--- title: "SurrealDB vs. Neo4j | Database comparison" description: "A comparison of SurrealDB and Neo4j features and capabilities." url: https://surrealdb.com/comparison/neo4j --- # SurrealDB vs. Neo4j SurrealDB is built for live, large-scale application data. Neo4j is built for traversal-heavy graph workloads at modest scale. [Migrate from Neo4j](https://surrealdb.com/docs/build/migrating/from-other-databases/from-neo4j) [Contact us](https://surrealdb.com/contact) 1. ![Babcock](https://surrealdb.com/assets/static/babcock.lo4rnVg1.svg) 2. ![ING](https://surrealdb.com/assets/static/ing.X3I6S3_V.svg) 3. ![British Airways](https://surrealdb.com/assets/static/british-airways.KEsZiwV-.svg) 4. ![Nvidia](https://surrealdb.com/assets/static/nvidia.DaIEuMil.svg) 5. ![Apple](https://surrealdb.com/assets/static/apple.D5pq4flY.svg) 6. ![SpaceX](https://surrealdb.com/assets/static/spacex.CQJEk-IL.svg) 7. ![Samsung](https://surrealdb.com/assets/static/samsung.CH-vQgnb.svg) 8. ![adidas](https://surrealdb.com/assets/static/adidas.DdTC5qhk.svg) 9. ![Tencent](https://surrealdb.com/assets/static/tencent.paQmLxyy.svg) 10. ![Alibaba](https://surrealdb.com/assets/static/alibaba.B16idgfM.svg) 11. ![PolyAI](https://surrealdb.com/assets/static/poly-ai.c3w_fAg6.svg) 12. ![Later](https://surrealdb.com/assets/static/later.Ds736jFO.svg) 13. ![Verizon](https://surrealdb.com/assets/static/verizon.BI7CajdX.svg) 14. ![Liberty Mutual](https://surrealdb.com/assets/static/liberty-mutual.B7qOU1pd.svg) 15. ![Walmart](https://surrealdb.com/assets/static/walmart.BjDg_Sr8.svg) 16. ![Carrier](https://surrealdb.com/assets/static/carrier.D21gC6NX.svg) 17. ![Saks Fifth Avenue](https://surrealdb.com/assets/static/saks-fifth-avenue.COIDpLSb.svg) 18. ![San Francisco Compute Company](https://surrealdb.com/assets/static/sfcc.B7jlImq4.svg) 19. ![Shield AI](https://surrealdb.com/assets/static/shield-ai.pINZ0KJr.svg) 20. ![Wix](https://surrealdb.com/assets/static/wix.DvHhmoBi.svg) KEY ADVANTAGES ## Why teams choose SurrealDB over Neo4j Neo4j is optimised for read-heavy graph workloads. SurrealDB is designed for continuously updated graphs and multi-model workloads at scale. ### Built for live workloads SurrealDB supports mixed read/write workloads and real-time updates for constantly changing operational systems. ### Lower cost at scale Efficient storage usage through auto-sharding reduces infrastructure costs. ### Multi-model by design Graph, document, vector, geospatial, full-text, and temporal data in one engine. ### Horizontal scale SurrealDB distributes data and compute across nodes with multi-writer capabilities. HOW IT COMPARES ## How SurrealDB and Neo4j differ AI applications need live data, continuous real-time updates, and complex retrieval queries - pushing graph-only read-optimised systems to their limits. Feature Neo4j SurrealDB Architecture Graph-native with tightly coupled storage and compute. Performance depends on in-memory page cache. Distributed, multi-model database with decoupled query and storage layers. Designed for heavy read and write workloads. Models Single-model graph database focused on nodes, relationships, and properties. Native multi-model: document, relational, graph, key-value, time-series, vector, and geospatial. Scale Each database uses a single write leader. Write scaling requires partitioning data across composite databases. Horizontally scalable for both reads and writes. Designed to avoid manual sharding. Write performance Relies on in-memory page cache. Writes cause churn and eviction, degrading performance. Write path designed for concurrent updates across nodes. Storage optimised for sustained write throughput. Resilience Loss of the primary writer causes temporary unavailability until leader re-election. Distributed deployment provides high availability. No single node failure causes system unavailability. Transactional consistency ACID on write leader. Clustered reads are replica-lagged unless causal consistency is enforced via bookmarks. Distributed ACID transactions with strong consistency guarantees. Pricing Enterprise capabilities gated behind proprietary offerings. Scaling requires full-replica read nodes. Open source core with straightforward pricing. Costs scale linearly with data volume and workload. DIGITAL TWINS ## Digital twins: graph plus everything else Neo4j models the graph part of a digital twin well. The rest of a twin - high-frequency telemetry, configuration documents, semantic search over manuals, geospatial state, scenario branching - typically requires additional systems and the synchronisation layer between them. SurrealDB handles all of these in a single transactional engine, in one query language. For digital twin workloads, this means one substrate rather than a stack to operate. Read more in our [Digital Twins use case](https://surrealdb.com/use-cases/digital-twins). ``` 1-- One query: walk the graph, join recent telemetry,2-- score similar incidents by vector, restrict by geography.3SELECT4 id, name, status,5 ->depends_on->asset.