Limitations
Overview
This page documents known limitations of the Build a Doc connector to help you plan your implementations.
Document Size Limits
| Type | Limit |
|---|---|
| Maximum input file size | Plan-dependent |
| Maximum output file size | Plan-dependent |
| Maximum combined data source size | Plan-dependent |
Format Limitations
Word Documents
- Password-protected documents not supported
- Some advanced formatting may not convert perfectly to all output formats
- Macros are not executed during conversion
Excel Documents
- Password-protected documents not supported
- Encrypted workbooks not supported
PowerPoint Documents
- Embedded videos are not converted
- Some animations may not render in PDF output
- Audio content is excluded from conversions
PDF Documents
- Encrypted PDFs require password for processing
- Digital signatures may be affected by modifications
Template Limitations
Expression Complexity
- Very deeply nested expressions may timeout
- Extremely large loops may impact performance
Data Binding
- Maximum data source count per action
- Maximum data source count per action
- Very deeply nested data structures may impact performance or cause timeouts; test templates with representative data and simplify deeply nested structures where possible.
- Array size limits for loop operations
API Limitations
Rate Limits
| Plan | Requests per Minute |
|---|---|
| Free | Limited |
| Standard | Plan-dependent |
| Enterprise | Custom |
Quota Limits
| Plan | Document Actions/Month | Utility Actions/Month |
|---|---|---|
| Free | 50 | 50 |
| Standard | 500 | 5000 |
| Midsize | 1200 | 10000 |
| Large | 5000 | 100000 |
Connection Limitations
- Connections are user and tenant-specific
- Cannot share connection credentials across tenants
- Connection names must be unique per user
Workarounds
Large Documents
- Split into smaller sections
- Process in batches
- Use asynchronous patterns for very large jobs
Complex Templates
- Pre-compute complex calculations in data source
- Simplify template logic where possible
- Split into multiple templates if needed
High Volume
- Implement queuing for high-volume scenarios
- Distribute processing across time
- Consider enterprise plan for higher limits