Freshdesk to Pylon migration requires dependency-ordered loading, write-side rate-limit throttling, and special handling for the 30k-ticket pagination ceiling and temporary S3 attachment URLs.
Migrating from Freshdesk to Pylon requires moving data from a traditional email-first ticketing system to an AI-native, Slack-first B2B support platform with fundamentally different data models — Tickets become Issues, Companies become Accounts, and Conversations become Messages. There is no native import path between the two platforms, and Freshdesk's CSV exports lack conversation histories, attachments, and archived tickets. A custom API-based ETL pipeline or managed migration service is required, complicated by asymmetric rate limits: Freshdesk allows 200–700 requests per minute for extraction while Pylon's issue creation endpoint is limited to 10 requests per minute for writes.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
Disable all Freshdesk automations and notification rules before starting any data export
Bulk API reads can trigger webhooks, which consume your rate limit and can send unintended emails to customers.
Pylon's issue creation API accepts a created_at timestamp (RFC3339), which lets you
Pylon's issue creation API accepts a created_at timestamp (RFC3339), which lets you preserve original ticket creation dates. If omitted, the current time is used — destroying your historical timeline.
Custom objects and schema constraints
Pylon has a streamlined schema. If you rely heavily on Freshdesk Custom Objects or integrations that simulate custom objects (e.g., asset tracking), those patterns don't transfer directly. Flatten this data into standard Issue attributes or Account-level fields. Push CRM data back to the CRM rather than forcing it into the support platform.
Multi-company contacts
Freshdesk can associate contacts with multiple companies. Pylon's contact creation centers on one account reference at a time. Decide early whether to collapse those relationships, duplicate contacts selectively, or model the intermediary as a partner account. (support.freshdesk.com)
Common Pitfalls Summary
- API Rate Limits: Freshdesk allows 200–700 req/min globally; Pylon restricts issue creation to 10 req/min. You must build a write-side throttle. - Attachments: Freshdesk provides temporary S3 URLs. You must download and re-host them before pushing to Pylon. - Data Loss: Relying on CSV exports will orphan your attachments and flatten your conversation threads.
Inline images will break
Freshdesk inline images in HTML ticket descriptions use Freshdesk-hosted URLs (*.freshdesk.com/...). After you cancel Freshdesk, those URLs die. Every inline image in every ticket description must be parsed, downloaded, re-hosted, and URL-rewritten before loading into Pylon.
Run this during extraction, not as a post-processing step
If you wait until after Freshdesk account cancellation, the source URLs will be dead and the images are gone permanently.
When creating issues via API, set destination_metadata.destination to "internal" to
When creating issues via API, set destination_metadata.destination to "internal" to prevent Pylon from sending email or Slack notifications to the customer for every historical ticket you import.
Do not cancel your Freshdesk account until email routing is fully verified in Pylon and
Do not cancel your Freshdesk account until email routing is fully verified in Pylon and you have confirmed no emails are still arriving at Freshdesk. A premature cancellation means bounced customer emails.
Before rebuilding, audit which automations are actually used
Many Freshdesk instances accumulate dozens of Dispatch'r and Observer rules over the years, some conflicting. Migration is a good time to prune. Export the list from Freshdesk (Admin → Automations), mark each as keep/discard, then only rebuild the ones that matter.
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 Pylon can hold your support model
Walk your current workflow through Pylon: 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 → Pylon 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 → Pylon specifics
- Channel alignment
- B2B customers live in Slack Connect and Teams. Pylon integrates natively with these channels, plus Discord, WhatsApp, and in-app chat, so customers never leave their workspace. Freshdesk treats chat as a bolt-on to an email-first architecture.
- Account-centric support model
- Pylon ties every Issue to an Account, giving agents full customer context — health scores, CRM data, renewal risk — directly in the support view. Freshdesk can achieve this with custom apps and marketplace add-ons, but the experience requires significant configuration.
