> ## Content Index > Fetch the complete content index at: https://www.edgewisely.com/llms.txt > Use this file to discover other available public pages before exploring further. # Top 7 Graph Databases in 2026 - URL: https://www.edgewisely.com/top-7-graph-databases-2026/ - Published: 2026-09-14T05:09:12.000Z - Updated: 2026-09-14T05:09:12.000Z - Description: The category consolidated in 2026: licences tightened, Kuzu disappeared into Apple, and a zero-ETL query engine arrived. Neo4j, Neptune, Memgraph, TigerGraph, Arango, FalkorDB and PuppyGraph compared. - Author: John Karpentar - Tags: Roundups, Engineering **For engineering and data teams choosing a store for connected data — fraud rings, knowledge graphs, entity resolution, agent memory. The category consolidated in 2025 and 2026: licenses tightened, one well-liked open-source engine disappeared into Apple, and a new class of tool arrived that queries your existing tables as a graph without moving any data.** A graph database stores relationships as first-class objects, so questions like "which accounts are within six hops of this one" resolve by traversal instead of by a pile of SQL joins. The leaders in 2026 are [Neo4j](https://neo4j.com/?ref=edgewisely.com), still the reference implementation and the largest ecosystem; [Amazon Neptune](https://aws.amazon.com/neptune/?ref=edgewisely.com), the default when the rest of the stack is AWS; and [Memgraph](https://memgraph.com/?ref=edgewisely.com), the in-memory option for workloads measured in milliseconds. Behind them sit [TigerGraph](https://www.tigergraph.com/?ref=edgewisely.com) for large-scale analytics, [Arango](https://arango.ai/?ref=edgewisely.com) for multi-model, [FalkorDB](https://www.falkordb.com/?ref=edgewisely.com) for GraphRAG and agent memory, and [PuppyGraph](https://www.puppygraph.com/?ref=edgewisely.com), which is not a database at all. All pricing and feature claims below are as of September 2026. ## How we picked these Four criteria, applied in this order. **Production maturity.** Is the engine actually run in production by organizations that are not the vendor, and does it have the operational surface — backups, HA, access control, monitoring — that implies? **Adoption and ecosystem.** Driver coverage, framework integrations, hiring pool, and the volume of public material a team can learn from when something breaks at 2am. **Capability fit.** Does it do something the others do not — in-memory latency, multi-model storage, zero-ETL querying, native vector search alongside traversal? **Deployment honesty.** Can you self-host it, and on what licence? This mattered more in 2026 than it did three years ago, because several vendors in this category moved off OSI-approved licences — a pattern we also traced across [time-series databases](https://www.edgewisely.com/top-7-time-series-databases-2026/). We also required seven genuinely distinct options. We left out engines that had been abandoned or absorbed — most notably Kuzu, the embedded graph database whose team [Apple acquired in October 2025](https://betakit.com/apple-strikes-deal-to-acquire-canadian-database-software-startup-kuzu/?ref=edgewisely.com). Its GitHub repository was archived on 10 October 2025; the acquisition only became public in February 2026 through an EU Digital Markets Act filing. Community forks exist, but a fork inherited by volunteers is not a product recommendation. ## Quick comparison | Company | Best for | Deployment | Pricing model | | -------------- | -------------------------------------------- | ----------------------------------- | ----------------------------------------------------- | | Neo4j | The default choice; largest ecosystem | Self-hosted, managed (Aura), hybrid | Free CE (GPLv3); Aura from $65/GB/month | | Amazon Neptune | Teams already standardized on AWS | Managed only (AWS) | Consumption; Serverless from $0.1098/NCU-hour | | Memgraph | Sub-second traversals on hot data | Self-hosted, managed | Free CE (BSL 1.1); Enterprise from $25k/year for 16GB | | TigerGraph | Deep multi-hop analytics at scale | Self-hosted, managed (Savanna) | Pricing on request | | Arango | Graph plus document plus vector in one store | Self-hosted, managed | Free CE (limits apply); Enterprise on request | | FalkorDB | GraphRAG and multi-tenant agent memory | Self-hosted, managed | Free tier; paid cloud tiers | | PuppyGraph | Graph queries over an existing lakehouse | Self-hosted (Docker, AMI) | Free Dev Edition; Enterprise on request | ## 1\. Neo4j Neo4j is the oldest and most widely deployed engine in the category, and remains the reference point that every competitor benchmarks against. It stores native property