Front's native importer caps at 9,000 tickets and is unmaintained. Automated tools drop custom fields and inline images. An engineer-led API migration is the only path to full data fidelity.
Migrating from Freshdesk to Front is a data-model translation problem. Freshdesk organizes support around structured tickets with statuses, priorities, and groups. Front organizes everything around collaborative conversation threads inside shared inboxes. The structural gaps are where data silently disappears: Front's native importer is capped at 9,000 tickets and unmaintained, automated tools drop custom fields and inline images, and both platforms enforce strict rate limits that govern migration speed. A full-fidelity migration requires engineer-led API work to preserve custom fields, rewrite inline image HTML references, handle Front's async import behavior, and manage exponential backoff across both platform's rate limit budgets.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
Do not treat Front's native importer as a full-history migration path
Front states the Freshdesk importer is not actively maintained, does not import tags, and is limited to the most recent 9,000 tickets. That is acceptable for a small test or lightweight move. It is not enough for most mature support teams. (help.front.com)
Custom field mapping is a design decision, not a default
Front supports custom fields on conversations, contacts, and accounts — but no automated migration tool maps them. You must create target fields in Front before import and decide where each Freshdesk field lands: conversation custom fields for ticket-level metadata, contact custom fields for requester data, account custom fields for company data, or tags for categorical values. Front caps custom fields at 50 per category. (help.front.com)
Preserve original IDs
Store the Freshdesk ticket ID in a Front conversation custom field and use stable external_id values on imported messages. This gives you idempotent re-runs, easier QA, and a clean rollback trail.
Practical throughput for export
On a Growth plan, expect roughly 25–30 minutes just to list all ticket IDs for a 50,000-ticket account — before downloading any conversations or attachments. Enterprise plans cut this roughly in half.
The 429 trap
When you exceed the limit, Front returns a 429 Too Many Requests response with a Retry-After header. If your script continues to send requests without honoring that header, the lockout window resets and extends — pushing your migration further into the future with every additional request. Your scripts must include exponential backoff logic. Hammering the API after a 429 makes things worse, not better.
History parity is not ticket-count parity
You can match ticket counts and still lose private notes, later replies, author attribution, or inline screenshots. Always diff a sample at the conversation-event level, not just the record-count level. Start with any Freshdesk ticket that has more than 10 conversation events — those are the ones most likely to be truncated by naive extraction. (developers.freshdesk.com)
Automate what you can
If you are migrating more than a few hundred tickets, write a validation script that pulls migrated conversations from Front's API and compares event counts, field values, and attachment counts against your Freshdesk source data. Manual spot-checking does not scale.
Compliance and data retention
If you operate in a regulated industry (healthcare, finance, legal), consider preserving a full Freshdesk data export in its native format before you decommission the account. Freshdesk's admin panel export (Admin → Account → Export Data) produces CSV files that serve as a compliance-friendly archive alongside your migrated Front data. This is separate from the migration itself — it is your insurance policy for audits or legal discovery that require records in their original system format.
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 Freshdesk
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 Front can hold your support model
Walk your current workflow through Front: 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 Freshdesk → Front 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.
Freshdesk → Front specifics
- Cross-functional collaboration needs
- Teams that involve sales, ops, or engineering in customer threads find Freshdesk's siloed group model limiting.
- Multi-channel unification
- Front aggregates email, SMS, WhatsApp, social, and live chat into a single inbox natively. Freshdesk requires toggling between Freshdesk, Freshchat, and Freshcaller.
- Outgrowing ticket-centric UX
- Growing teams with relationship-driven support — logistics, fintech, B2B SaaS — often find Freshdesk's impersonal ticket IDs at odds with the customer experience they want to deliver.
- Inline image handling
- Our scripts extract, download, and re-attach inline images so your historical context survives the migration intact.
- Zero downtime execution
- Your support team keeps working in Freshdesk during the migration. We handle delta syncs to catch tickets created or updated during the transfer window, so nothing is lost in the cutover. See Zero-Downtime Help Desk Data Migration: How to Keep Support Running During the Move.
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 Freshdesk 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 Freshdesk 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 Freshdesk 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
Freshdesk → Front specifics
- Rate limit engineering
- We build exponential backoff, per-endpoint throttling, and queue management into every migration script. Our partner OAuth integration uses a separate 120 rpm rate limit bucket that does not count against your company's standard Front API budget. (dev.frontapp.com)
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 Freshdesk → Front 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 Freshdesk → Front 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 Front 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.
Freshdesk → Front specifics
- High-cardinality text fields
- (order IDs, case numbers): Write to a Front conversation custom field or an internal comment with a structured prefix.
- Categorical fields
- (department, product line): Convert to Front tags with a namespace (e.g., product:widget-pro) or a Front custom field dropdown.
- Custom field spot checks
- Pick 10 tickets with populated custom fields and verify the values landed in the correct Front conversation, contact, or account fields.
- Custom field preservation
- We map structured custom field data to Front's conversation, contact, and account custom fields — or to tags and internal comments when that fits your workflow better. Automated tools drop this data entirely.
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 Front sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Front 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 Front'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.
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 Front, 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 Freshdesk 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 Front'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 Freshdesk 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 Freshdesk read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
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 Freshdesk and Front 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 Front, 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 Freshdesk 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.
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Record-count reconciliation
Compare total ticket count in Freshdesk against total conversation count in Front. A mismatch means tickets were dropped — check for API errors, rate limit failures, or workspace scoping issues.
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Contact and account linking
Verify that migrated conversations are linked to the correct Front contacts and accounts, not orphaned or assigned to a default.
