to textarea or text)

    Data Format Converter Reshape the export into the format Zendesk's importer expects
  8. Map picklist values

    if Zammad uses a select or tree_select field, ensure every option value exists in the Zendesk dropdown. Zammad's tree_select (hierarchical) must be flattened to a single-level dropdown in Zendesk or encoded with a delimiter (e.g., Parent::Child)

    JSON to CSV Converter Flatten nested API responses into a reviewable sheet

Zammad → Zendesk specifics

Small team, < 5K tickets, no custom fields
CSV for users + a third-party tool for tickets (if one supports Zammad). Otherwise, API-to-API.
Unused custom fields
delete or ignore before mapping to Zendesk

Don't move on until

  • Every source field is mapped, deliberately dropped, or parked in a custom field
  • Status, priority and channel value maps agreed with the support lead
  • Mapping spec version-controlled and signed off
04 Test Migration Prove the pipeline on a small, representative slice. 0/6

Objective A pilot load into a Zendesk sandbox that reconciles cleanly and has been reviewed by real agents.

  1. Stand up a Zendesk sandbox that matches production config

    Solution architect 2-3 days

    Create the custom fields, groups, brands, business hours and SLA policies first. A pilot into a default sandbox tests nothing, because the failures you care about are all configuration mismatches.

  2. Pick a deliberately nasty pilot sample

    Data engineer 0.5 day

    Take 500-1000 records chosen for difficulty, not convenience: the longest ticket threads, tickets with the most attachments, non-Latin character sets, merged and split tickets, deleted requesters, and every status value. A clean random sample proves only that easy records are easy.

  3. Run the load with masked data and instrument everything

    Data engineer 1-2 days

    Log every API request and response with its source record ID. When 40 records fail out of 10,000 you need to know exactly which ones and why, without re-running the whole batch.

    PII Masker Never load real customer PII into a sandbox
  4. Measure real throughput against the rate limit

    Data engineer 1 day

    Record achieved records-per-hour under Zendesk's actual rate limits, including retries and backoff. Extrapolate to the full volume: if the maths says the full load exceeds your freeze window, you fix that now, not on cutover night.

    Published rate limits are ceilings, not throughput. Assume real-world rates are meaningfully lower once retries and backoff are counted.

  5. Reconcile the pilot and triage every failure

    Data engineer 1-2 days

    Diff source against target on record counts and field-level values. Every discrepancy gets a root cause and a fix — "probably fine" at pilot scale becomes thousands of broken records at full scale.

    Migration Validation Tool Diff the pilot batch against source before scaling up
  6. Put real agents in front of the pilot data

    Support lead 2 days

    Have two or three agents work sample tickets end to end in the sandbox. They find the things reconciliation cannot see: unreadable threading, missing context, macros that no longer make sense. Fix the mapping, then re-run.

Zammad → Zendesk specifics

Spam and test tickets
filter by tag or status before extraction
Small sample test
Migrate 50–100 tickets to a Zendesk sandbox to validate field mapping, status translation, and attachment handling
Full volume test
Migrate all data to a Zendesk sandbox to validate timing, rate limit handling, and edge cases

Don't move on until

  • Pilot batch reconciles to 100% on record counts
  • Agents have reviewed sample tickets and confirmed they are workable
  • Measured throughput extrapolates to a viable full-load window
05 Cutover Execute the switch inside a controlled, reversible window. 0/7

Objective All in-scope data live in Zendesk, agents working in the new system, and a rollback path that stayed available throughout.

  1. Pre-load history before the freeze

    Data engineer 3-10 days

    Load closed tickets and contacts days or weeks ahead while Zammad stays live. Only open tickets and the final delta need to move inside the freeze — this is the single biggest lever on window length.

    Helpdesk Migration Planner Size the freeze window from Zendesk's real API limits
  2. Publish the runbook with times, owners and abort criteria

    Project manager 1 day

    A timed sequence: freeze start, final export, delta load, channel switch, smoke test, go/no-go, agent switch. Name who does each step and the explicit condition that triggers a rollback. Decide the abort criteria before the night, when nobody wants to be the one to call it.

  3. Freeze Zammad and take the final delta

    Support ops 2-4 hours

    Stop new ticket creation, let agents finish in-flight replies, then export everything changed since the pre-load. Announce the freeze to the whole business, not just support — someone always tries to raise a ticket during it.

