



Have any questions? We’re here to help You
Yes, Makini supports both cloud-based and on-premises systems. For on-premises installations, connections require double the connection credits compared to cloud systems. The connection process typically requires opening specific ports and whitelisting Makini's IP addresses in your firewall configuration. For some on-premises systems, VPN tunnels may be necessary. We provide detailed technical requirements during implementation planning. In cases where security policies prohibit external connections, we offer self-hosted deployment options where Makini runs entirely within your infrastructure, eliminating the need for external network access to on-premises systems.
Makini Flows is our embedded workflow automation platform, built on n8n, which we consider the best workflow automation tool available. It's fully integrated into Makini and runs on our infrastructure. Flows allows you to build complex integration logic using a visual workflow builder—no code required, though code is supported for advanced use cases. Workflows can be triggered by schedules, webhooks, API calls, or events from connected systems. You can perform data transformations, implement conditional logic, call external APIs, and orchestrate multi-step processes. Flows includes over 1,000 pre-built connectors beyond Makini's industrial systems, enabling integrations with databases, messaging platforms, cloud services, and more. Most customer activations are completed using Flows due to its flexibility and ease of use.
Makini's API supports date filtering on most endpoints using query parameters. You can filter by creation date, modification date, or entity-specific date fields like order date or delivery date. Common patterns include `modified_after=2024-01-01` to retrieve records updated since a specific date, or relative timestamps like `modified_after=2024-01-01T00:00:00Z`. For optimal performance, use incremental data retrieval patterns rather than repeatedly fetching all records. The sync status endpoint provides the last sync timestamp, which you can use as the `modified_after` value for your next query. This approach minimizes data transfer and API load while ensuring you capture all changes.
Based on our market data, building industrial integrations in-house typically costs $50,000-$150,000+ per integration and takes 2-24 months depending on complexity. Maintenance requires dedicated resources—roughly one full-time person per three integrations. Makini transforms these economics: integrations go live in 1-6 weeks, costs are predictable OPEX rather than large upfront CAPEX, and maintenance is included. You gain access to 2,000+ integrations instead of building them one at a time. Our team has six years of specialization in industrial integrations, meaning we've solved problems you haven't encountered yet. For product companies, Makini allows faster time to market and frees engineering resources to focus on your core product rather than building and maintaining integration infrastructure.
