Visibility in this ranking can be paid for. Payment moves a vendor's position within the
shortlist; it never adds a vendor, and it never changes a word of the review. The largest vendors in graph databases
cannot hold places 1 to 3. Places 1 to 3 go to challengers, vendors outside the best-known brands in a category,
wherever the category has them; the established names follow from place 4 in their ranked order. How it works: placement disclosure ·
editorial process.
The 12 tools, reviewed
#1 Memgraph
In-memory graph database with Cypher and open Community Edition · United Kingdom · memgraph.com
in-memory Cypher open source
Memgraph is the easiest of the twelve to put a number on. Its pricing page says cost scales with memory and that there are no per-query charges, no compute fees and no charges for replicas or algorithms, so a procurement conversation is about one variable.
- Pricing: Open source + paid tiers. Community Edition is free and open source. Free plan: open source.
- Best for: Real-time graph apps on a clear budget
- Strengths: ACID transactions with on-disk persistence; High-availability replication; Cypher query language.
Where it falls short
The engine is memory-resident, so a graph that outgrows RAM means a bigger instance, and the price follows.
Wrong for
Teams whose graph holds billions of edges on a small budget, or that must run on Azure or Google Cloud with a managed service.
Visit Memgraph →
#2 FalkorDB
Low-latency graph database aimed at GraphRAG and multi-tenant apps · United States · falkordb.com
GraphRAG multi-tenancy managed cloud
FalkorDB is built for the situation many product teams are in: a graph that serves an AI feature or a multi-tenant SaaS product and has to stay cheap while it is small.
- Pricing: Per instance / month. Free plan with community support; Startup from $73 per 1 GB per month, Pro from $350 per 8 GB per month, Enterprise on request. Free plan: free plan.
- Best for: Small teams building GraphRAG and SaaS features
- Strengths: Multi-graph and multi-tenancy; Graph access control; TLS on paid plans.
Where it falls short
The free plan lacks TLS, VPC, backups and any support beyond the community, so it is for prototypes only. Startup has no high availability and no clustering; those arrive at the Pro price, a steep jump.
Wrong for
Enterprises that need long-standing vendor references, regulated-industry attestations or deep partner services.
Visit FalkorDB →
#3 TigerGraph
Distributed graph platform with GSQL, Cypher and hybrid graph and vector search · United States · tigergraph.com
distributed GSQL hybrid vector search
TigerGraph is the right kind of heavy machinery. The Savanna cloud lists a free tier with up to 32 vCPUs and 256 GB RAM per workspace, a paid tier at $45 per GB per month with automatic backups and bring-your-own-cloud options, and a business-critical tier at $126 per GB per month with multi-zone availability.
- Pricing: Usage-based. Savanna starts with a free tier, then lists $45 per GB per month and $126 per GB per month for business critical. Free plan: free plan.
- Best for: Deep-traversal analytics on large graphs
- Strengths: GSQL query language; openCypher and GQL support; Hybrid graph and vector search.
Where it falls short
GSQL is a proprietary language with a learning curve, and the more of your logic lives in it the harder a later move becomes. Per-GB pricing at the higher tiers looks steep for modest graphs.
Wrong for
Teams that want a small, simple graph behind an app and prefer plain Cypher.
Visit TigerGraph →
#4 Neo4j
The best-known graph database, with Cypher and the Aura cloud service · United States · neo4j.com
Cypher managed cloud incumbent
Neo4j is where most people first meet graph databases, and that is its real asset. the learning material (GraphAcademy, a developer center, community forum) is the deepest in the category, and the product line covers a managed service, self-managed Graph Database, Graph Data Science for algorithms and Fleet Manager for running many deployments. AuraDB Free needs no payment method.
- Pricing: Usage-based. AuraDB Free is $0. Free plan: free plan.
- Best for: Teams that value ecosystem and hiring pool
- Strengths: Cypher query language; AuraDB managed service; Graph Data Science.
Where it falls short
Metered cloud rates climb quickly with memory, and the entry price tier sits well above a lighter engine for small graphs. Several valuable capabilities are packaged as separate products, so the bill depends on which you add.
Wrong for
Cost-sensitive teams with small graphs, or buyers that want one SQL-plus-graph store.
Visit Neo4j →
#5 Amazon Neptune
AWS-managed graph database with serverless and analytics options · United States · aws.amazon.com
AWS serverless managed
For a business whose data, identity and networking already sit in AWS, Neptune removes the most work. It is fully managed, offers on-demand instances, a serverless mode that scales capacity measured in NCUs billed per second, and Neptune Analytics for algorithms across billions of relationships. The company markets managed GraphRAG with Bedrock Knowledge Bases and vector search over the graph.
- Pricing: Usage-based. Pay per instance hour, storage, I/O and transfer. Free plan: none.