    Tickets created during an unenforced freeze land in the old system and are the most common source of permanently lost data.

  4. Load the delta and open tickets

    Data engineer 2-6 hours

    Run the delta load, then reconcile counts before touching any channel. Do not repoint email until the delta has verified — an inbound ticket arriving mid-load is far harder to untangle than a few extra minutes of freeze.

    Migration Validation Tool Confirm the final delta landed before you reopen
  5. Repoint channels and verify with live traffic

    IT / integrations 2-4 hours

    Switch email forwarding and MX or connector settings, update chat widgets and web forms, and re-authorise integrations. Then send real test tickets through every channel and confirm each lands, routes and triggers the right automation.

    Email forwarding changes can take up to a full DNS TTL to propagate — check the TTL days in advance and lower it if needed.

    Cron Expression Builder Schedule the delta syncs that run through the freeze
  6. Run the go/no-go and switch the agents

    Project sponsor 1-2 hours

    Walk the exit criteria with the decision-maker, call it explicitly, then move agents over with a named person on hand for the first few hours. Keep Zammad read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.

  7. Use Zendesk's bulk delete APIs

    to remove imported tickets if needed (note: Zendesk permanently deletes tickets after 30 days in the deleted tickets view)

Zammad → Zendesk specifics

Tag all imported data
with a migration identifier (e.g., migration_zammad_2026)
Don't cancel your Zammad subscription or decommission infrastructure
until post-migration QA is fully signed off

Don't move on until

  • Full historical load complete and counts matched
  • Inbound channels repointed and verified with live test tickets
  • Rollback decision point passed explicitly, not by default
06 Validation Prove the migration is complete, then close it out. 0/7

Objective Documented evidence that data, workflow and reporting all survived, and a signed acceptance.

  1. Run the full reconciliation

    Data engineer 1-2 days

    Compare source and target on every object: total counts, counts by status, counts by group, attachment counts, and field-level spot checks on a random sample. Produce one report you can hand to an auditor.

    Migration Validation Tool Reconcile Zammad and Zendesk record-for-record
  2. Verify field completeness, not just record counts

    Data engineer 1 day

    Re-profile the loaded data and compare null rates per field against the source profile. Matching record counts with a field that silently arrived empty is the failure mode counts alone will never catch.

    Data Profiler Prove field completeness held up through the load
  3. Rebuild reporting and compare against baselines

    Support ops 2-3 days

    Recreate your core dashboards — volume, first response time, resolution time, CSAT — and compare to pre-migration figures for the same period. Explain every variance; a changed SLA calculation is a real finding, not a rounding error.

    SLA and first-response metrics are usually recalculated from the loaded timestamps, so they will differ if any timestamp mapping was approximate.

  4. Test the workflow layer end to end

    Support ops 2 days

    Fire every trigger, automation, SLA escalation, macro and notification with a live ticket. Workflow does not migrate — it gets rebuilt — so it is untested until someone has actually watched it run.

  5. Confirm compliance and produce the audit trail

    Compliance / DPO 1 day

    Re-scan the loaded data for regulated fields, confirm retention and deletion policies are configured in Zendesk, and file the evidence with your PII decisions from the audit phase.

    PII & Compliance Scanner Produce the compliance evidence your auditor will ask for
  6. Sign off, then decommission on a schedule

    Project sponsor 1 day

    Get written acceptance against the Discovery success criteria. Keep Zammad read-only for an agreed period (30-90 days is typical), take a final archive export, and only then cancel. Diarise the decommission date so it does not quietly renew.

  7. Record counts

    Compare ticket, user, and organization counts between source and target

Zammad → Zendesk specifics

Ongoing sync needed post-migration
API-to-API for historical data, then middleware or Zammad webhooks for forward sync.
Field spot-checks
Sample 50–100 tickets and compare field values side by side
Attachment verification
Confirm attachments are accessible and correctly linked
Relationship integrity
Verify that tickets point to the correct requester, assignee, and organization
Timestamp accuracy
Confirm created_at timestamps match original Zammad values

Don't move on until

  • Full reconciliation report attached to the project record
  • Reporting baselines match pre-migration figures within agreed tolerance
  • Formal acceptance signed and archive retention scheduled

Field mapping reference

The field-by-field mapping for each object. Use this as the starting point for your mapping spec.

