SurrealDB vs.
Neo4j

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

Yes. First-class relations, multi-depth traversal, pattern matching and graph-aware filtering, with concurrent writes across nodes where Neo4j has one write leader per database.

Q: Can I migrate from Neo4j to SurrealDB?

Yes. Nodes, relationships and properties map onto SurrealDB's flexible schema. A migration guide is in the documentation.

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Neo4j. Built for traversal-heavy graph workloads at modest scale.

SurrealDB. Built for live, large-scale application data, graph included.

Together in one. Graph beside documents, vectors and relations.

01 |THE DIFFERENCE

Where Neo4j is read-tuned, SurrealDB is built for live data

Neo4j is optimised for read-heavy graph workloads. SurrealDB is designed for continuously updated graphs and multi-model workloads at scale.

WRITES

Built for graphs that change

Neo4j is tuned for read-heavy traversal over an in-memory page cache, and sustained writes churn it. SurrealDB's write path is designed for concurrent updates across nodes and sustained throughput.

Explore the architecture

MODELS

Documents and vectors beside the graph

Neo4j is a single-model store of nodes, relationships and properties. SurrealDB keeps the graph beside documents, vectors, relations and time-series, queried together.

Explore the data models

SCALE

Horizontal writes without composite databases

Each Neo4j database has one write leader, and write scaling means partitioning across composite databases. SurrealDB scales reads and writes horizontally with compute separated from storage.

Explore scaling

02 |THE SAME QUERY

How SurrealDB and Neo4j differ

AI applications need live data, continuous updates and complex retrieval, which pushes read-optimised graph stores to their limits.

Data

SurrealDB

Neo4j

Models

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

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

Operations

Architecture

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

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

Scale

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

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

Write performance

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

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

Resilience

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

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

Consistency and cost

Transactional consistency

Distributed ACIDDistributed ACID transactions with strong consistency guarantees.

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

Pricing

Open coreOpen source core with straightforward pricing. Costs scale linearly with data volume and workload.

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

04 |DIGITAL TWINS

Graph plus everything else. Telemetry, documents, vectors and geography beside the graph, in one engine.

One query

Walk, join, score and bound in one statement

Neo4j models the graph of a digital twin well. Telemetry, configuration documents, semantic search, geospatial state and scenario branching usually mean further systems and a sync layer. SurrealDB holds all of them in one engine and one query language. Read more in the digital twins use case.

-- One query: walk the graph, join recent telemetry,
-- score similar incidents by vector, restrict by geography.
SELECT
    id, name, status,
    ->depends_on->asset.{ id, name, status } AS upstream,
    (SELECT value, recorded_at FROM reading
       WHERE asset_id = $parent.id AND metric = "vibration"
       ORDER BY recorded_at DESC LIMIT 3) AS recent_readings,
    (SELECT id, summary, vector::distance::knn() AS dist
       FROM incident_report
       WHERE embedding <|3|> $manual_chunk) AS similar_incidents
FROM asset
WHERE kind = "compressor"
  AND geo::distance(location, (-1.6, 53.8)) < 25000;

TRUSTED BY

Enterprise teams building on SurrealDB

Three data stores collapsed into one

An AI assistant for 10,000 technicians

Nine backend tools consolidated into one

Low-latency RAG, under the customer's control

45 million recommendation queries a month

Neo4j,answered

How is SurrealDB different from Neo4j?

Neo4j is a graph-native database optimised for read-heavy traversal. SurrealDB is a multi-model database with native graph beside documents, vectors, full-text and temporal data, built for continuously updated production systems.

Can SurrealDB handle the same graph workloads as Neo4j?

Can I migrate from Neo4j to SurrealDB?

THE PLATFORM

Everything an application and its agents know. Five surfaces, one engine.

Database

Document, graph, vector, time-series and relational in one engine.

Five data models as dotted tiles: documents, graph, vector, time-series and relational

Agent Memory

What an agent learns, with its source and its time, in the same engine.

A timeline of remembered facts, each with its source

Cloud

Managed clusters in the regions you choose, scaled on demand.

Clusters in three regions on a world map, each running or scaling

Studio

Query, explore and design the schema from the browser.

MCP

Every model that speaks MCP reaches the database and the memory directly.

Three models connected through MCP to the database and Agent Memory

IN PRODUCTION

Trusted at scale. Samsung, Nvidia, Verizon, Tencent and Walmart run on SurrealDB.

14,000+

Developers building on SurrealDB Cloud

4M+

Developers building on SurrealDB worldwide

FROM THE TEAMS

SurrealDB gives us a foundation where we can unify semantic search, knowledge graphs, and AI-driven decision making without stitching together multiple systems. Collapsing responsibility into SurrealDB has become our default engineering posture.

Justin Foley

VP of Engineering, Later

GET STARTED

Migrate from Neo4j. Traverse, search and transact in one engine.

Start free with SurrealDB Migration guide

SurrealDB vs. Neo4j