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Integration timelines vary by complexity. For standard implementations with no customizations, connections can be live within 1-2 weeks. This includes authentication setup and basic workflow configuration. For implementations requiring custom workflows or specific business logic, timelines typically range from 2-6 weeks depending on the scope. Complex enterprise deployments with multiple systems and custom requirements may take 6-10 weeks. These timelines are significantly shorter than traditional integration projects, which often take 2-24 months.
Makini uses standard HTTP status codes and structured error responses. Error responses include an error code (e.g., `AUTHENTICATION_FAILED`, `RATE_LIMIT_EXCEEDED`), error type for categorization, a human-readable error message, and a unique request ID for support inquiries. Common status codes include 400 for invalid requests, 401 for authentication failures, 403 for permission issues, 429 for rate limiting, 500 for server errors, and 503 for service unavailability. Use the error code for programmatic error handling rather than parsing error messages. The request ID helps our support team quickly identify and investigate specific issues.
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.