- Best for: Companies already running everything on AWS
- Strengths: Neptune Database; Neptune Serverless; Neptune Analytics.
Where it falls short
The invoice has five moving parts: instance hours, storage, backup beyond the free allowance, I/O at $0.20 per million requests in its example, and transfer.
Wrong for
Multi-cloud or on-premises teams, and anyone who wants a laptop-first experience.
Visit Amazon Neptune →
#6 Azure Cosmos DB for Apache Gremlin
Gremlin graph API on Microsoft's managed multi-model database · United States · learn.microsoft.com
Azure Gremlin serverless
Cosmos DB's Gremlin API is the quiet default in many Azure estates. It stores vertices and edges as a managed service, speaks Gremlin from the Apache TinkerPop family, and bills through the same request-unit model as the rest of Cosmos DB, with a free tier on a new account of 1,000 RU/s and 25 GB.
- Pricing: Usage-based. Billed in request units with provisioned, autoscale or serverless options, plus storage. Free plan: free plan.
- Best for: Azure shops with modest graph needs
- Strengths: Apache TinkerPop Gremlin; Property graph model; Provisioned throughput.
Where it falls short
Microsoft's introduction page warns that for high-scale scenarios readers should consider Cosmos DB for NoSQL, and for online analytical processing graphs or Gremlin migrations should consider Graph in Microsoft Fabric. Request-unit billing makes traversal cost hard to predict.
Wrong for
Teams with analytic graph workloads, anyone outside Azure, or developers who prefer Cypher.
Visit Azure Cosmos DB for Apache Gremlin →
#7 PuppyGraph
Zero-ETL graph query engine over your existing data stores · United States · puppygraph.com
zero-ETL Gremlin Cypher
PuppyGraph takes a different route from the rest: it does not hold your data. It maps tables in sources you already run onto a graph model and answers Gremlin and Cypher queries against them, so there is no pipeline to build and no second copy to reconcile.
- Pricing: Quote only. Developer Edition is free and single node. Free plan: free plan.
- Best for: Data teams with warehouse or lakehouse data
- Strengths: Zero-ETL graph queries over relational stores; Gremlin and Cypher support; SQL and graph queries together.
Where it falls short
Because it only queries other systems, speed and consistency are bounded by those systems, and write-heavy graph applications are out of scope. The free edition caps data sources at two. Enterprise has no published price.
Wrong for
Product teams that need a transactional graph store behind an application, with writes at low latency.
Visit PuppyGraph →
#8 Stardog
Enterprise knowledge graph platform with virtual graphs and inference · United States · stardog.com
knowledge graph virtual graphs inference
Stardog's central idea is that the knowledge graph should connect to data where it sits. Virtual graphs map existing databases into the graph, an inference engine derives facts from rules, and the platform includes a Studio development environment, data quality tools and a built-in NLP pipeline.
- Pricing: Quote only. Stardog Free has no commercial limitations; Enterprise licenses, including high availability and support, are quoted by sales. Free plan: free plan.
- Best for: Knowledge graph programs over many sources
- Strengths: Virtual graphs; Inference engine; Machine learning NLP pipeline.
Where it falls short
The production price is not published, and the free edition omits high availability, backups and most connectors, so a pilot does not match the live system. The semantic modeling skills required are scarce.
Wrong for
Small teams with a simple graph application and no ontology work.
Visit Stardog →
#9 GraphDB
RDF and SPARQL database with reasoning, from Graphwise · Austria · ontotext.com
RDF SPARQL reasoning
GraphDB is what you pick when the standard matters. It is fully compliant with RDF 1.1 and SPARQL 1.1, supports RDF-star, runs forward-chaining reasoning with the standard rulesets RDFS and OWL 2 RL and QL, and is compatible with the RDF4J framework. A plugin API gives geospatial indexing and connectors to MongoDB, Lucene, Elasticsearch, Solr and Kafka.
- Pricing: Quote only. GraphDB Free is available; Enterprise is priced by sales ('call us for custom pricing'). Free plan: free plan.
- Best for: Semantic standards and taxonomy projects
- Strengths: RDF 1.1 and SPARQL 1.1 compliance; RDF-star and SPARQL-star; RDFS and OWL 2 RL and QL reasoning.
Where it falls short
The Free edition lacks the cluster that production needs, and Enterprise pricing is quote-only.
Wrong for
Application teams that just want to store edges and run traversals.
Visit GraphDB →
#10 NebulaGraph
Distributed open-source graph database with an enterprise and cloud edition · United States · nebula-graph.io
distributed open source nGQL
NebulaGraph targets graphs that do not fit on one machine. The open-source edition covers a subset of core features and is meant for developers and experiments, the Enterprise edition adds enhanced performance, security and premium support, and NebulaGraph Cloud offers one-click deployment with pay-as-you-go billing and a 14-day free trial with no credit card.
