Migrating Kayako to Freshdesk requires archived=1 to extract closed tickets, special handling for timestamp preservation, rate-limit pacing, and a delta-sync architecture for zero-downtime cutover.
There is no native migration path from Kayako to Freshdesk; Freshdesk's built-in CSV import cannot handle conversation threads, attachments, or timestamp preservation, making an API-to-API scripted approach the only viable option for non-trivial migrations. The data models are structurally similar but differ in status/priority value encoding, conversation thread storage (Kayako posts vs. Freshdesk notes/replies), and custom field type enforcement. Custom engineering work is required to handle Kayako's archived ticket extraction (the archived=1 parameter), historical timestamp preservation (Freshdesk's ticket creation endpoint overwrites created_at with the current server time), rate-limit pacing, attachment re-upload, and suppression of outbound email notifications during bulk import.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
The Kayako REST API has no concept of staff, team, or department permissions
Once you authenticate, your credentials grant unrestricted read/write access to all helpdesk data. Treat these credentials with the same care as a database superuser account.
Kayako's total_count can change between pages if tickets are created or closed during extraction
Run your initial extraction during a low-traffic window and treat the result as a point-in-time snapshot.
Inline images in HTML ticket bodies are a common edge case
Kayako may store inline images as cid: references or src attributes pointing to authenticated Kayako endpoints. During migration, download these images, upload them to Freshdesk, and rewrite the references in the HTML body before creating the conversation entry. Skipping this step results in broken image placeholders in the agent UI.
The standard POST /api/v2/tickets/:id/reply endpoint sends email notifications to
The standard POST /api/v2/tickets/:id/reply endpoint sends email notifications to customers and this cannot be suppressed through API parameters. Using POST /api/v2/tickets/:id/notes with private: false creates a visible note without sending email. For historical conversations, this is typically the safer import path.
Even failed requests count against your rate limit
A 400 from a malformed payload or a 401 from a bad key still consumes a call. Validate your payloads before sending them. On a Growth plan, a tight loop of bad requests can burn through 100 calls in under a minute and lock you out.
Use an overlap window on every delta cycle
If your watermark is 2026-07-20T10:00:00Z, re-fetch from 09:50:00Z and deduplicate by case ID. That small overlap absorbs clock drift, delayed indexing, and race conditions around tickets being closed or reopened at the boundary.
Do not delete contacts that existed in Freshdesk before the migration
Your manifest should flag which contacts were pre-existing (matched by email) vs. created during import. Deleting a pre-existing contact removes their entire ticket history in Freshdesk.
When validating thread completeness, use GET /api/v2/tickets/{id}/conversations
Do not rely on include=conversations on the ticket view endpoint—it only returns up to 10 conversations. (developers.freshdesk.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.
Objective A written scope with agreed success criteria, a named owner per workstream, and a budget approved by finance.
Keep these open
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Pull the real numbers out of Kayako
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 -
Decide what history actually moves
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 -
Confirm Freshdesk can hold your support model
Walk your current workflow through Freshdesk: 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.
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Build the business case
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 Kayako → Freshdesk timeline -
Name owners and set the go/no-go date
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.
Kayako → Freshdesk specifics
- Kayako's archived ticket behavior
- Cases closed for 30+ days are moved to an archive. Default API calls to /api/v1/cases do not include them. If you skip the archived=1 parameter, you silently lose your entire closed ticket history. (help.kayako.com)
- Timestamp preservation on the Freshdesk side
- The standard Freshdesk POST /api/v2/tickets endpoint stamps the current server time as created_at. It does not accept a historical timestamp through the public API. Freshworks staff have confirmed this in community responses. (community.freshworks.com)
- Notification side effects
- Creating replies via the standard Freshdesk API sends email notifications to customers. During a bulk import of historical tickets, that means thousands of unwanted emails unless you take explicit steps to prevent it.
