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Neo4j is optimised for read-heavy graph workloads and traversal-centric analysis. SurrealDB is designed for continuously updated graphs and multi-modal 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.
Designed for horizontal scale: SurrealDB distributes data and compute across nodes and has multi-writer capabilities.
Enterprise teams use SurrealDB for continuously changing datasets where correctness, performance, and scale matter.
SurrealDB vs. Neo4j at a glance
AI applications need live data, continuous real-time updates, and complex retrieval queries, pushing graph-only read-optimised systems to their limits.
Neo4j suits mostly static graphs, while SurrealDB is built for constantly changing, high-volume, multi-model workloads.
Business-critical capabilities
Neo4j: Graph-native database optimised for traversal. Cache-dependent performance. Performance degrades under sustained write-heavy workloads.
SurrealDB: Designed for continuously updated data with native graph, document, vector, full-text, and temporal querying.
Platform openness and composability
Neo4j: Graph-first engine. Non-graph typically lives elsewhere, requiring duplication and ETL.
SurrealDB: Single engine and query language across all data models.
Cost and performance
Neo4j: Based on public pricing, enterprise cloud deployments are ~2-3x more expensive. Strong performance requires the working graph to fit in memory.
SurrealDB: Lower cost due to open source model and efficient hardware utilisation. Compute can be scaled horizontally to handle heavier workloads.
SurrealDB is built for enterprise reliability and live data
Designed for continuous writes
Neo4j relies on an in-memory page cache. Writes replace affected pages in cache, causing churn and eviction, degrading performance.
SurrealDB: is built for mixed read/write workloads, with a write path designed for concurrent updates across nodes and storage optimised for sustained write throughput rather than cache residency.
Scales beyond memory limits
Neo4j performance assumes the working dataset fits in memory on a single machine; when it does not, performance degrades.
SurrealDB: is designed to operate beyond single-node memory limits and handle large, evolving datasets.
No hard cluster limits
Neo4j clustering is limited to a single primary writer, and scales reads through reader nodes with full replicas.
SurrealDB: is multi-master and designed for large distributed deployments without single-writer leadership or full-replica scaling constraints.
Enterprise takeaway
Neo4j is best suited for slowly changing analytical graphs.
SurrealDB: is designed for live, large-scale production systems such as AI agents.
SurrealDB is open and interoperable by design
One platform instead of many
Neo4j is graph-native. Documents, vectors, and full-text search typically require separate systems, leading to duplication and complex pipelines.
SurrealDB: unifies graph, document, vector, full-text, and temporal data in a single engine.
Unified query execution
Neo4j executes queries as staged index lookups and graph traversals with optimisation limited to the graph domain.
SurrealDB: executes vector similarity, full-text search, document filtering, and graph traversal within a single unified query plan.
Control without lock-in
Neo4j's advanced capabilities and horizontal scaling are tied to proprietary enterprise offerings.
SurrealDB: provides the same core capabilities in open source, with full control over deployment and architecture.
Platform takeaway
Neo4j excels at graph traversal and pattern matching, but typically operates as part of a broader multi-database architecture.
SurrealDB: reduces system sprawl, operational overhead, and data inconsistency by collapsing multiple databases into one unified platform.
Feature-by-feature comparison
Business model
| Neo4j | SurrealDB |
|---|---|
| Largely proprietary, with most production-grade features gated behind a commercial agreement. | Open source and available for use. |
Availability
| Neo4j | SurrealDB |
|---|---|
| Runs locally, on-premises, and on all major public clouds, either self-managed or via the Neo4j Aura managed service. | Runs locally, on-premises, and on all major public clouds. Deployable as embedded, single-node, or distributed. |
Architecture
| Neo4j | SurrealDB |
|---|---|
| Graph-native database with tightly coupled storage and compute. Optimised for read scaling with a single write leader. Performance depends on the in-memory page cache, with higher latency when data exceeds memory. Heavy writes cause cache churn and coordination overhead. | Distributed, multi-model database with decoupled query and storage layers. Uses distributed key-value stores in clustered mode to support horizontal scaling while remaining efficient on a single node. Designed for heavy read and write production workloads. |
Scale
| Neo4j | SurrealDB |
|---|---|
| Scales reads via replicas. Write scalability is limited to a single leader. Data is fully replicated to each read node, increasing memory requirements and constraining practical dataset size. | Horizontally scalable for both reads and writes by adding query nodes and scaling the distributed storage layer. Designed to avoid manual sharding and to support large datasets and high write throughput. |
Resilience
| Neo4j | SurrealDB |
|---|---|
| Loss of the primary writer causes temporary unavailability until leader re-election completes. | Distributed deployment provides high availability and fault tolerance. No single node failure causes system unavailability. |
Transactional consistency
| Neo4j | SurrealDB |
|---|---|
| ACID transactions on the write leader, clustered reads are replica-lagged unless causal consistency is enforced via bookmarks. | Supports distributed ACID transactions with strong consistency guarantees. |
Models
| Neo4j | SurrealDB |
|---|---|
| Single-model graph database focused on nodes, relationships, and properties. | Native multi-model database supporting document, relational, graph, key-value, time-series, vector, and geospatial access patterns in a single system. |
Pricing
| Neo4j | SurrealDB |
|---|---|
| Enterprise cloud pricing is $146 per GB of memory per month. A 128 GB deployment costs $18,688 per month. Scaling requires read replicas that each carry a full copy of the data, causing costs to grow rapidly with dataset size and throughput. | At an equivalent memory footprint, a cloud deployment is 62.4% cheaper than Neo4j. SurrealDB's distributed storage engine enables auto-sharded distributed storage and regularly achieves 70-80% compression. Costs scale roughly linearly with data volume and workload growth. |
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