- AI-native workflows
- Pylon's AI handles triage, routing, knowledge gap detection, and draft replies out of the box. Freshdesk's Freddy AI capabilities are available as paid add-ons (Copilot, AI Agent sessions), with sessions that expire monthly.
- Small business, <1,000 tickets, low engineering bandwidth
- Use a self-service tool if official Freshdesk-to-Pylon support exists. Otherwise, go straight to a managed service.
- Enterprise, attachments, deep history, or account hierarchy
- API-based ETL or managed service. CSV and iPaaS are false economies here.
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 → Pylon specifics
- Pylon data cleanup
- If you need to wipe the Pylon instance and retry, use Pylon's bulk delete API. Delete in reverse load order: Messages → Issues → Contacts → Accounts.
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 → Pylon 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 → Pylon 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 Pylon 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.
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 Pylon sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Pylon 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 Pylon'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 the full loop before cutover
Send a test email to support@yourdomain.com and verify it creates an Issue in Pylon with correct routing.
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Fix forward
if: issues are isolated (specific field mapping errors, a handful of missing attachments), email is routing correctly, and agents can work while you patch.
Freshdesk → Pylon specifics
- Email routing revert
- If you updated forwarding rules, keep the old Freshdesk intake address documented so you can re-point in minutes. If you changed MX records, keep the old values documented (MX revert takes up to 48 hours — this is your biggest rollback latency).
- Slack/Teams channels
- Document which channels were reconnected to Pylon. To roll back, disconnect from Pylon and re-bind to Freshdesk.
- Staging database is your safety net
- Do not delete your staging database until the migration is fully signed off. If the Pylon import fails or data corruption is discovered, you can wipe Pylon and re-run the load from staging without re-extracting from Freshdesk.
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 Pylon, 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 Pylon'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.
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Update email forwarding rules
If your support email uses forwarding (e.g., Google Workspace or Microsoft 365 forwarding support@ to Freshdesk's intake address like support@yourdomain.freshdesk.com), update the forwarding destination to Pylon's intake address.
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Update SPF and DKIM records
to authorize Pylon to send email on behalf of your domain. Without this, outbound replies from Pylon may land in spam.
Freshdesk → Pylon specifics
- Before cutover — set up email in Pylon
- Configure your support email address in Pylon's settings so it is ready to receive.
- If using MX records pointing to Freshdesk
- Update MX records to point to your email provider (Google Workspace, Microsoft 365, etc.) and set up forwarding from there to Pylon. MX record changes can take up to 48 hours to propagate — plan for this.
- During the transition period,
- monitor both Freshdesk and Pylon for 24–48 hours to catch emails that arrive at the old destination due to DNS propagation delays.
- Do not cancel Freshdesk until at least 7 days post-cutover
- Keep it in read-only mode (disable email intake, remove agent licenses).
- Rollback
- if: data corruption is widespread (>5% of records), email routing is failing and cannot be fixed within 2 hours, or agents cannot perform basic support operations in Pylon.
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 Pylon 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 Pylon, 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.
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
| Freshdesk field | Pylon field | Notes |
|---|---|---|
| Company | Account | Pylon supports subaccounts and partner accounts. Preserve source company ID as external_id. |
| Contact | Contact | Link to Accounts via account_id. Match by email. |
| Agent | User | Pylon uses roles (Admin, Member, etc.). Manual rebuild. |
| Ticket | Issue | Core mapping — see field table below. |
| Conversation (reply/note) | Message | Each reply becomes a Message on the Issue. Preserve internal vs. public visibility strictly. |
| Ticket attachment | Attachment URL | Must be re-hosted; Freshdesk S3 URLs are temporary. |
| Knowledge Base article | Knowledge Base article | Separate API; requires collection structure. Migrate as a separate workstream. |
| Tags | Tags | Direct mapping; create tags in Pylon first. Normalize casing before load. |
| Custom fields | Custom fields | Must pre-create in Pylon with matching slugs. |
| Groups | Teams | Manual rebuild. |
| Canned responses | Macros | Manual rebuild. |
| SLAs | SLAs | Manual rebuild. |
| Automations | Triggers | Manual rebuild — see Automation Rebuild section below. |
| Parent/child companies | Subaccounts | Use when roll-up reporting matters. |
| Agencies / consultants | Partner accounts | Avoid duplicating cross-customer contacts. |
Tickets Issues
The 30,000-ticket pagination ceiling, archived ticket exclusions, and Pylon's 10 req/min write limit make large-scale ticket migration the highest-risk entity requiring date-range segmentation and extended write windows.
