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Last updated: September 26, 2026. This guide was fully refreshed in September 2026 — the comparison table, methodology, and DB-Engines ranking notes below reflect the current graph DBMS landscape as of this month.
What this page covers
Graph databases (also called graph DBMS) store and query data as nodes and relationships instead of tables and joins, which makes them a strong fit for fraud detection, recommendation engines, knowledge graphs, network/IT operations, and master data management. This page gives you three things:
- A side-by-side comparison table of the leading graph databases — query language, data model, license, deployment, and best-fit use cases.
- A plain-language methodology section explaining how and when we built this comparison.
- An explanation of the DB-Engines Graph DBMS ranking — what it measures, where to find the current standings, and how to view past versions of the ranking on web.archive.org.
We deliberately do not reproduce DB-Engines popularity scores on this page. Rankings like that change monthly, so we link to the live source instead of quoting numbers that go stale.
Methodology (September 2026 refresh)
Here is exactly how this comparison was put together for the September 26, 2026 update:
- Selection: we included the graph DBMS platforms most commonly evaluated by engineering teams and most visible in database market coverage: Amazon Neptune, ArangoDB, Dgraph, JanusGraph, Memgraph, Microsoft Azure Cosmos DB (Gremlin API), Neo4j, Ontotext GraphDB, Stardog, and TigerGraph.
- Criteria: for each database we recorded its data model (labeled property graph, RDF triple store, or multi-model), its primary query language(s), licensing and pricing model, deployment options (self-hosted, managed cloud, or both), and the workload it is best suited for.
- Sources: vendor documentation and product pages for product facts, and the DB-Engines Graph DBMS ranking for market context. We link to DB-Engines rather than copying its scores.
- Ordering: the table is alphabetical. It is a comparison, not a popularity ranking — for current popularity standings, see the DB-Engines ranking linked above.
- Currency: licensing and product details were re-checked against vendor pages at the time of this update. Product details change; verify against vendor documentation before committing to a platform.
Graph database comparison table (September 2026)
| Database | Data model | Query language(s) | License / pricing | Deployment | Best suited for |
|---|---|---|---|---|---|
| Amazon Neptune | Property graph + RDF | Gremlin, openCypher, SPARQL | Proprietary (AWS, pay-per-use) | AWS-managed only | Teams already running on AWS who want graph and RDF support in one service |
| ArangoDB | Multi-model (graph + document + key-value) | AQL | Apache 2.0 through 3.11; newer releases source-available (BUSL) | Self-hosted + ArangoGraph managed cloud | Applications that want graph queries alongside documents without a second database |
| Dgraph | Property graph | GraphQL, DQL | Open source (Apache 2.0) | Self-hosted + managed cloud | GraphQL-native backends and APIs-first teams |
| JanusGraph | Property graph | Gremlin | Open source (Apache 2.0) | Self-hosted (pluggable storage backends such as Cassandra or HBase) | Distributed graphs that outgrow a single machine |
| Memgraph | Property graph (in-memory) | Cypher (openCypher-based) | Source-available (BSL), free Community tier | Self-hosted + Memgraph Cloud | Real-time streaming analytics and low-latency traversal workloads |
| Microsoft Azure Cosmos DB (Gremlin API) | Property graph | Gremlin | Proprietary (Azure, pay-per-use) | Azure-managed only | Teams standardized on Azure who need globally distributed graph storage |
| Neo4j | Property graph | Cypher | Open source Community Edition (GPLv3); commercial tiers | Self-hosted + AuraDB managed cloud | General-purpose graph applications with a large ecosystem of drivers, tooling, and learning material |
| Ontotext GraphDB | RDF triple store | SPARQL | Proprietary; free tier available | Self-hosted + managed SaaS | Semantic knowledge graphs, linked data, and standards-heavy publishing workflows |
| Stardog | RDF + property graph | SPARQL, Gremlin | Proprietary; free tier available | Self-hosted + Stardog Cloud | Enterprise knowledge graphs with data virtualization needs |
| TigerGraph | Property graph | GSQL | Proprietary; free tier available | Self-hosted + TigerGraph Cloud | Massively parallel deep-link analytics on very large graphs |
Table is ordered alphabetically, not by popularity or rank. Product and licensing details can change — confirm against vendor documentation before deciding. For current popularity standings across graph DBMS, see the DB-Engines Graph DBMS ranking.