Object Object 10 fields
Zammad fieldZendesk fieldNotes
Ticket Ticket 1:1 mapping. Use Ticket Import API (/api/v2/imports/tickets) or batch endpoint (/api/v2/imports/tickets/create_many)
Ticket Article Ticket Comment Map internal flag → public: false. Map article type and sender to determine comment author
User (customer) User (end-user) Create via Users API before importing tickets
User (agent) User (agent) Must exist in Zendesk before ticket import — tickets reference assignee_id and author_id
Organization Organization Create via Organizations API. Map organization_id on users
Group Group Create manually or via API. Map group_id on tickets
Tag Tag Applied to tickets during import
Knowledge Base Category Help Center Section Requires Zendesk Guide. Map category hierarchy
Knowledge Base Answer Help Center Article HTML content migration. Handle embedded images
Custom Object Attribute Custom Ticket/User/Org Field Must create fields in Zendesk first with matching data types
State Status 6 fields
Zammad fieldZendesk fieldNotes
new new Direct map
open open Direct map
pending reminder pending Map pending_time to Zendesk's due date if applicable
pending close pending Collapsed with pending reminder
closed closed See immutability warning below
merged closed Add internal note or tag referencing original ticket number
Zammad Zendesk 11 fields
Zammad fieldZendesk fieldNotes
number external_id Preserves original ticket number for agent cross-reference
title subject Direct map
state.name status See state mapping table above
priority.name priority Map: 1 low → low, 2 normal → normal, 3 high → high
group.name group_id Lookup group ID in Zendesk by name via crosswalk
owner_id assignee_id Map Zammad user ID → Zendesk user ID via crosswalk table
customer_id requester_id Map Zammad user ID → Zendesk user ID via crosswalk table
created_at created_at Direct map (ISO 8601). Must use Import API to set
updated_at updated_at Direct map
close_at solved_at Only for closed tickets
tags tags Array → comma-separated or array
Articles Comments 5 fields high

Zammad's rich article metadata (type, sender, internal flag, channel) must be flattened into Zendesk's simpler comment structure, risking loss of channel-specific context such as phone vs. SMS vs. social origin.

Zammad fieldZendesk fieldNotes
body html_body Zammad stores HTML. Requires inline image URL rewriting
internal public Invert: internal: true → public: false
created_by_id author_id Map via user crosswalk
created_at created_at Direct map
attachments uploads Download from Zammad, upload to Zendesk, attach via token
KB Object Guide Object 5 fields
Zammad fieldZendesk fieldNotes
Knowledge Base Help Center 1:1; Zendesk Guide must be enabled
Category (top-level) Category POST /api/v2/help_center/categories
Category (nested) Section POST /api/v2/help_center/categories/{id}/sections
Answer Article POST /api/v2/help_center/sections/{id}/articles
Translation Article Translation Separate article per locale, or use POST /api/v2/help_center/articles/{id}/translations
Zammad Zendesk 20 fields
Zammad fieldZendesk fieldNotes
ticket.number ticket.external_id Cross-reference key
ticket.title ticket.subject Direct map
ticket.state.name ticket.status Translate values (see state mapping above)
ticket.priority.name ticket.priority Map 1–4 scale
ticket.group.name ticket.group_id Lookup by name
ticket.customer_id ticket.requester_id Via user crosswalk
ticket.owner_id ticket.assignee_id Via user crosswalk
ticket.created_at ticket.created_at ISO 8601
ticket.close_at ticket.solved_at Only for closed tickets
article.body comment.html_body Sanitize inline image URLs
article.internal comment.public Invert value
article.created_by_id comment.author_id Via user crosswalk
article.created_at comment.created_at ISO 8601
article.type (no equivalent) Preserve as tag or body prefix
article.sender (no equivalent) Use to determine author_id context
user.firstname + lastname user.name Concatenate
user.email user.email Direct map
user.organization_id user.organization_id Via org crosswalk
organization.name organization.name Direct map
organization.domain organization.domain_names Wrap in array

Risk matrix

Per-object risk for this pair. Plan extra validation around anything marked high.