graphs and is queried in Cypher, the language Neo4j created and then opened up through the openCypher project in 2015\. That lineage turned out to matter: when ISO published [GQL as ISO/IEC 39075:2024](https://www.iso.org/standard/76120.html?ref=edgewisely.com) in April 2024 — the first new ISO database language standard since SQL in 1987 — the core syntax landed largely identical to Cypher. Neo4j's own documentation is candid that Cypher now covers most mandatory GQL features and much of the optional surface, with some mandatory features still outstanding. The product splits three ways: a self-hosted Community Edition under GPLv3, a self-hosted Enterprise Edition, and AuraDB, the managed service. The company raised a [$325 million Series F](https://neo4j.com/press-releases/neo4j-announces-seriesf-funding/?ref=edgewisely.com) in June 2021 led by Eurazeo with participation from GV, at a valuation above $2 billion. **Best for:** teams who want the largest talent pool, the deepest documentation, and the lowest chance of being stranded. **Pros** - Widest driver, framework, and tooling support of any graph database; most third-party integrations target it first. - Cypher is the direct ancestor of the ISO GQL standard, so the query skills transfer. - Three deployment models, including a fully managed service with published per-GB pricing. - Largest body of public documentation, courses, and community troubleshooting material. **Cons** - Community Edition omits clustering, role-based access control, and hot backups — the features most production deployments need — pushing serious users to Enterprise or Aura. - GPLv3 on Community Edition is a genuine obstacle for teams that want to embed or redistribute. - AuraDB list pricing of $65/GB/month for Professional and $146/GB/month for Business Critical gets expensive at larger graph sizes. - Horizontal write scaling remains harder than in engines designed around sharding from the start. ## 2\. Amazon Neptune Neptune is AWS's managed graph service, and its main argument is that it is already inside your account. It is unusual in supporting three query languages over the same service — Apache TinkerPop Gremlin, openCypher v9, and SPARQL 1.1 — which means both property-graph and RDF teams can use it without changing model. Storage grows automatically in 10 GiB segments up to 128 TiB, replicates across three availability zones, and supports up to 15 read replicas. AWS has since split the offering in two. Neptune Database is the transactional engine; Neptune Analytics is a separate engine for graph algorithms and vector search over data in S3 or an existing Neptune database. Amazon Bedrock Knowledge Bases uses Neptune underneath for its managed GraphRAG capability, which is the clearest signal of where AWS intends the product to sit — and a reminder of how far [enterprise RAG platforms](https://www.edgewisely.com/top-7-enterprise-rag-platforms-2026/) have moved into managed infrastructure. **Best for:** organizations whose data, IAM, and compliance posture already live in AWS. **Pros** - Three query languages on one service, covering both property-graph and RDF workloads. - Serverless option bills per NCU-second from $0.1098/NCU-hour in US East, with no upfront commitment. - Deep AWS integration: VPC isolation, IAM fine-grained access control, KMS encryption, CloudWatch metrics, over 20 compliance standards including HIPAA eligibility. - Point-in-time restore to any second within a retention window of up to 35 days. **Cons** - No self-hosted option at all; adopting Neptune is a commitment to AWS. - I/O charges under the Standard configuration make costs hard to forecast for traversal-heavy workloads — AWS itself recommends switching to I/O-Optimized once I/O exceeds 25% of spend. - Gremlin support targets TinkerPop 3.3, behind the current upstream release. - Neptune Database and Neptune Analytics are separate engines with separate pricing, which complicates architectures needing both transactions and algorithms. ## 3\. Memgraph Memgraph is built for the case where traversal latency is the product. It is written in C++ and holds the working set in memory, with durability provided by snapshots and a write-ahead log — the inverse of Neo4j's disk-first design with a RAM cache. It speaks Cypher over the Bolt protocol, so Neo4j drivers connect unchanged and most existing queries run without rewriting. That compatibility is deliberate, and it is the main reason teams evaluate it as a drop-in. Community Edition is free with no dataset cap and is positioned for production use. Enterprise adds multi-tenancy, automatic failover, and the security and compliance controls