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Makini is a unified API platform for industrial systems integration. We provide connectivity to over 2,000 ERP, CMMS, and WMS systems through a single, standardized API. Instead of building separate integrations for each system, you connect once to Makini and gain access to all supported platforms. This approach transforms integration projects that typically cost tens of thousands of dollars and take months into a manageable operational expense with deployment times of 1-2 weeks.
API tokens must be stored securely and should never be exposed on the client side or in public repositories. Store tokens in secure environment variables or dedicated secrets management systems like AWS Secrets Manager, HashiCorp Vault, or Azure Key Vault. Never hardcode tokens in application code or commit them to version control. Implement proper access controls so only authorized services can access stored tokens. For production environments, use separate tokens from development/testing environments. Rotate tokens periodically and immediately revoke tokens if you suspect they've been compromised. Makini tokens provide access to customer data, so treat them with the same security standards you'd apply to database credentials.
The `RATE_LIMIT_EXCEEDED` error indicates you've exceeded the API rate limit for the connection or account. Rate limits are typically set per connection and per time window (usually per minute). When you hit a rate limit, the response includes a `Retry-After` header indicating when you can retry the request. Implement exponential backoff in your retry logic to avoid immediately hitting the limit again. If you consistently hit rate limits, review your API usage patterns—you may be making unnecessary requests, polling too frequently, or could benefit from webhook-based synchronization. For legitimate high-volume needs, contact us to discuss increasing your rate limits.
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.