- No conversation thread import
- CSV import creates tickets with a description but cannot import reply chains, private notes, or conversation histories. Every multi-reply ticket arrives as a single flat entry.
- No attachment support
- Attachments must be uploaded separately via the API.
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.
Objective A profiled, cleaned export with every quality defect either fixed at source or explicitly accepted.
Keep these open
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Take a full Kayako export and profile it
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 Kayako export for nulls, outliers and type drift -
Validate file structure before anyone writes a transform
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 -
Inventory PII and set retention
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 -
Quantify duplicates, orphans and dead references
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 Kayako where you can — migrating them just moves the mess.
Data Cleaner Strip empty rows, stray whitespace and dead columns -
Clean and normalise the export
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.
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Produce a masked copy for sandbox work
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 -
Track everything in your manifest
Every Freshdesk ticket ID, contact ID, and company ID created during import should be logged with its source Kayako ID.
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Contact cleanup
Use DELETE /api/v2/contacts/:id for contacts created during migration. Contacts with associated tickets cannot be deleted until those tickets are removed first.
Kayako → Freshdesk specifics
- Bulk delete via API
- Use DELETE /api/v2/tickets/:id to remove migrated tickets. This moves them to trash (recoverable for 30 days). For permanent deletion, follow with DELETE /api/v2/tickets/:id/restore is not needed—tickets in trash auto-purge. Freshdesk rate limits apply to deletes as well.
- Deletion throughput
- At 70% rate utilization on a Pro plan, you can delete approximately 280 records per minute. Cleaning up 50,000 tickets takes roughly 3 hours.
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.
Objective A reviewed field-level mapping covering every object, with an explicit decision for every field that has no target.
Keep these open
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Generate the first-pass Kayako → Freshdesk field map
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 Kayako → Freshdesk field pair -
Map status, priority and channel values, not just field names
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.
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Decide how custom fields land
Create the target custom fields first, matching type exactly (a dropdown mapped to free text can never be mapped back). Where Freshdesk has no equivalent, decide between a new custom field, a tag, or a note appended to the ticket body — and record which.
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Resolve identity and threading
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.
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Plan attachments, inline images and threading order
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.
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Freeze and sign off the mapping spec
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.
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Validate your field mapping
by importing 100–200 representative tickets spanning different statuses, priorities, and custom field combinations.
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Confirm custom field configurations
Create all custom fields in the sandbox first. Run your import script and check that dropdown values, date formats, and checkbox fields populate correctly.
Data Format Converter Reshape the export into the format Freshdesk's importer expects
Kayako → Freshdesk specifics
- No custom field type enforcement preview
- Dropdown values that don't exist in Freshdesk are silently dropped rather than flagged.
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.
Objective A pilot load into a Freshdesk sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Freshdesk sandbox that matches production config
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.
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Pick a deliberately nasty pilot sample
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.
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Run the load with masked data and instrument everything
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 -
Measure real throughput against the rate limit
Record achieved records-per-hour under Freshdesk'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.
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Reconcile the pilot and triage every failure
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 -
Put real agents in front of the pilot data
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.
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Test notification behavior
Verify that your import approach (notes vs. replies) does not trigger outbound emails.
Kayako → Freshdesk specifics
- Measure throughput
- Time how long 1,000 tickets take to import at your plan's rate limit to estimate total migration duration.
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.
Objective All in-scope data live in Freshdesk, agents working in the new system, and a rollback path that stayed available throughout.
Keep these open
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Pre-load history before the freeze
Load closed tickets and contacts days or weeks ahead while Kayako 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 Freshdesk's real API limits -
Publish the runbook with times, owners and abort criteria
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.
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Freeze Kayako and take the final delta
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.
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Load the delta and open tickets
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 -
Repoint channels and verify with live traffic
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 -
Run the go/no-go and switch the agents
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 Kayako read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
Kayako → Freshdesk specifics
- Practice rollback
- Test your cleanup script (see Rollback Strategy section) to confirm you can delete test data cleanly.
