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.

Concepts 10 min read

Knowledge Graph Database Comparison: Neo4j, FalkorDB, Neptune, and More

Published 2026-04-30

How to read this comparison

Graph database benchmarks are notorious for being either vendor-funded or wildly outdated, so this article does not try to award a single winner. Instead, it picks the dimensions that matter when you are choosing for a real knowledge graph project: query language, scaling model, write throughput, hosting options, licensing, and the size of the human ecosystem around the product. Each of the six platforms below has a use case where it is the obvious choice and several where it is the wrong one.

The reference benchmark worth knowing is the LDBC Social Network Benchmark (SNB), maintained by the Linked Data Benchmark Council since 2014. SNB defines two workloads: an interactive workload that simulates a social network's read traffic, and a business intelligence workload that simulates analytical queries over a large connected graph. Most serious graph vendors publish SNB numbers, which makes cross-platform comparison plausible — though never apples-to-apples, because data sets and tuning differ.

Version numbers in this article are accurate as of April 2026. The graph database market moves quickly enough that anything in here may be obsolete in six months; check the vendor's release notes before committing.

Neo4j 5.27 — the incumbent

Neo4j is the dominant property-graph database. Founded in 2007 and now in its 5.x release line (5.27 LTS as of Q1 2026), it speaks Cypher natively and was the language's original home. It is the database most graph engineers learned first, which means the tooling, documentation, and community are unmatched.

Strengths: rich Cypher implementation, mature graph algorithms library (GDS), best-in-class visualisation through Neo4j Bloom, robust security including label-based access control, and Aura — Neo4j's hosted offering — which removes most operational burden. The Community Edition is GPLv3 and is genuinely usable; the Enterprise Edition under a commercial licence adds clustering, fine-grained auth, and online backup.

Trade-offs: clustering is read-replica-based until Neo4j 5's Fabric and clustering improvements, and write-throughput scales vertically more than horizontally. Pricing for Enterprise is per-instance and adds up fast in production. Knowledge graph workloads with lots of writes from concurrent extraction pipelines can hit ceilings sooner than expected.

Neo4j is the right choice when budget allows, when you need the deepest tooling ecosystem, and when single-region operation is acceptable. It is the wrong choice when costs need to stay close to zero or when very high write concurrency is the dominant workload.

FalkorDB 4.0 — the modern Redis-native option

FalkorDB is a property-graph database built as a Redis module, forked from RedisGraph (which Redis Labs deprecated in 2023) and now developed independently by FalkorDB Ltd. The 4.0 series, released in late 2025, introduced sparse-matrix-based query execution that delivers single-digit-millisecond latency for many traversal queries. It speaks Cypher (subset, growing) and ships under the AGPL with a commercial alternative.

Strengths: extraordinary latency for the typical knowledge-graph traversal workload, Redis-style operational simplicity (one binary, well-known protocol, familiar persistence options), low memory overhead, and a Docker-friendly deployment story. The query engine uses GraphBLAS sparse linear algebra under the hood, which is what gives it its speed advantage on traversal-heavy workloads.

Trade-offs: Cypher coverage is narrower than Neo4j's, especially for newer language features and the GDS-equivalent algorithms library. Tooling and documentation are catching up but lag the larger ecosystems. Clustering exists but is less battle-tested than Neo4j's. The company is small.

FalkorDB is the right choice when latency and cost matter more than feature breadth, when the workload is traversal-heavy, and when self-hosting is acceptable. It is the wrong choice when you need every advanced Cypher feature or a long, mature feature checklist. KnodeGraph runs on FalkorDB, which is why we can offer Pro at $14.99/month.

FalkorDB's GraphBLAS-based query engine delivers single-digit-millisecond traversal latency, which is the main reason KnodeGraph can offer Pro at $14.99 a month.