larger organizations require. **Best for:** real-time workloads — fraud scoring, routing, streaming analytics — where p99 latency is a hard requirement. **Pros** - Memory-first architecture delivers materially lower traversal latency than disk-first engines on hot data. - Bolt and Cypher compatibility makes migration from Neo4j cheap to test. - Community Edition imposes no dataset size limit and is supported for production. - Published Enterprise entry price of $25,000/year for 16GB, scaling with memory — unusual transparency in this category. **Cons** - The working set must fit in RAM, which puts a hard economic ceiling on graph size. - Community Edition ships under BSL 1.1, which is not an OSI-approved open-source licence and forbids offering Memgraph as a service. - Smaller ecosystem and community than Neo4j, so fewer worked examples when something unusual breaks. - Multi-tenancy and automatic failover are Enterprise-only, which pulls many production deployments into a paid tier regardless of data size. ## 4\. TigerGraph TigerGraph targets deep multi-hop analytics — the queries that degrade badly on engines tuned for shallow traversals. It uses native parallel graph storage and computation, and its own query language, GSQL. In January 2025 the company launched [Savanna](https://www.tigergraph.com/savanna/?ref=edgewisely.com), a cloud-native platform that separates storage and compute scaling and adds scheduled expansion and auto stop/resume. Savanna lets users query in GSQL, openCypher, or GQL pattern-matching syntax; TigerGraph participated in the GQL standardization effort from its start. The company raised a [$105 million Series C](https://www.qimingvc.com/en/news/tigergraph-raises-105m-series-c-its-enterprise-graph-database?ref=edgewisely.com) led by Tiger Global. Publicly named customers include Amgen, Citrix, Intuit, Jaguar Land Rover, and UnitedHealth Group. **Best for:** analytical workloads that traverse many hops across very large graphs. **Pros** - Parallel architecture is designed for deep-link analytics rather than shallow lookups. - Savanna decouples storage and compute, and connects to Snowflake, Spark, Delta Lake, Iceberg, and Postgres alongside the major object stores. - Three query language options including GQL pattern matching. - Ships pre-configured solution kits for common patterns such as transaction fraud, entity resolution, and KYC. **Cons** - GSQL is proprietary and unfamiliar; the skills do not transfer, and hiring is harder than for Cypher. - No published pricing for either the self-hosted product or Savanna — every evaluation starts with a sales conversation. - Smaller open-source presence and community than Neo4j or Memgraph. - Cost and elasticity claims for Savanna come from TigerGraph's own launch materials and have not been independently verified. ## 5\. Arango Arango — formerly ArangoDB, now operating as Arango with a repositioned "Contextual Data Platform" — stores graph, document, key-value, and vector data in one engine, queried through a single language, AQL. For teams whose workload is only partly graph-shaped, that consolidation removes a system from the architecture. The company positions the platform around enterprise AI context and GraphRAG, and was included in The Forrester Wave: Multimodel Data Platforms, Q2 2026. ![Arango Contextual Data Platform product graphic](https://storage.ghost.io/c/54/5a/545a66b3-60ef-480c-80ae-765bac52f6ec/content/images/2026/09/gdb-arango.jpg) Image: [Arango](https://arango.ai/?ref=edgewisely.com) Licensing is the thing to understand before adopting. In October 2023 ArangoDB [replaced Apache 2.0 with BSL 1.1](https://arango.ai/blog/evolving-arangodbs-licensing-model-for-a-sustainable-future/?ref=edgewisely.com) for future source versions, reverting to Apache 2.0 after four years. Separately, the prepackaged Community Edition binaries moved to a new ArangoDB Community License that restricts use to internal business purposes and caps a single cluster at 100GB. **Best for:** applications that need graph traversal alongside document and vector search without running three databases. **Pros** - One engine and one query language across graph, document, key-value, and vector workloads. - BSL 1.1 source reverts to Apache 2.0 after four years, so the licence change has a defined end state. - Vector search sits in the same store as the graph, removing a separate [vector database](https://www.edgewisely.com/7-best-vector-databases-in-2026-pinecone-qdrant-weaviate-milvus-chroma-turbopuffer-and-pgvector-compared/) from GraphRAG architectures. - Recognized in Forrester's Q2 2026 multimodel data platform evaluation. **Cons** - The Community