- Initial bulk sync
- Extract all Kayako cases (including archived), users, organizations, posts, and attachments. Map, transform, and load them into Freshdesk. This is the longest phase—see throughput estimates above for planning.
- Delta sync loop
- After the bulk sync completes, run incremental pulls from Kayako using updated_at filters to capture only tickets created or modified since the bulk extraction started. Push these deltas to Freshdesk. Run this every 2–4 hours, always including archived=1 so closed history does not disappear from late passes.
- Final delta sync
- Run one last delta pull. The time window should be minutes, not hours. On a 50K-ticket account, the final delta typically covers 50–200 tickets if timed correctly.
- DNS/routing switch
- Redirect your support email forwarding and widget endpoints from Kayako to Freshdesk.
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.
Objective Documented evidence that data, workflow and reporting all survived, and a signed acceptance.
Keep these open
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Run the full reconciliation
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 Kayako and Freshdesk record-for-record -
Verify field completeness, not just record counts
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 -
Rebuild reporting and compare against baselines
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.
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Test the workflow layer end to end
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.
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Confirm compliance and produce the audit trail
Re-scan the loaded data for regulated fields, confirm retention and deletion policies are configured in Freshdesk, and file the evidence with your PII decisions from the audit phase.
PII & Compliance Scanner Produce the compliance evidence your auditor will ask for -
Sign off, then decommission on a schedule
Get written acceptance against the Discovery success criteria. Keep Kayako 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.
Kayako → Freshdesk specifics
- Verification
- Run ticket count comparisons, spot-check content, and validate custom field mapping.
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.
Resource Endpoint
| Kayako field | Freshdesk field | Notes |
|---|---|---|
| Cases (tickets) | GET /api/v1/cases | Add archived=1 for closed tickets |
| Case messages/posts | GET /api/v1/cases/:id/posts | Includes replies and notes. Supports MESSAGES and NOTES filters. |
| Users | GET /api/v1/users | Both customers and agents |
| Organizations | GET /api/v1/organizations | Company records |
| Custom fields | GET /api/v1/cases/fields | Field definitions and types |
| Attachments | Included in message/post responses | Download via content_url |
Kayako Freshdesk
| Kayako field | Freshdesk field | Notes |
|---|---|---|
| subject | subject | Direct 1:1. If blank, synthesize from the first public post. |
| First message content | description | HTML content. Check for inline images. |
| status | status | Map to Freshdesk integers: 2=Open, 3=Pending, 4=Resolved, 5=Closed |
| priority | priority | Map to Freshdesk integers: 1=Low, 2=Medium, 3=High, 4=Urgent |
| type | type | Must pre-create matching type values in Freshdesk first |
| tags | tags | Array of strings. Normalize casing and whitespace first. |
| assigned_agent | responder_id | Map by email address. Agent must exist in Freshdesk. |
| assigned_team | group_id | Map team names to Freshdesk group IDs |
| requester / user_id | requester_id or email | Match by email. Create contact if missing. |
| created_at | created_at | Requires special import handling (see timestamp section) |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Tickets | high | Archived tickets are silently excluded without the `archived=1` parameter, and Freshdesk overwrites original creation timestamps, creating significant data loss and reporting accuracy risks. |
| Conversation Threads | high | Multi-reply threads and private notes require per-ticket post extraction and careful ordering (Kayako returns posts in reverse order), with incorrect handling flattening or disordering conversation history. |
| Contacts | medium | Freshdesk matches contacts by email only, and mismatches silently create duplicate contact records rather than flagging errors. |
| Companies/Organizations | medium | Organization records must be pre-created in Freshdesk before ticket import, and ID-based relationship linking is not supported through CSV import. |
| Custom Fields | high | Dropdown values that do not exist in Freshdesk are silently dropped rather than flagged, and custom field type enforcement is not previewed before import. |
| Attachments | medium | Attachments are not supported via CSV import and must be individually downloaded from Kayako's `content_url` and re-uploaded via the Freshdesk API, adding significant transfer time and failure points. |
| Agents and Assignment | low | Agent mapping is straightforward via email address lookup, but all agents must be pre-created in Freshdesk before import to resolve `responder_id` references. |
| Tags | low | Tags transfer as simple string arrays but require normalization of casing and whitespace to avoid creating near-duplicate tag entries in Freshdesk. |
| Statuses and Priorities | medium | Kayako and Freshdesk use different status and priority value schemas requiring explicit integer mapping (e.g., Freshdesk uses 2=Open, 3=Pending, 4=Resolved, 5=Closed), with unmapped values causing import failures. |
| Inline Images | medium | HTML ticket descriptions may contain inline images referencing Kayako-hosted URLs that will break after migration unless re-hosted and rewritten to point to new locations. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Archived Ticket Silent Exclusion
Kayako's default API response excludes cases closed for 30+ days unless the `archived=1` parameter is explicitly set, silently dropping entire closed ticket histories.