ObjectRiskNotes
Tickets high Tickets require full article-to-comment translation, timestamp preservation via the Import API, and careful handling of status/priority mapping between the two platforms' differing state models.
Ticket Articles / Comments high Zammad's rich article metadata (type, sender, internal flag, channel) must be flattened into Zendesk's simpler comment structure, risking loss of channel-specific context such as phone vs. SMS vs. social origin.
Attachments high Each attachment must be individually downloaded from Zammad and re-uploaded to Zendesk's Upload API, dramatically increasing API call volume and introducing failure points at scale.
Custom Fields high Zammad's object attributes and Zendesk's custom fields differ in type support, validation, and scoping, requiring manual schema creation on the Zendesk side and per-record value transformation.
Users / Contacts medium User records map relatively well between the two platforms, but role semantics differ (Zammad's role-based users vs. Zendesk's end-user/agent split), and deduplication must be handled during import.
Organizations low Organization records are structurally similar in both platforms and map with minimal transformation, though custom organization attributes need separate field mapping.
Groups medium Both platforms use groups for routing and assignment, but permission models and hierarchies differ, requiring manual group recreation and agent re-assignment in Zendesk.
Tags low Tags are simple string values in both systems and transfer cleanly, though Zammad tags are ticket-scoped while Zendesk supports tags on users and organizations as well.
Knowledge Base Articles medium Zammad's multilingual Knowledge Base with categories must be restructured into Zendesk Guide's section-and-article hierarchy, with potential loss of translation linkage and embedded media requiring re-upload.
SLA Metrics and Reporting Data high Zendesk's Ticket Import API does not carry over SLA metrics, meaning historical SLA compliance data, first-response times, and resolution metrics from Zammad cannot be reconstructed natively in Zendesk.

The hard parts

What makes this specific migration difficult, beyond the mechanics.

Article-to-Comment Data Translation

Zammad stores each message as a typed article with sender, type, and internal flags, which must be translated into Zendesk's simpler public/internal comment model, losing some channel-level nuance in the process.

Attachment Re-upload Workflow

Attachments must be individually downloaded from Zammad's per-article attachment endpoints and re-uploaded to Zendesk's Upload API to obtain tokens before they can be referenced in imported ticket comments.

Dual-Platform Rate Limiting

Both Zammad and Zendesk enforce API rate limits, requiring the migration pipeline to implement throttling, retry logic, and error recovery on both the extraction and loading sides simultaneously.

Custom Field Schema Mapping

Zammad's object attributes across tickets, users, organizations, and groups must be mapped to Zendesk's custom fields and potentially custom objects, with differences in field types, validation rules, and dropdown option values requiring careful translation.

Timestamp and History Preservation

Preserving original created_at and updated_at timestamps requires using Zendesk's Ticket Import API rather than the standard Ticket API, and this import endpoint does not support SLA metrics or side conversations.

No Native Migration Path

While Zammad provides a built-in Zendesk-to-Zammad migrator, there is no official reverse path, meaning the entire Zammad-to-Zendesk pipeline must be custom-built and tested from scratch.

Tools used in this playbook

All free, all run entirely in your browser — nothing is uploaded.

FAQ

Can I migrate full ticket history from Zammad to Zendesk?

Yes, but only via API. Zendesk's Ticket Import API (/api/v2/imports/tickets) lets you create tickets with multiple comments, preserving timestamps and author information. CSV imports only work for users and organizations, not ticket conversation history.

Does Zendesk have a built-in Zammad importer?

No. There is no native migration path between Zammad and Zendesk. Zammad documents a Zendesk → Zammad migrator, but there is no official reverse. You must extract data via Zammad's REST API and load it using Zendesk's Ticket Import API, or use a managed migration service.

What are Zendesk's API rate limits for ticket imports?

Zendesk rate limits depend on your plan: 200 req/min (Team), 400 (Growth), 700 (Professional), 2,500 (Enterprise). The Ticket Import endpoint shares the account-level limit. Exceeding it returns a 429 error with a Retry-After header.

Will inline images transfer from Zammad to Zendesk?

Not automatically. Zammad stores inline images as CID (Content-ID) references. Your migration script must parse the HTML body, download the CID image attachments, upload them to Zendesk, and rewrite the HTML source links to point to the new Zendesk URLs.

Are SLA metrics preserved when importing tickets into Zendesk?

No. Zendesk does not calculate SLA metrics on imported tickets — first reply time, resolution time, and similar metrics will be blank. Zendesk recommends tagging imported tickets and excluding them from SLA reports.

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