License caps a single cluster at 100GB and excludes commercial use — a significant restriction the Apache 2.0 era did not have. - Two overlapping licence regimes (BSL 1.1 for source, Community License for binaries) make compliance genuinely confusing. - AQL is specific to Arango; the skills do not transfer to Cypher or GQL shops. - The rebrand and platform repositioning mean documentation and marketing now span two domain names and two product generations. ## 6\. FalkorDB FalkorDB takes an unusual implementation route: it represents adjacency as sparse matrices and executes graph queries as linear algebra, using AVX acceleration. It grew out of the Redis graph module lineage and remains Redis-based. The practical consequence of the design is cheap multi-tenancy — the engine supports many separate graphs inside one database, which is awkward to do on engines that assume one graph per instance. It speaks Cypher, and indexes vector embeddings in the same store as the graph. ![FalkorDB graph database for GenAI product graphic](https://storage.ghost.io/c/54/5a/545a66b3-60ef-480c-80ae-765bac52f6ec/content/images/2026/09/gdb-falkordb.jpg) Image: [FalkorDB](https://www.falkordb.com/?ref=edgewisely.com) The company aims squarely at GraphRAG and agent memory rather than at general-purpose graph workloads, with integrations for LangChain, LlamaIndex, Graphiti, and the Model Context Protocol. FalkorDB's website names BMW, Mimecast, and Snowflake among its users. **Best for:** multi-tenant GraphRAG and agent-memory systems where each customer needs an isolated graph. **Pros** - Native multi-graph support makes per-tenant isolation cheap without running an instance per tenant. - Sparse-matrix and linear-algebra execution is a genuinely different approach to traversal, not a reimplementation of the same design. - Vector indexing alongside the graph removes a separate vector store from GraphRAG stacks. - Cypher support and published Neo4j migration tooling lower the switching cost. **Cons** - Narrower positioning: built for GraphRAG and agent workloads, not as a general-purpose transactional graph database. - Much smaller company and community than Neo4j or Neptune, which is a real consideration for long-lived systems. - The latency and memory comparisons published on its site are vendor-run benchmarks against unnamed competitors, and should be reproduced on your own workload before they inform a decision. - Redis heritage means operational behaviour and memory characteristics will be unfamiliar to teams coming from disk-first graph engines. ## 7\. PuppyGraph PuppyGraph is the outlier here, and it is on the list because it solves the problem a lot of teams actually have: they want graph queries, but their data is already in a warehouse or lakehouse and they do not want to run an ETL pipeline into a second system. PuppyGraph is a query engine, not a database. You point it at existing relational stores — Databricks, Snowflake, BigQuery, Iceberg tables, Postgres — define a graph schema over those tables, and query in Gremlin or Cypher. Nothing is copied, which sidesteps the pipeline work that [ETL and data integration platforms](https://www.edgewisely.com/top-7-etl-and-data-integration-platforms-2026/) otherwise absorb. ![PuppyGraph zero-ETL graph query engine product graphic](https://storage.ghost.io/c/54/5a/545a66b3-60ef-480c-80ae-765bac52f6ec/content/images/2026/09/gdb-puppygraph.jpg) Image: [PuppyGraph](https://www.puppygraph.com/?ref=edgewisely.com) The company raised [$5 million in seed funding](https://www.businesswire.com/news/home/20241023866174/en/PuppyGraph-Raises-$5-Million-in-Seed-Funding-Led-by-Defy.vc-to-Bring-Zero-ETL-GraphRAG-and-Real-Time-Graph-Analytics-To-Market?ref=edgewisely.com) led by defy.vc, announced October 2024\. Named users include Coinbase, Palo Alto Networks, Netskope, Clarivate, and Protocol Labs. **Best for:** teams with a mature lakehouse who want graph traversal without standing up and syncing a graph database. **Pros** - No ETL and no data duplication — the single copy of data stays in the warehouse and remains queryable in SQL. - Broad source support across Iceberg, Databricks, Snowflake, BigQuery, AlloyDB, ClickHouse, and standard relational databases. - Free Developer Edition runs from a single Docker container with no payment or form. - Gremlin and Cypher support means existing graph queries mostly port over. **Cons** - Not a database: no storage, no transactional writes, and no durability guarantees of its own. It inherits whatever the underlying store provides. - Query performance is bounded by the source system; a slow warehouse produces slow traversals regardless of