{ id, name, status } AS upstream,6 (SELECT value, recorded_at FROM reading7 WHERE asset_id = $parent.id AND metric = "vibration"8 ORDER BY recorded_at DESC LIMIT 3) AS recent_readings,9 (SELECT id, summary, vector::distance::knn() AS dist10 FROM incident_report11 WHERE embedding <|3|> $manual_chunk) AS similar_incidents12FROM asset13WHERE kind = "compressor"14 AND geo::distance(location, (-1.6, 53.8)) < 25000; ``` TRUSTED BY ## Enterprise teams building on SurrealDB From knowledge graphs to AI assistants - how enterprise teams are building on SurrealDB. ![Samsung](https://surrealdb.com/assets/static/4c58b81e7b3c9466.C_Hv0eml.svg) DATABASE ### [Unlocking insights with knowledge graphs](https://surrealdb.com/customer/samsung) Samsung Ads uses SurrealDB to build dynamic, real-time knowledge graphs for smarter campaign execution - collapsing three legacy data stores into one. Read case study ![Verizon](https://surrealdb.com/assets/static/18b99996c689000f.B5PQ-nI9.svg) DATABASE ### [AI assistant empowering 10,000 technicians](https://surrealdb.com/customer/verizon) Verizon uses SurrealDB to power a generative AI assistant for 10,000 field technicians, delivering instant access to documentation, outage updates, and workflows. Read case study ![Tencent](https://surrealdb.com/assets/static/401d8346058682c8.DqM87mst.svg) DATABASE ### [Unified infrastructure monitoring](https://surrealdb.com/customer/tencent) Tencent consolidated nine backend tools into one real-time monitoring platform powered by SurrealDB's multi-model context graph. Read case study ![PolyAI](https://surrealdb.com/assets/static/c5fa07c66cd05131.BnC7wHcc.svg) DATABASE ### [High-performance customer service AI powered by RAG](https://surrealdb.com/customer/polyai) PolyAI connects SurrealDB to Agent Studio for low-latency, customer-controlled RAG across voice AI experiences. Read case study ![Saks Fifth Avenue](https://surrealdb.com/assets/static/saks-fifth-avenue-white.pDJ9HGmf.svg) DATABASE ### [AI-powered personalisation at massive scale](https://surrealdb.com/customer/saks) Saks Fifth Avenue uses SurrealDB's vector search and graph capabilities to deliver real-time, AI-powered personalisation across 5 million luxury customers and 45 million monthly product-recommendation queries. Read case study FREQUENTLY ASKED QUESTIONS ## Common questions about Neo4j How is SurrealDB different from Neo4j? Can SurrealDB handle the same graph workloads as Neo4j? Can I migrate from Neo4j to SurrealDB? GET STARTED ## Migrate from Neo4j Graph queries without a separate store: traverse, search, and transact in one engine. ![Samsung](https://surrealdb.com/assets/static/4c58b81e7b3c9466.C_Hv0eml.svg)![NVIDIA](https://surrealdb.com/assets/static/nvidia.DaIEuMil.svg)![Apple](https://surrealdb.com/assets/static/f7dc2519e0d212bc.Cn8MYAK7.svg)![Verizon](https://surrealdb.com/assets/static/18b99996c689000f.B5PQ-nI9.svg)![Tencent](https://surrealdb.com/assets/static/401d8346058682c8.DqM87mst.svg) SOC 2 Type 2 GDPR Cyber Essentials Plus ISO 27001 [Start free with SurrealDB](https://studio.surrealdb.com/current/instances/deploy) [Migration guide](https://surrealdb.com/docs/build/migrating/from-other-databases/from-neo4j) ```json {"@context":"https://schema.org","@type":"Organization","name":"SurrealDB","url":"https://surrealdb.com","logo":"https://surrealdb.com/assets/static/logo.BG7_TG2b.svg","description":"SurrealDB is the unified data layer for AI. 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SurrealDB is a multi-model database that provides native graph capabilities alongside documents, vectors, full-text search, and temporal data in one engine - designed for continuously updated, large-scale production systems."}},{"@type":"Question","name":"Can SurrealDB handle the same graph workloads as Neo4j?","acceptedAnswer":{"@type":"Answer","text":"Yes. SurrealDB supports first-class graph relations, multi-depth traversals, pattern matching, and graph-aware filtering. It also handles mixed read/write workloads with concurrent writes across nodes, whereas Neo4j uses a single write leader per database."}},{"@type":"Question","name":"Can I migrate from Neo4j to SurrealDB?","acceptedAnswer":{"@type":"Answer","text":"Yes. SurrealDB supports the same graph concepts - nodes, relationships, and properties - with a flexible schema that can mirror your existing Neo4j data model. A migration guide is available in the documentation."}}]} ```