- Pricing: Open source + paid tiers. Open-source edition, an Enterprise edition by quote, and NebulaGraph Cloud pay as you go with a 14-day free trial and no credit card. Free plan: open source.
- Best for: Large-graph teams comfortable operating clusters
- Strengths: nGQL and openCypher; ISO-GQL support; Distributed architecture.
Where it falls short
Published cloud rates and Enterprise pricing are not on the pages we read, so budgeting needs a call. The open edition leaves out features that production usually needs.
Wrong for
Buyers who want the shortest route to a managed service with a printed price.
Visit NebulaGraph →
#11 AllegroGraph
Distributed RDF graph and document database with a natural-language agent · United States · allegrograph.com
RDF SPARQL knowledge graph
AllegroGraph, from Franz Inc., has been selling RDF technology for a long time and describes itself as a horizontally distributed graph and document database. Version 9.0 adds GraphTalker, an agent that lets business users ask natural-language questions of a knowledge graph and iteratively explores schema, tests queries and learns from errors until the SPARQL works.
- Pricing: Quote only. A free edition is offered; paid licenses are sold through a purchase process and cloud options, with prices not published. Free plan: free plan.
- Best for: Knowledge graph teams wanting a distributed RDF store
- Strengths: Distributed graph and document database; GraphTalker natural-language agent; Gruff visualization.
Where it falls short
Pricing is not shown, so a comparison needs a quote and a call.
Wrong for
Startups and product teams that need a property graph with Cypher drivers and a large community.
Visit AllegroGraph →
#12 JanusGraph
Open-source distributed graph database on pluggable storage backends · United States · janusgraph.org
open source Gremlin Linux Foundation
JanusGraph is the independence pick. It is fully open source under Apache 2.0, a Linux Foundation project since 2017, and stores graphs on a backend you choose, such as Cassandra, HBase, Bigtable or ScyllaDB, with Elasticsearch, Solr or Lucene for indexes.
- Pricing: Open source. Free under the Apache 2.0 license; you pay for the servers, storage backend and the engineers who run them. Free plan: open source.
- Best for: Platform teams that want no vendor
- Strengths: Apache 2.0 license; Gremlin and Apache TinkerPop; ACID or eventually consistent transactions.
Where it falls short
There is no vendor to call. You choose, size, secure, back up and upgrade the storage and index layers, and the community is volunteer-led.
Wrong for
Small teams without platform engineers, or anyone who wants a managed service and a support contract.
Visit JanusGraph →
Questions and answers
What is the best graph database software in 2026?
Memgraph ranks first of 12, best for real-time graph apps on a clear budget. FalkorDB is second (small teams building GraphRAG and SaaS features) and TigerGraph third (deep-traversal analytics on large graphs). Places 1 to 3 go to challengers, vendors outside the best-known brands, wherever the category has them.
What is the difference between a property graph and an RDF graph?
A property graph stores nodes and edges that carry their own key-value properties, and is queried with Cypher, Gremlin or GQL.
Do I need a graph database, or will PostgreSQL do?
If your questions stop at two or three joins and the data fits one server, a relational database with recursive queries is usually enough and cheaper to run.
Which graph databases have a genuinely free option?
In this list, Neo4j has AuraDB Free, TigerGraph has a free Savanna tier and a Community download, FalkorDB and Stardog have free plans, GraphDB and AllegroGraph have free editions, PuppyGraph has a Developer Edition, and Cosmos DB has a free tier on new accounts.
Is Neo4j still the default choice for graph databases?
Neo4j remains the best-known name and has the largest body of tutorials, courses and hired talent, which is a real advantage when you recruit.
Can a graph database power GraphRAG and AI assistants?
Yes, and most vendors now market exactly that. The pattern is to extract entities and relationships from documents, store them as a graph, and retrieve connected facts alongside vector matches before the model answers.
How much memory should I plan for?
Plan from the number of nodes, edges and properties, then add headroom for indexes and replicas. In-memory engines, including Memgraph and FalkorDB, price by memory, so the graph size maps straight to the bill.
What query language should my team learn?
Cypher has the widest reach among property graph products and is the safest bet for new hires. Gremlin is the language of Apache TinkerPop and fits JanusGraph and Cosmos DB.
Is Azure Cosmos DB for Apache Gremlin a safe long-term bet?
It works, and it is the shortest path for teams already on Azure, with a free tier on new accounts.
Can I run a graph database in the EU with EU-based support?
Options are limited. Graphwise, which sells GraphDB, is headquartered in Vienna, and Neo4j lists entities in Malmö, London and Frankfurt alongside its US company.
When does an RDF triple store beat a property graph?
When the value is in shared meaning. If several departments must agree on definitions, link terms to external vocabularies, and let a reasoner derive new facts from rules, a triple store is the better foundation.
