Knodegraph is a knowledge graph builder that lets you upload documents and automatically extract entities and relationships using AI. You can also build graphs manually with a drag-and-drop editor.

How much does Knodegraph cost?

Knodegraph has a free tier with 3 graphs and 100 nodes per graph. The Pro plan is $14.99/month and includes unlimited graphs, 50,000 nodes, NLP extraction, multilingual support, and API access.

What languages does Knodegraph support?

Knodegraph supports over 100 languages for entity extraction, including English, Arabic (with full RTL support), French, Spanish, German, Chinese, Japanese, and many more.

Is my data secure on Knodegraph?

Yes. Knodegraph uses per-user data isolation, meaning your knowledge graphs are completely separate from other users. All data is stored on secure, self-hosted infrastructure with encrypted connections.

How does AI extraction work?

Upload any document and Knodegraph uses Claude AI to identify entities (people, organizations, locations, concepts) and the relationships between them. Extracted data is staged for your review before being added to your graph - you always have final control.

Can I export my knowledge graphs?

Yes. Free users can export to PNG, SVG, JSON, and CSV. Pro users additionally get access to JSON-LD, RDF, and Neo4j export formats.

KnodeGraph vs InfraNodus (2026)

Verdict

Choose InfraNodus to analyze a text as a network — surface its main topical clusters, measure discourse structure, and find the structural gaps that suggest new ideas. Choose KnodeGraph to build a curated knowledge graph of typed entities and labeled relationships extracted from documents. One analyzes language statistically; the other models the world the documents describe.

Last updated: June 10, 2026

At a Glance

KnodeGraph

Best for: Turning document collections into typed, editable knowledge graphs with AI extraction

Pricing: Free $0/mo (3 graphs, 100 nodes, 5 AI doc extractions/mo); Pro $14.99/mo (unlimited graphs, 50K nodes, 50 extractions/mo, API)

https://knodegraph.com/

InfraNodus

Best for: Text-network analysis: topic clusters, discourse structure, and content-gap discovery

Pricing: Cloud plans roughly $9–$29/mo depending on tier (verify current pricing on infranodus.com); open-source edition available for self-hosting

https://infranodus.com/

KnodeGraph vs InfraNodus: Side-by-Side

	KnodeGraph	InfraNodus
Graph model	Typed entities + labeled relationships (knowledge graph)	Words as nodes, co-occurrence as edges (text network)
What an edge means	A semantic relation (works-at, founded-by)	Two words appearing near each other
Core strength	Building a curated, queryable model of a document corpus	Revealing topic clusters and structural gaps in a discourse
AI role	Claude extracts entities/relations; human reviews each	AI generates insights, questions, and ideas from the network
Human review workflow	Staging area: approve, edit, merge, reject	Analysis is instant; no curation step
Graph editing	Full visual canvas editor with undo/redo	Analysis-oriented; limited manual graph construction
Network analytics	Visual exploration, filtering, clustering views	Built-in discourse metrics, modularity, gap detection
Self-hosting	Not available (managed SaaS)	Open-source edition can be self-hosted
Export	PNG, SVG, JSON, CSV	Graph and data exports (formats vary by plan)
Pricing	$0 free tier; $14.99/mo Pro	Roughly $9–$29/mo cloud tiers; open-source self-host

Statistical Networks vs Semantic Graphs

InfraNodus, built by Nodus Labs, treats any text as a network: words become nodes, and words that occur near each other get connected. Community-detection algorithms then reveal the topical clusters in the discourse, and the tool's signature move is finding 'structural gaps' — clusters that are poorly connected — because bridging them is where new ideas tend to live. An AI layer generates questions and insights from that structure.

KnodeGraph builds a different artifact entirely. Claude AI reads your documents and proposes typed entities — people, organizations, locations, concepts — and labeled relationships between them. You review each extraction before it is committed. The output is not a map of word usage but a model of the things your documents describe and how they relate.

