PLCGateway AI MCP vs Induction
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
PLCGateway AI MCP compared with Induction
| Detail | 
PLCGateway AI MCP | 
Induction |
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| What it does | MCP server for industrial software that lets AI assistants connect machines, build screens and analyze production data
| Agent optimization layer that automatically optimizes agent workloads to extend context windows and reduce costs
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| Stage | Not given
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| Founded | Not given
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| Based in | TR
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| Pricing model | Not given
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| Key features | - Connect machines and create devices from plain language instructions
- Build process screens and report dashboards that engineers review before deployment
- Define production assets and calculate OEE for discrete, continuous or batch processes
- Query alarm statistics and production data in natural language
- Support MCP protocol over network HTTP or local stdio connections
- Works with any MCP-compatible AI client including desktop and self-hosted models
- Role-based access control matching viewer, operator and administrator permissions
- Audit logging of every write operation with user identification
- Acknowledge alarms and record batch results within user permissions
- Generate Python code examples for REST API integration
| - Automatically removes redundant and stale context from agent workflows in real time
- Reduces token usage by 37–67% across agent workloads
- Maintains baseline pass rates and success rates without code changes
- Extends context windows for longer-running workflows
- Zero code changes required for integration
- Continually monitors and learns agent workloads
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| What makes it different | - Built-in MCP server in every product, no separate AI add-on or cloud service needed
- Production data remains on-premise; nothing sent to cloud unless user's chosen AI model requires it
- Drafts protect production: AI-created screens and dashboards reviewed before going live
- Same access controls for AI as for people: roles, rights and logging
- Assists in user's own language and explains PLCGateway features through built-in help
| - Reduces agent inference costs by 37–67% while maintaining the same success rates
- Works transparently with zero code changes required
- Focuses optimization on removing wasted tokens while keeping success rates high
- Enables teams to take on more ambitious problems with the same resources
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| Free plan | Not given
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| Open source | Not given
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| Platforms | Not given
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| Public API | Not given
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | Not checked yet ● no change since last check | 12 ● no change since last check |
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| Trust Flow (Majestic) | 0 | 8 |
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| Citation Flow (Majestic) | 0 | 29 |
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| Referring domains (Majestic) | 0 | 42 |
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3 details filled in for both companies.
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