Amazon Neptune — the AWS-native choice

Amazon Neptune is the AWS managed graph database, supporting both RDF (with SPARQL 1.1) and property graphs (with Gremlin and OpenCypher). The 1.3.x release line, current in early 2026, runs on AWS-managed hardware with automatic backups, point-in-time recovery, and read-replica scaling.

Strengths: zero operational overhead if you are already on AWS, both query languages supported in one cluster, deep IAM and VPC integration, automatic encryption, and the option of Neptune Analytics for in-memory analytical workloads on snapshots. Neptune Serverless (GA since 2023) handles bursty workloads gracefully.

Trade-offs: AWS-only, which is a deal-breaker for any organisation that is not already on AWS or has multi-cloud requirements. Per-hour pricing on the smallest instance still adds up to roughly $250/month before you store any data. OpenCypher support lags Neo4j's Cypher in places, and Neptune does not support all Gremlin features.

Neptune is the right choice for AWS-native organisations that want a managed graph database with both SPARQL and property-graph access. It is the wrong choice for anyone outside the AWS ecosystem or for cost-sensitive small teams.

TigerGraph 4.x — the analytics specialist

TigerGraph is a distributed graph database designed for analytical workloads on very large graphs. It uses GSQL, its own query language, which is more SQL-like than Cypher and supports user-defined functions for in-database analytics. TigerGraph 4.x (current LTS) supports horizontal scaling across many nodes natively.

Strengths: market-leading performance on multi-billion-edge graphs in the LDBC SNB business-intelligence workload, deep analytics with built-in graph algorithms, MPP-style query distribution, and native support for incremental loading at scale. Used in production at major financial-services and telecom firms for fraud detection.

Trade-offs: GSQL is yet another query language to learn, the enterprise pricing model targets large accounts, and the developer experience for small teams is heavier than Neo4j's or FalkorDB's. The free Community Edition has hard limits.

TigerGraph is the right choice for very large analytical knowledge graphs (think 10B+ edges) where horizontal scaling and complex analytics are the dominant requirements. It is the wrong choice for small teams, prototype work, or any project that values portability across query languages.

ArangoDB 3.12 — the multi-model option

ArangoDB is a multi-model database that handles graph, document, and key-value workloads in one engine. Version 3.12 (early 2026) speaks AQL, ArangoDB's own query language, with first-class graph traversal syntax and good performance on mixed workloads. The licensing model shifted in 2024 to BSL (Business Source License), which is permissive for most uses but requires a commercial agreement at scale.

Strengths: one database for graph and JSON workloads, which simplifies architectures where you would otherwise need both Neo4j and MongoDB. Smart Joins for relational-style queries are surprisingly fast. Cluster mode is genuinely horizontal-scaling.

Trade-offs: AQL is yet another language. Pure graph workloads are usually faster on a dedicated graph engine. Some tooling around graph algorithms (community detection, centrality at scale) lags Neo4j's GDS.

ArangoDB is the right choice when you genuinely need both graph and document storage in one place, which is common for product feature stores. It is the wrong choice when graph traversal performance is the only thing that matters.

Stardog 9 — the semantic-web heavyweight

Stardog is the strongest commercial RDF triple store with deep ontology and reasoning support. Version 9 (current as of early 2026) speaks SPARQL 1.1 and supports inference, virtual graphs (federated queries against external relational data), and constraint validation via SHACL.

Strengths: production-grade OWL reasoning, virtual-graph federation that makes it possible to query a graph that spans your triple store and a Postgres database in one SPARQL query, strong ontology tooling, and native R2RML mapping for relational-to-RDF projection. Stardog Cloud removes operational burden.

Trade-offs: commercial licensing is enterprise-priced. SPARQL is a higher learning curve than Cypher. Performance on traversal-heavy workloads usually trails property-graph engines.

Stardog is the right choice when formal reasoning, ontology-driven validation, and federation across heterogeneous data sources are the core requirements — typically in regulated industries (life sciences, finance) where the cost of incorrect inference is high. It is the wrong choice for cost-sensitive teams or projects that just need a graph.

