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Makini uses cursor-based pagination for retrieving large datasets. API responses include a `next_cursor` field when additional results are available. To retrieve the next page, include the cursor value in your next request: `GET /api/v1/purchase-orders?cursor=CURSOR_VALUE`. Cursor-based pagination is more reliable than offset-based pagination because it handles data changes between requests—if records are added or deleted while you're paginating, you won't miss records or see duplicates. Page size is configurable up to a maximum limit (typically 100-500 records per page depending on entity type). For optimal performance, use the largest page size your application can handle efficiently. The API response also includes total count when available from the source system.
Makini takes a defense-in-depth approach to security. All data in transit uses TLS 1.2 or higher. Data at rest is encrypted using AES-256 encryption. Customer credentials are encrypted using secure key management with separate encryption keys per customer. We implement network segmentation, strict access controls, and follow the principle of least privilege. Our infrastructure undergoes regular security audits, penetration testing, and vulnerability assessments. We're SOC 2 Type 2 certified, demonstrating our commitment to security controls. Employee access to production systems is logged and monitored. For customers with strict compliance requirements, we offer self-hosted deployments where data never leaves your infrastructure, eliminating Makini as a data processor.
Our standard SLA targets 99.9% uptime for cloud deployments, which translates to less than 9 hours of downtime per year. For enterprise customers with critical integration requirements, we offer enhanced SLAs up to 99.99% through multi-region redundancy and dedicated infrastructure. SLAs cover the Makini platform itself—availability of connected third-party systems is outside our control, though we monitor their health and alert you to issues. For self-hosted deployments, uptime depends on your infrastructure configuration, and we provide architecture guidance to help you achieve your availability targets. We maintain a public status page showing real-time system health and incident history.
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.
