SurrealDB vs. Neo4j

Q: How is SurrealDB different from Neo4j?

Neo4j is a graph-native database optimised for read-heavy traversal workloads. 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.

Q: Can SurrealDB handle the same graph workloads as Neo4j?

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.

Q: Can I migrate from Neo4j to SurrealDB?

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.

SurrealDB is built for live, large-scale application data. Neo4j is built for traversal-heavy graph workloads at modest scale.

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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

SurrealDB vs. Neo4j

AI applications need live data, continuous real-time updates, and complex retrieval queries - pushing graph-only read-optimised systems to their limits.

Architecture

Neo4j

Graph-native with tightly coupled storage and compute. Performance depends on in-memory page cache.

SurrealDB

Distributed, multi-model database with decoupled query and storage layers. Designed for heavy read and write workloads.

Models

Neo4j

Single-model graph database focused on nodes, relationships, and properties.

SurrealDB

Native multi-model: document, relational, graph, key-value, time-series, vector, and geospatial.

Scale

Neo4j

Each database uses a single write leader. Write scaling requires partitioning data across composite databases.

SurrealDB

Horizontally scalable for both reads and writes. Designed to avoid manual sharding.

Write performance

Neo4j

Relies on in-memory page cache. Writes cause churn and eviction, degrading performance.

SurrealDB

Write path designed for concurrent updates across nodes. Storage optimised for sustained write throughput.

Resilience

Neo4j

Loss of the primary writer causes temporary unavailability until leader re-election.

SurrealDB

Distributed deployment provides high availability. No single node failure causes system unavailability.

Transactional consistency

Neo4j

ACID on write leader. Clustered reads are replica-lagged unless causal consistency is enforced via bookmarks.

SurrealDB

Distributed ACID transactions with strong consistency guarantees.

Pricing

Neo4j

Enterprise capabilities gated behind proprietary offerings. Scaling requires full-replica read nodes.

SurrealDB

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.

1-- One query: walk the graph, join recent telemetry,
2-- score similar incidents by vector, restrict by geography.
3SELECT
4    id, name, status,
5    ->depends_on->asset.{ id, name, status } AS upstream,
6    (SELECT value, recorded_at FROM reading
7       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 dist
10       FROM incident_report
11       WHERE embedding <|3|> $manual_chunk) AS similar_incidents
12FROM asset
13WHERE 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 the context layer.

Samsung

Unlocking insights with knowledge graphs

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

AI assistant empowering 10,000 technicians

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