Timestamp Preservation Limitation
Freshdesk's standard ticket creation endpoint stamps the current server time as `created_at` and does not accept historical timestamps through the public API, breaking historical reporting accuracy.
Unwanted Notification Side Effects
Creating replies via the Freshdesk API triggers outbound email notifications to customers, which during bulk historical import can result in thousands of erroneous emails for long-resolved tickets.
Conversation Thread Flattening Risk
Kayako stores multi-reply conversations as ordered posts on a case, and Freshdesk's CSV import cannot ingest reply chains or private notes, reducing threaded conversations to single flat entries.
Rate Limiting and Throughput
Both Kayako and Freshdesk impose API rate limits (Freshdesk trial accounts cap at 50 requests/minute), requiring careful pacing and retry logic to avoid throttling during large-scale data transfers.
Delta Sync Without Server-Side Filtering
Kayako Cloud's API does not support a server-side `updated_since` query parameter, forcing full dataset fetches and client-side filtering for delta synchronization during cutover.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
How do I export archived tickets from the Kayako API?
By default, the Kayako GET /api/v1/cases endpoint only returns active tickets. Add the archived=1 query parameter to include cases closed more than 30 days ago. Without it, your entire closed ticket history is silently excluded. Example: /api/v1/cases?limit=100&offset=0&archived=1.
Can I set historical created_at timestamps when importing tickets to Freshdesk?
The standard Freshdesk POST /api/v2/tickets endpoint does not accept a historical created_at value—it always stamps the current server time. Freshworks staff have confirmed the public API does not support backdating. To preserve original timestamps, contact Freshdesk support for import API access, use a managed migration service with established import pathways, or store original dates in a custom field as a workaround.
What are Freshdesk API rate limits by plan?
Freshdesk enforces account-level per-minute limits: Growth gets 100 calls/min, Pro gets 400 calls/min, and Enterprise gets 700 calls/min. Trial accounts are limited to 50 calls/min. There are also per-endpoint sub-limits. Always check the X-RateLimit-Remaining response header for your actual allocation.
Does the Freshdesk API send email notifications when creating replies during migration?
Yes. The standard POST /api/v2/tickets/:id/reply endpoint sends email notifications to customers and this cannot be suppressed via API parameters. To avoid mass-emailing customers during a historical import, use POST /api/v2/tickets/:id/notes with private: true for internal notes, or private: false for visible notes that do not trigger email.
How do I bypass the Freshdesk 300-ticket search limit?
Freshdesk search returns 30 results per page with a maximum of 10 pages (300 tickets total). Use time-based chunking: query small date ranges and slide the window forward to keep results under 300 per query. For count verification, use the List All Tickets endpoint with updated_since filters instead of search. For migration lookups, maintain a local manifest table keyed by source ID rather than relying on search.