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Scalable AI Management Platform
Summary
GDPR audits, data residency requirements, and the legal team's veto on US-hosted AI kill most off-the-shelf chat tools before the first sprint ends — Synaplan exists for exactly that wall.
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.
Bottom line: Pick Synaplan when data sovereignty and audit logs are non-negotiable and your routing logic fits a drag-and-drop DAG; plan a different architecture when your branching conditions require programmatic control that the visual layer cannot express.
Hosted & API Pricing
The model is free to self-host. These are the creator's hosted/API options.Pro
Chat widget with Pro features
- CRM/ERP integrations
- Advanced mappings
Pricing may have changed since last verified. Check the official site for current plans.
Pricing Plans
Subscription- Price
- €19.95/month
Free
Core platform and open-source features
- Multi-model routing
- Basic RAG
- Memories
- Self-hosting
Pro
Chat widget with advanced integrations
- CRM/ERP API connections
- Enhanced widget customization
- Priority support
View full pricing on synaplan.com →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- 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
Sign in to edit- 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.
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About
- Platforms
- Docker, Web
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- 2026-07-02T18:30:50.522Z
Best For
Who it's for
- Companies requiring GDPR-compliant AI chat
- Developers wanting self-hosted multi-model orchestration
- Website owners adding AI support without custom coding
- Teams needing audit logs and data sovereignty
What it does well
- Embed knowledge-grounded chat widgets on company websites
- Route queries across cloud and local models with cost tracking
- Build RAG pipelines over PDFs, websites, and structured data
- Maintain persistent memories for consistent team or brand voice
Integrations
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Frequently Asked Questions
- Is Scalable AI Management Platform free?
- Scalable AI Management Platform has a permanent free tier alongside paid upgrades (paid plans from €19.95/month). You can keep using a baseline version indefinitely without paying.
- Is Scalable AI Management Platform open source?
- Yes. Scalable AI Management Platform is open source.
- Does Scalable AI Management Platform have an API?
- Yes. Scalable AI Management Platform exposes a developer API. See the official documentation at https://synaplan.com for details.
- Can I self-host Scalable AI Management Platform?
- Yes. Scalable AI Management Platform supports self-hosting on your own infrastructure.
- What platforms does Scalable AI Management Platform support?
- Scalable AI Management Platform is available on: Docker, Web.
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Synaplan is an open-source AI platform that lets teams connect multiple LLM providers — OpenAI, Claude, Gemini, and local models via Ollama — under a single interface with per-token cost tracking, GDPR-compliant data handling, and self-hosting as a first-class option. The core workflow runs in three layers: a RAG pipeline that ingests PDFs, crawled website URLs, and structured text; a DAG-based routing layer that maps incoming queries to the right model or knowledge source; and a chat widget that embeds on any website without requiring the site owner to write backend code. Audit logs accompany every interaction, satisfying compliance requirements that would otherwise block deployment in regulated European markets.
The memory system is the feature that separates Synaplan from a generic RAG wrapper. Rather than treating each session as stateless, Synaplan captures preferences, tone markers, and factual context as categorized memory nodes, visualized as a neural map that grows with use. The vendor states this enables the model to draft replies that sound consistent with a team’s established voice — useful for support agents or branded chat where session-to-session inconsistency would be noticeable.
Synaplan fits well for European companies that need a widget-based support interface backed by their own document corpus, and for developers who want to experiment with multi-model routing without handing data to a third-party SaaS. CRM and ERP integration for personalized widget answers is a paid-only feature. Where it breaks: the visual DAG routing layer, while accessible, is not a substitute for code when routing logic involves more than a few conditions. Teams that outgrow the canvas typically end up maintaining a parallel scripting layer, which raises the question of why they kept the visual layer at all. MCP server support is described in the v3.6.4 release notes, indicating the platform can act as an MCP host, though the docs describe this as basic at this stage.