Property graph vs. RDF: the two families of graph database
The DB-Engines Graph DBMS category mixes two technically different families, so it helps to know which one you need before comparing products:
- Labeled property graphs (LPG) store nodes and relationships that both carry key-value attributes. Query languages in this family include Cypher (declarative pattern matching, originated at Neo4j and standardized through openCypher), Gremlin (the graph traversal language of Apache TinkerPop), and GSQL (TigerGraph's SQL-inspired language). Neo4j, Memgraph, JanusGraph, and TigerGraph work this way.
- RDF triple stores store data as subject–predicate–object triples and are queried with SPARQL, a W3C standard. This model dominates semantic web, linked-data, and enterprise knowledge graph work. Ontotext GraphDB and Stardog are the best-known examples, and Amazon Neptune serves both families.
- Multi-model databases such as ArangoDB treat the graph as one of several access paths over the same data, which can simplify architecture when you need document and graph queries over shared data.
The DB-Engines Graph DBMS ranking, explained
Many people arrive at this page while researching the DB-Engines Graph DBMS ranking — the monthly popularity ranking of graph databases published at db-engines.com/en/ranking/graph+dbms. Here is what it is and how to use it responsibly:
- What it measures: DB-Engines computes a popularity score for each DBMS from a set of public signals — search interest, technical discussions, job offerings, and professional profile mentions — and publishes the standings monthly. It measures interest and adoption signals, not technical performance.
- It is not a benchmark: a higher DB-Engines score does not mean a database is faster or more capable for your workload. Use the ranking to gauge community and market momentum; use your own benchmark (against your data and query patterns) to gauge fit.
- Check the live page for current standings: because the scores change every month, any specific numbers quoted elsewhere on the web — including in older articles — can be outdated. The live page is the authoritative source.
How to view historical DB-Engines graph rankings on web.archive.org
Because the ranking is updated monthly, researchers comparing a specific year's standings often use archived snapshots:
- Go to web.archive.org (the Wayback Machine).
- Paste
https://db-engines.com/en/ranking/graph+dbmsinto the search box and press Enter. - The calendar view shows every archived snapshot. Pick the year and month you care about (for example a 2024 snapshot to see where graph DBMS stood in 2024).
- Compare that snapshot against the current live ranking to see how the landscape moved.
This is the most reliable way to see DB-Engines graph standings for a past period without relying on third-party articles that quote old scores.
How to choose a graph database in 2026
- Start with the data model: if your domain is naturally relational-with-attributes (customers, transactions, networks), start with property graph products; if you need ontologies, taxonomies, or linked open data, start with RDF triple stores.
- Pick the query language your team can staff for: Cypher and openCypher knowledge is the most widely available; Gremlin matters in Apache TinkerPop and multi-cloud contexts; SPARQL is the standard for semantic work.
- Decide operating model early: Neptune and Cosmos DB are managed-only and lock you to their cloud; Neo4j, Memgraph, TigerGraph, and Ontotext GraphDB give you both self-hosted and managed options; ArangoDB and Dgraph can run anywhere.
- Check licensing: pure open source (Apache 2.0: JanusGraph, Dgraph; GPLv3: Neo4j Community), source-available (BSL/BUSL: Memgraph, newer ArangoDB), or proprietary with free tiers (TigerGraph, Ontotext GraphDB, Stardog) — the right answer depends on your compliance posture.
- Match scale to architecture: in-memory single-node engines (Memgraph) optimize latency; distributed engines (JanusGraph, TigerGraph) optimize sheer graph size; managed services shift the scaling problem to the provider.
Frequently asked questions
Which graph database is the most popular right now?
Popularity shifts month to month, so we do not quote specific standings here. Check the live DB-Engines Graph DBMS ranking for the current month's scores.
What is the difference between Cypher, Gremlin, and SPARQL?
Cypher is a declarative graph query language focused on readable pattern matching; Gremlin is a traversal language in the Apache TinkerPop ecosystem suited to programmatic, step-by-step queries; SPARQL is the W3C standard for querying RDF data and is required (rather than optional) when your data model is a triple store.
Is the DB-Engines ranking the same as a performance benchmark?
No. DB-Engines measures popularity signals such as search interest, technical discussions, job offerings, and professional profiles. It tells you about mindshare and adoption, not how fast a system will run your queries.
Questions about a specific platform in the table? Re-check the vendor's documentation for the latest licensing and deployment details — this comparison was last verified in September 2026.