| Freshdesk field | Pylon field | Notes |
|---|---|---|
| id | external_id | Store for cross-reference and idempotent reruns |
| subject | title | Direct |
| description / description_text | body_html | Freshdesk provides HTML; Pylon accepts HTML. Parse for inline images. |
| status (2=Open, 3=Pending, 4=Resolved, 5=Closed) | state (new, waiting_on_you, waiting_on_customer, on_hold, closed) | Integer → string mapping (see below) |
| priority (1=Low, 2=Med, 3=High, 4=Urgent) | priority (low, medium, high, urgent) | Integer → string |
| requester_id | contact_id | Lookup by email from pre-migrated contacts |
| responder_id | assignee_id | Lookup by email from pre-created users |
| company_id | account_id | Lookup from pre-migrated accounts |
| tags | tags | Array → array |
| created_at | created_at | RFC3339 format |
| updated_at | — | Used for delta sync polling (see Delta Sync section); not mapped to a Pylon field |
| custom_fields.cf_* | custom_fields [{slug, value}] | Map cf_ prefix fields to Pylon slugs |
| attachments [].attachment_url | attachment_urls | Download from Freshdesk S3, re-host, pass URLs |
Freshdesk Pylon Automation Mapping
| Freshdesk field | Pylon field | Notes |
|---|---|---|
| Dispatch'r (ticket creation rule): Assign to group based on tag | Pylon Trigger: condition on tag → action: assign to Team | Create the Team first, then build the trigger |
| Dispatch'r: Set priority based on subject keyword | Pylon Trigger: condition on title contains keyword → action: set priority | Pylon triggers support regex or keyword matching on issue title |
| Dispatch'r: Auto-assign to agent based on company | Pylon Trigger: condition on account → action: assign to user | Requires account to be linked on issue creation |
| Observer (ticket update rule): Notify manager when priority = Urgent | Pylon Trigger: condition on priority change to urgent → action: send notification | Configure notification channel (Slack, email) in Pylon |
| Observer: Reopen ticket on customer reply | Pylon: Built-in behavior | Pylon reopens closed issues on new activity by default |
| Supervisor (time-based rule): Escalate if no response in 4 hours | Pylon SLA policy + escalation trigger | Set up SLA policy first, then configure breach action |
| Canned responses | Pylon Macros | Manual recreation — export canned response text from Freshdesk, create as macros in Pylon |
| Auto-responder on ticket creation | Pylon Trigger: on issue creation → action: send reply | Configure reply template in Pylon |
| Satisfaction survey after resolution | Pylon CSAT configuration | Enable in Pylon settings; configure timing and channel |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Tickets/Issues | high | The 30,000-ticket pagination ceiling, archived ticket exclusions, and Pylon's 10 req/min write limit make large-scale ticket migration the highest-risk entity requiring date-range segmentation and extended write windows. |
| Conversation Threads | high | Freshdesk's reply/note conversation model must be flattened into Pylon's Messages structure, and CSV exports completely omit conversation histories, making API extraction mandatory. |
| Attachments | high | Temporary S3 URLs from Freshdesk expire and require individual HTTP downloads followed by re-uploads to Pylon, adding significant API call volume and potential for data loss if URLs expire mid-migration. |
| Contacts | medium | Freshdesk separates Contacts and Agents into different APIs while Pylon uses a unified Contacts and Users model, requiring deduplication and role-mapping logic during transformation. |
| Companies/Accounts | medium | Freshdesk's flat company model must map to Pylon's richer Account structure with subaccounts and partner accounts, and relationship hierarchies need to be manually reconstructed. |
| Custom Fields | medium | Custom fields on tickets, contacts, and companies must be individually mapped to Pylon's issue, contact, and account custom fields, with picklist values requiring exact matching or remapping. |
| Tags and Categories | low | Tags are simple string values that transfer relatively cleanly, though category taxonomies may need restructuring to align with Pylon's labeling conventions. |