PuppyGraph's engine. - Enterprise pricing is unpublished and based on server memory and CPU. - Small company on a $5 million seed round, which is a concentration risk for a component sitting in a critical query path. ## How to choose If you have no strong constraint and want the safest default, start with **Neo4j** — the ecosystem advantage is real and the Cypher-to-GQL path protects the investment. If your infrastructure and compliance posture are already AWS, **Amazon Neptune** removes a vendor relationship and an operational burden, provided you model the I/O costs first. If traversal latency is the product and your working set fits in memory, **Memgraph** is the strongest technical fit, and its Bolt compatibility makes it cheap to prove. If your queries go deep — six hops and beyond across billions of edges — **TigerGraph** is built for exactly that shape, though budget a sales cycle. If your workload is only partly graph-shaped, **Arango** collapses three systems into one, but read both licences before committing. If you are building GraphRAG or agent memory with per-tenant isolation, **FalkorDB** is purpose-built for it. And if your data already sits in a lakehouse and the thought of syncing it into a graph store is the objection, **PuppyGraph** removes the pipeline entirely — as long as you accept that you are adding a query engine, not a database. ## Frequently Asked Questions ### What is a graph database used for? Graph databases store relationships as first-class data, making them well suited to fraud ring detection, recommendation engines, entity resolution, knowledge graphs, supply chain analysis, and network and IT asset mapping. They are strongest where queries traverse several hops between entities, which becomes expensive in relational systems as join depth increases. ### Is GQL replacing Cypher? Not replacing — converging. ISO published GQL as ISO/IEC 39075:2024 in April 2024, the first new ISO database language standard since SQL in 1987\. Its core syntax is largely identical to Cypher, on which it was based. Neo4j reports that Cypher now covers most mandatory GQL features, with some still in progress. ### Which graph databases are genuinely open source? Fewer than in 2023\. Neo4j Community Edition remains GPLv3\. Memgraph and ArangoDB both moved to Business Source License 1.1, which is source-available rather than OSI-approved open source. Arango additionally applies a separate Community License to its binaries, capping clusters at 100GB and excluding commercial use. ### Do I need a graph database for GraphRAG? Not necessarily a dedicated one. Several engines now index vector embeddings alongside the graph — Arango, FalkorDB, and Neptune Analytics among them — which removes the need for a separate vector database. PuppyGraph takes a different route, running GraphRAG queries over lakehouse tables with no graph store at all. ### What happened to Kuzu? Apple agreed to acquire the Ontario-based company in October 2025\. Its GitHub repository was archived on 10 October 2025 alongside a final 0.11.3 release, and its website came down. The acquisition was not publicly confirmed until February 2026, when an EU Digital Markets Act filing disclosed it. Community forks continue, but the original project is not maintained. --- **Editor's note — sources:** [ISO/IEC 39075:2024 (GQL)](https://www.iso.org/standard/76120.html?ref=edgewisely.com); [Neo4j Series F press release](https://neo4j.com/press-releases/neo4j-announces-seriesf-funding/?ref=edgewisely.com); [Neo4j Cypher Manual — GQL conformance](https://neo4j.com/docs/cypher-manual/current/appendix/gql-conformance/?ref=edgewisely.com); [Amazon Neptune features](https://aws.amazon.com/neptune/features/?ref=edgewisely.com); [Memgraph pricing](https://memgraph.com/pricing?ref=edgewisely.com); [TigerGraph Savanna](https://www.tigergraph.com/savanna/?ref=edgewisely.com); [TigerGraph Series C](https://www.qimingvc.com/en/news/tigergraph-raises-105m-series-c-its-enterprise-graph-database?ref=edgewisely.com); [ArangoDB licensing change](https://arango.ai/blog/evolving-arangodbs-licensing-model-for-a-sustainable-future/?ref=edgewisely.com); [FalkorDB](https://www.falkordb.com/?ref=edgewisely.com); [PuppyGraph seed funding](https://www.businesswire.com/news/home/20241023866174/en/PuppyGraph-Raises-$5-Million-in-Seed-Funding-Led-by-Defy.vc-to-Bring-Zero-ETL-GraphRAG-and-Real-Time-Graph-Analytics-To-Market?ref=edgewisely.com); [BetaKit on Apple's Kuzu acquisition](https://betakit.com/apple-strikes-deal-to-acquire-canadian-database-software-startup-kuzu/?ref=edgewisely.com). Pricing and feature details verified September 2026 and subject to change.