How to choose in practice

Three filters cut the field quickly. First, language: do you want Cypher (Neo4j, FalkorDB, Memgraph, Neptune OpenCypher), SPARQL (Stardog, Neptune RDF, GraphDB), or something else (TigerGraph GSQL, ArangoDB AQL)? Second, hosting: are you AWS-only (Neptune is the path of least resistance), self-host-only (Neo4j Community, FalkorDB), or fine with any managed cloud (most platforms)? Third, scale: are you in the millions of edges (any platform), tens of millions (most), or billions (TigerGraph, scaled-up Neo4j Aura, partitioned Neptune)?

Once those three answers are clear, the remaining decision is usually a tie between two options. At that point, run the LDBC SNB Interactive workload at your expected scale on both, with realistic data and queries from your domain. Vendor-published numbers are useful for sanity checks, but your own workload is the only fair test.

If you do not want to make the call yourself and your data is mostly documents, KnodeGraph is opinionated by design. We pick FalkorDB and a property-graph model so you can be productive in an afternoon, with the option to export to JSON and migrate later if your needs grow.

Frequently Asked Questions

What about Memgraph, JanusGraph, or Dgraph?

Memgraph is a strong Cypher-speaking competitor to Neo4j with an in-memory-first architecture and excellent streaming integration; we left it out of the main list because its positioning overlaps heavily with FalkorDB and Neo4j Aura. JanusGraph is open source and built on Cassandra/HBase, which is great for multi-tenant scaling but operationally complex; it is best for teams already running those backends. Dgraph speaks GraphQL natively and shines for API-first workloads; it has had organisational turbulence over the past few years that has affected community confidence.

Which one is fastest?

There is no single answer — speed depends on workload. For pure traversal latency at small graph sizes (millions of edges), FalkorDB consistently wins on published benchmarks. For analytical workloads at billion-edge scale with heavy aggregations, TigerGraph leads. For mixed read-write workloads with rich queries, Neo4j 5.x is competitive across the board. The honest recommendation is to run the LDBC SNB benchmark on your candidate platforms with your own data shape before committing.

Is the LDBC SNB benchmark trustworthy?

It is the best public benchmark we have for knowledge-graph-shaped workloads. The Linked Data Benchmark Council audits submissions, the data generator and queries are open source, and major vendors publish results with full configuration details. Like all benchmarks, it does not perfectly mirror any specific real workload, so use it for sanity checks rather than as a final selection criterion. The LDBC website (ldbcouncil.org) hosts current results.

Can I migrate between graph databases later?

Mostly yes. Property-graph data exports cleanly to JSON or to Neo4j's import format, which most other property-graph engines can ingest. Cypher portability across Neo4j, FalkorDB, Memgraph, and Neptune (OpenCypher) is reasonable but not perfect — about 90% of typical queries port unchanged. RDF-to-property-graph migrations are lossier. Plan for some query rewriting and ontology adjustment if you ever switch.

Why does KnodeGraph use FalkorDB and not Neo4j?

Three reasons. Latency: FalkorDB's GraphBLAS query engine is hard to beat for the traversal-heavy workloads our users run. Cost: the Redis-module deployment model is dramatically cheaper at our price point ($14.99/mo Pro tier). Operational simplicity: FalkorDB ships as a single Redis-protocol service that fits cleanly into our Docker Compose stack. We considered Neo4j Community Edition seriously and may add it as a deployment target for self-hosted enterprise customers, but FalkorDB has been the better default for the hosted product.

Source

Linked Data Benchmark Council, 'LDBC Social Network Benchmark', specifications and results archive, ldbcouncil.org. Version numbers cited: Neo4j 5.27, FalkorDB 4.0, Amazon Neptune 1.3.x, TigerGraph 4.x, ArangoDB 3.12, Stardog 9, all current April 2026. [link]

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