Freshdesk → Front specifics
- Conversation-event-level diffing
- For a random sample of 20–50 migrated tickets, compare the number of replies and notes in Freshdesk against messages and comments in Front. This catches the 10-event truncation problem and missing private notes.
- Attachment integrity
- Open 5–10 migrated conversations that had attachments. Verify files download correctly and inline images render in the message body.
- Timestamp verification
- Check that message timestamps in Front match the original Freshdesk conversation timestamps. Remember that Front comments (migrated private notes) will show the migration date, not the original date — verify you preserved the original timestamp in the comment body if that was part of your plan.
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.
Freshdesk Front Data Mapping
| Freshdesk field | Front field | Notes |
|---|---|---|
| Ticket | Conversation | 1:1 mapping. Ticket subject becomes conversation subject. |
| Agent | Teammate | Match by email. Unmatched agents fall back to a default teammate. |
| Group | Inbox | Each Freshdesk group maps to a Front inbox within a workspace. |
| Requester (Contact) | Contact | Matched by email handle. Front enforces unique email per contact. |
| Company | Account | Domain matching is the cleanest reconnection method. Automated tools skip this entirely. |
| Public Reply | Inbound/Outbound Message | Direction depends on whether the author is the requester or an agent. |
| Private Note | Comment | Front comments are internal-only, matching Freshdesk private notes. |
| Custom Fields | Conversation/Contact/Account Custom Fields | Front supports custom fields on all three object types, but they must be pre-created. 50-field-per-category limit. (help.front.com) |
| Tags | Tags | 1:1 mapping. The native importer drops tags; API-led migrations preserve them. (help.front.com) |
| Ticket Status | Conversation Status | Freshdesk has 4+ statuses; Front has Open, Archived, Trash, Spam. Custom mapping required. |
| Attachments | Attachments | File attachments transfer. Inline images require special handling (see below). |
| Satisfaction Rating | (no native equivalent) | CSAT data does not have a target object in Front's model. Export CSAT scores to a spreadsheet or preserve them as conversation comments with a structured prefix (e.g., [CSAT: 5/5]) before migration so the data is not lost entirely. |
| Ticket Priority/Source | (no native equivalent) | Must be encoded as tags or conversation custom fields, or dropped. |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Inline Images | high | Dropped by all automated tools; requires engineer-led HTML rewriting to preserve inline rendering |
| Custom Fields | high | No automated tool migrates them; must be pre-created in Front with explicit mapping decisions |
| Ticket History (>10 events) | high | Embedded conversation view silently truncates at 10 events; dedicated endpoint required |
| Organizations to Accounts | high | Skipped by native importer and automated tools; domain uniqueness constraint applies |
| Contact Deduplication | high | Front enforces unique email/phone per contact; duplicates in Freshdesk cause import failures |
| Ticket Statuses | medium | Freshdesk's multiple statuses must map to Front's limited set with original preserved as tags |
| Tags | medium | Native importer drops them entirely; only API-led migrations preserve tag data |
| Attachments | medium | File attachments transfer but inline images require separate handling |
| Private Notes | medium | Map to Front comments; timestamps default to migration date rather than original |
| Satisfaction Ratings | high | No native equivalent in Front's model; data must be encoded as tags or dropped |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Inline Image Preservation
Freshdesk stores inline images as HTML references; preserving them requires downloading binaries, re-uploading to Front, and rewriting HTML body references.
Rate Limit Bottleneck
Front's Growth plan allows only 100 requests/min with 5/sec burst limits, making the write side the bottleneck for any large migration.
Custom Field Mapping
Front supports 50 fields per category across conversations, contacts, and accounts — but no automated tool maps them, requiring manual pre-creation and design decisions.
Status Model Mismatch
Freshdesk has 4+ statuses plus custom statuses; Front only supports Open, Archived, Trash, and Spam — requiring creative mapping with tag preservation.
Native Importer Limitations
Front's built-in importer caps at 9,000 tickets, drops tags, and is officially not actively maintained.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I migrate more than 9,000 tickets from Freshdesk to Front?
Not with Front's native importer — it is hard-capped at 9,000 of your most recent tickets and cannot be re-run for the next batch. For larger accounts, you need either a SaaS migration tool like Help Desk Data Migration or an engineer-led API migration that pages through the full Freshdesk dataset using the REST API.
Does Help Desk Data Migration support custom fields and inline images when migrating to Front?
No. Help Desk Data Migration's official Front checklist explicitly lists custom fields, inline images, and organizations under data that will not be migrated. Custom field and inline image preservation requires a custom API migration.
What are the Freshdesk API rate limits for a migration?
Freshdesk enforces per-minute rate limits by plan: Growth allows 200 calls/min, Pro allows 400/min, and Enterprise allows 700/min. Trial accounts are capped at 50/min. These limits are account-wide, and per-endpoint sub-limits further restrict specific calls like ticket listing to roughly 20 calls/min on Growth.
What happens to inline images during a Freshdesk to Front migration?
Inline images are stored as references in Freshdesk's HTML ticket bodies. Automated tools and Front's native importer drop them or convert them to disconnected attachments. Preserving inline rendering requires an engineer-led migration that downloads the images, re-uploads them to Front, and rewrites the HTML body references.
How long does a Freshdesk to Front migration take?
It depends on ticket volume, API plan tiers, and attachment sizes. A 20,000-ticket account on Freshdesk Growth and Front Growth plans typically takes 8–12 hours for a full API migration. Front's native importer runs at roughly 3,000 tickets per hour but caps at 9,000 total.