SurrealDB is built for live, large-scale application data. Neo4j is built for traversal-heavy graph workloads at modest scale.
Neo4j is optimized for read-heavy graph workloads and traversal-centric analysis. SurrealDB is designed for continuously updated graphs and multi-modal workloads at scale.
Enterprise teams use SurrealDB for continuously changing datasets where correctness, performance, and scale matter.
at a glance
AI applications need live data, continuous real-time updates, and complex retrieval queries, pushing graph-only read-optimized systems to their limits.
Neo4j suits mostly static graphs, while SurrealDB is built for constantly changing, high-volume, multi-model workloads.
Graph-native database optimised for traversal. Cache-dependent performance. Performance degrades under sustained write-heavy workloads.
Designed for continuously updated data with native graph, document, vector, full-text, and temporal querying.
Graph-first engine. Non-graph typically lives elsewhere, requiring duplication and ETL.
Single engine and query language across all data models.
Based on public pricing, enterprise cloud deployments are ~2-3x more expensive. Strong performance requires the working graph to fit in memory.
Lower cost due to open source model and efficient hardware utilisation. Compute can be scaled horizontally to handle heavier workloads.
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.
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.
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.
Neo4j is best suited for slowly changing analytical graphs.
SurrealDB is designed for live, large-scale production systems such as AI agents.
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.
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.
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.
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.
A direct comparison of capabilities, architecture, and deployment options.
Business model
Neo4j
Largely proprietary, with most production-grade features gated behind a commercial agreement.
SurrealDB
Open source and available for use.
Availability
Neo4j
Runs locally, on-premises, and on all major public clouds, either self-managed or via the Neo4j Aura managed service.
SurrealDB
Runs locally, on-premises, and on all major public clouds. Deployable as embedded, single-node, or distributed.
Architecture
Neo4j
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.
SurrealDB
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
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.
SurrealDB
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
Loss of the primary writer causes temporary unavailability until leader re-election completes.
SurrealDB
Distributed deployment provides high availability and fault tolerance. No single node failure causes system unavailability.
Transactional consistency
Neo4j
ACID transactions on the write leader, clustered reads are replica-lagged unless causal consistency is enforced via bookmarks.
SurrealDB
Supports distributed ACID transactions with strong consistency guarantees.
Models
Neo4j
Single-model graph database focused on nodes, relationships, and properties.
SurrealDB
Native multi-model database supporting document, relational, graph, key-value, time-series, vector, and geospatial access patterns in a single system.
Pricing
Neo4j
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.
SurrealDB
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.
