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myICOR vs Scalable AI Management Platform

Bottom line

Scalable AI Management Platform is open source and myICOR is not.

Which one should you try?

myICOR

Freemium

Best for Users seeking a local, vendor-independent PKM system

Visit myICOR See full review

Scalable AI Management Platform

Freemium

Best for Companies requiring GDPR-compliant AI chat

Visit Scalable AI Management Platform See full review

Pricing, capabilities and platforms side by side. Prices are as of the "Pricing checked" date in the table.

myICOR

myICOR

The system is a local markdown folder pre-loaded with a six-person AI team: a routing orchestrator (Larry), a research specialist (Pax), a capture agent (Penn), and others — each with a named contract and a session journal so the next model picks up where the last one left off. You bring your own LLM; the folder supplies the memory. Research produces structured notes in place, drafts inherit your established voice, and weekly review prompts surface stale items automatically. The ceiling appears when you need real-time data, API integrations, or collaborative editing — none of that is in the folder. Teams that need those reach for purpose-built tools alongside this one.

Scalable AI Management Platform

Scalable AI Management Platform

Built by metadist data management GmbH and fully open-source, Synaplan runs on your own infrastructure, routes queries across OpenAI, Claude, Gemini, and local Ollama models, and tracks every token and its cost in one place. The RAG pipeline ingests PDFs, crawled web pages, and structured text, then surfaces answers through an embeddable chat widget — no custom coding required for the widget itself. Persistent memories let the model retain tone, preferences, and brand voice across sessions, which matters when your support agent needs to sound like the same person on every call. The visual DAG routing layer handles which model answers which query, though teams with complex conditional branching will find that abstraction has a ceiling.

AttributemyICORScalable AI Management Platform
PricingFreemiumFreemium
Price—€19.95/month
Pricing checkedNot checked yetNot checked yet
Free trialNot statedNot stated
Open sourceNoYes
Has APINoYes
Self-hosted optionYesYes
PlatformsLocal disk (any OS with markdown support)Docker, Web
Pros
  • LLM-agnostic folder architecture, so switching from Claude to Gemini mid-project is a matter of opening the same folder in a different app — no re-pasting context, no lost session history.
  • Persistent agent journals mean each specialist picks up from the last session, so you stop spending the first ten minutes of every AI conversation re-explaining who you are and what you're working on.
  • Plain markdown on your local disk means zero migration risk — if the vendor disappears tomorrow, every note, contract, and workflow you built is still readable by any text editor or LLM.
  • Larry's routing layer matches requests to the right specialist automatically, so you don't have to remember which prompt style triggers good research versus good drafting — the team handles the handoff.
  • Open-source scaffold under CC BY-NC-SA 4.0, so you can inspect, fork, and extend the agent contracts without waiting on a vendor roadmap or paying for access to the base system.
  • Self-hosted deployment with full data sovereignty, so your legal and compliance teams can sign off without carving out exceptions for a US-hosted SaaS.
  • Provider-agnostic model routing across OpenAI, Claude, Gemini, and local Ollama instances, which means switching to a cheaper or on-premises model when API costs spike is a configuration change, not a re-architecture.
  • Per-token cost tracking across all connected models, so you know exactly where your AI budget is going before the invoice arrives rather than after.
  • RAG pipeline that ingests PDFs, crawled pages, and structured text and surfaces answers through an embeddable widget — teams add AI support to a website without writing a backend.
  • Persistent categorized memory system that retains tone, preferences, and context across sessions, so a branded support agent does not sound like a different person every time a customer returns.
Cons
  • The folder has no mechanism for live data: API calls, web scraping, calendar reads, and CRM syncs are all outside its scope. Teams that need agents to pull live information must wire up a separate integration layer and maintain it alongside the folder — which is a second system to debug.
  • There is no multi-user collaboration model. Two people cannot edit the same folder simultaneously with conflict resolution. Teams of more than one person sharing a PKM workspace hit this wall immediately and typically move the shared layer to a tool with real-time sync — Notion, Obsidian Sync, or a shared Git repo — while keeping individual folders local.
  • No hosted inference or built-in LLM access means every new user must already have API credentials or a local model running before the team scaffold does anything. For non-technical users who came for the AI workflows, the setup friction before first use is real and the docs leave meaningful configuration detail to the user to figure out.
  • The agent team is fixed at the scaffold level — expanding it requires running Nolan's eight-step hiring procedure, which is a prompt-driven workflow inside the folder. Teams used to GUI-based agent builders who want to add a specialist in two clicks will find the process slower and more text-heavy than competing tools that offer visual agent creation.
  • The visual DAG routing layer handles straightforward query-to-source mappings cleanly, but branching logic beyond three or four conditions hits the canvas's expressive limit; teams that need programmatic control over routing end up scripting around the UI, which means they are maintaining two systems and the visual layer stops earning its place.
  • MCP server support and CRM/ERP integration for the widget are described as basic or paid-only features respectively, so teams that need deep integration with existing enterprise systems on day one will find the out-of-the-box surface area narrower than expected — at which point Dify or a custom LangChain setup with a hosted vector store becomes the more direct path.
  • The release cadence visible in the public GitHub log shows active development with back-to-back bug-fix releases, which signals a maturing but not yet stable platform; teams running customer-facing production widgets need to account for regression testing on each update rather than treating upgrades as routine.

Made up your mind?

myICOR

Freemium

Best for Users seeking a local, vendor-independent PKM system

Visit myICOR See full review

Scalable AI Management Platform

Freemium

Best for Companies requiring GDPR-compliant AI chat

Visit Scalable AI Management Platform See full review

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