The deepest difference is what an edge means: in InfraNodus, an edge says two words co-occur; in KnodeGraph, an edge says a specific relationship holds between two entities.

Where InfraNodus Wins

For discourse analysis, InfraNodus is the sharper instrument. If your question is 'what is this text actually about, what are its dominant clusters, and what is it not saying?', its network metrics answer in seconds — no review workflow, no curation, just paste text and read the structure.

Its gap-detection and AI ideation make it genuinely useful for writers, content strategists, and researchers hunting for the under-explored angle: the tool points at the disconnect between topic clusters and suggests ideas to bridge them. KnodeGraph has no equivalent discourse-gap analytics.

InfraNodus also has an open-source edition you can self-host, and its cloud entry price (around $9/mo) undercuts KnodeGraph Pro. For quick, exploratory text analysis on a budget, it wins.

Where KnodeGraph Wins

Word co-occurrence networks blur distinctions a knowledge graph preserves. InfraNodus cannot tell you that 'Anthropic' is an organization, 'Dario Amodei' is a person, and the relationship between them is led-by — it can only show the words travel together. KnodeGraph's typed entities and labeled relationships make the graph filterable, traversable, and meaningful as data.

Curation is the second advantage. Every AI extraction in KnodeGraph passes through human review — approve, edit, merge, or reject — so the final graph is something you can stand behind and export (JSON, CSV, PNG, SVG) into downstream analysis. Extraction works across 100+ languages including Arabic RTL, and Pro includes a REST API.

KnodeGraph also scales as a workspace: multi-document projects up to 50,000 nodes per graph, with a visual editor for ongoing maintenance rather than one-off analysis.

Pricing Compared

InfraNodus cloud plans run roughly $9–$29 per month depending on tier — check infranodus.com for current details — and the open-source edition can be self-hosted for free if you are comfortable running it yourself.

KnodeGraph offers a $0 free tier (3 graphs, 100 nodes, 5 AI extractions/month, no credit card) and Pro at $14.99/mo (unlimited graphs, 50K nodes per graph, 50 extractions/month, templates, API). The entry costs are close enough that price should not be the deciding factor; choose on which kind of graph you actually need.

Which Should You Choose?

Choose InfraNodus when the text itself is the object of study — analyzing a discourse, auditing content coverage, or hunting for the gap your next piece should fill.

Choose KnodeGraph when the world described by the text is the object of study — who did what, which organizations connect to which, what the entity landscape of a corpus looks like.

They are complementary rather than rivals: run InfraNodus to understand a discourse's shape and spot the missing angle, then build the curated entity graph of the underlying facts in KnodeGraph. Teams doing serious literature reviews often want both views of the same corpus.

Frequently Asked Questions

Is InfraNodus a knowledge graph tool?

Not in the typed-entity sense. InfraNodus builds text networks — words connected by co-occurrence — which are powerful for discourse analysis but different from a knowledge graph with typed entities (Person, Organization) and labeled relationships (works-at). KnodeGraph builds the latter.

Does KnodeGraph do content-gap analysis like InfraNodus?

No. KnodeGraph offers visual exploration, filtering, and clustering views of your entity graph, but it does not compute discourse-structure metrics or detect structural gaps between topic clusters. That analysis is InfraNodus's specialty.

Which is better for analyzing research papers?

It depends on the question. To map the people, methods, institutions, and findings across many papers as a structured graph, use KnodeGraph. To analyze the discourse of the papers — dominant themes and what's missing — use InfraNodus.

Can I self-host either tool?

InfraNodus has an open-source edition you can self-host. KnodeGraph is a managed SaaS only — there is no self-hosted version.

How do the prices compare?

InfraNodus cloud tiers run roughly $9–$29/mo (verify current pricing on their site). KnodeGraph is $0 on the free tier and $14.99/mo for Pro with AI extraction, 50K-node graphs, and API access included.

Ready to Build Your Knowledge Graph?

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