| Knowledge Base Articles | medium | Standard migration tools often fail on knowledge base content, particularly inline images and folder structures, requiring dedicated extraction and reformatting for Pylon's knowledge system. |
| Automations and SLAs | high | Freshdesk automations, SLA policies, and business hours have no migration path and must be completely rebuilt in Pylon's workflow engine, as these are configuration objects not transferable via API. |
| CSAT and Satisfaction Data | medium | Customer satisfaction scores and survey responses are not reliably handled by standard migration tools and lack a direct equivalent object in Pylon's data model. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Asymmetric API Rate Limits
Freshdesk allows 200–700 requests/minute for extraction while Pylon's issue creation endpoint caps at 10 requests/minute, requiring write-side throttling and buffering to prevent pipeline failures.
Freshdesk Pagination Ceiling
Freshdesk's List All Tickets API is hard-capped at 30,000 tickets (300 pages × 100 records), forcing accounts with larger volumes to use date-range segmentation or the full Account Export feature.
Attachment Re-hosting Complexity
Freshdesk serves attachments via temporary S3 URLs embedded in ticket/conversation JSON, requiring separate HTTP GET requests to download each file and re-upload it to Pylon before linking.
Conversation Threading Preservation
Freshdesk models conversations as replies and notes on tickets while Pylon uses a flat Messages structure on Issues, requiring careful transformation to maintain thread context and chronological ordering.
Status and Priority Mapping
Freshdesk uses integer-based priorities (1–4) and a different status taxonomy (Open, Pending, Resolved, Closed plus custom statuses) that must be mapped to Pylon's string-based equivalents and custom status configurations.
Archived Ticket Recovery
Freshdesk automatically archives closed inactive tickets after 120 days and excludes them from standard UI exports, requiring the full Account Export or API-based date-range queries to retrieve historical data.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I migrate Freshdesk tickets to Pylon using CSV export?
Only partially. Freshdesk CSV exports include ticket metadata but not full conversation histories, attachments, or archived tickets. Pylon has no CSV import UI, so you still need the API to load data. For anything beyond a basic snapshot of fewer than 1,000 tickets, API-based migration is required.
What are the Freshdesk API rate limits for data migration?
Freshdesk enforces account-wide rate limits by plan: Growth gets 200 calls/min, Pro gets 400/min, and Enterprise gets 700/min. Individual endpoints have sublimits — ticket listing is roughly 20 calls/min on Growth. The limit is shared with all integrations, and even failed requests count. The List All Tickets endpoint is capped at 300 pages (30,000 tickets max).
What is Pylon's API rate limit for importing data?
Pylon enforces per-endpoint rate limits: 60 req/min for account and contact operations, 10 req/min for issue creation, 20 req/min for issue search, and 10 req/min for attachment uploads. This is significantly lower than Freshdesk's extraction limits, so your pipeline needs a write-side throttle.
How do I migrate Freshdesk ticket attachments to Pylon?
Freshdesk provides temporary S3 URLs for attachments in the API response. Download each file via authenticated GET requests during extraction (URLs expire), re-host them on your own storage (S3, GCS), then pass the new public URLs into Pylon's attachment_urls array when creating issues. Inline images in HTML descriptions also need to be parsed, downloaded, re-hosted, and URL-rewritten.
How do I prevent customer notifications during a Freshdesk to Pylon import?
Create historical Pylon issues with destination_metadata.destination set to 'internal'. This prevents Pylon from sending email or Slack notifications to customers for every imported ticket. Patch workflow data like status and tags after validation.