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Connection credits are Makini's billing unit. Each system integration consumes a specific number of credits based on complexity. Systems are divided into three tiers: Tier 1 (simple systems like cloud CMMS), Tier 2 (mid-complexity ERP systems), and Tier 3 (complex systems like SAP). On-premises installations require double the credits of their cloud equivalents. For example, a cloud SAP S4/HANA connection might use 4 credits, while an on-premises SAP ECC installation uses 8 credits. Connection credits are consumed when you establish a connection and are returned to your pool when you disconnect. This allows flexible allocation across customers—you're not locked into specific connections.
Yes, Makini provides extensive customization options for field mappings. Through the connection settings interface, you can view how each system's fields map to Makini's unified model. You can remap fields, add custom field mappings, or create entirely new custom fields that will appear in API responses. These customizations are connection-specific, allowing different mapping configurations for different customers. Mapping changes take effect immediately without requiring code changes. For standardized workflows, default mappings typically provide sufficient coverage. Custom mappings are most useful when integrating with heavily customized systems or when you need fields beyond the standard unified model.
Makini provides sandbox connections for testing without affecting production systems. Sandbox connections include sample data representing common scenarios: standard purchase orders, orders with custom fields, orders in various states (draft, approved, completed), and error cases like invalid vendors or out-of-stock items. Sandbox data is read-only for safety—write operations return success responses without modifying data. This allows thorough testing of your integration logic without risk. For testing with specific systems, we recommend using dedicated test instances of the actual systems (like SAP sandbox environments) connected through Makini, which provides the most realistic testing experience.
Makini provides several performance monitoring capabilities. API responses include timing information showing request processing time. The dashboard includes performance metrics showing average response times, throughput, and error rates over time. You can set up alerts for performance degradation or error rate increases. Each request generates a unique request ID that enables detailed performance analysis. For workflow-based integrations, execution logs show per-step timing, helping identify bottlenecks. We recommend implementing client-side monitoring to track end-to-end latency including network time. Monitor trends over time rather than individual requests—occasional slow requests are normal, but sustained increases may indicate issues requiring investigation.
