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MongoDB started as a document store and later added search, vector, and limited graph features through separate systems. SurrealDB was built from the ground up to run document, relational, graph, vector, full-text, and temporal queries natively in one engine.
True multi-model engine: Document, relational, graph, vector, full-text, temporal, and geospatial access patterns natively in one system.
Temporal graph querying: Time is treated as a first-class constraint within graph traversal.
Unified indexing: No compound multikey limitations across arrays or nested fields.
ACID without locking: Transactions across all data models without global or table-level locking.
SurrealDB is built for systems that need correctness, composability, and consistent semantics across complex queries.
SurrealDB vs. MongoDB at a glance
As apps demand richer models, temporal logic, and AI retrieval, document-centric systems with external subsystems hit composability limits.
MongoDB splits document, search, and vector execution across separate paths. SurrealDB runs everything in one distributed engine with consistent semantics.
Business-critical capabilities
MongoDB: Document-first database with no relational query planner or cost-based join optimisation. Limited graph traversal and no first-class temporal semantics.
SurrealDB: Native document, relational, graph, vector, full-text, and temporal querying in one engine.
Platform openness and composability
MongoDB: Search and vector queries run through Atlas Search (Lucene-based) and must appear first in pipelines, limiting composability.
SurrealDB: All query primitives are first-class and fully composable within a single execution plan.
Cost and performance
MongoDB: Contention increases transaction overhead, and separate core vs. search/vector execution adds complexity.
SurrealDB: Unified execution reduces coordination overhead, hardware usage, and system sprawl.
SurrealDB delivers enterprise-grade correctness and consistency
ACID across all data models
MongoDB supports ACID transactions, but they're limited, costly under contention, and don't span search, vectors, or graph traversal.
SurrealDB: provides ACID transactions natively across all models in one engine.
No locking, predictable concurrency
MongoDB uses coordination-heavy concurrency control that adds overhead under concurrent workloads.
SurrealDB: avoids global and table-level locks for concurrency, enabling predictable reads and writes.
Temporal correctness by design
MongoDB lacks temporal graph traversal - time filters can't participate directly in traversal semantics.
SurrealDB: supports native temporal graph querying with time constraints built into traversal logic.
Enterprise takeaway
MongoDB is optimised for document-centric workloads.
SurrealDB: is optimised for correctness, composability, and consistency across complex, evolving datasets.
SurrealDB is open and unified by design
One engine, not fragmented subsystems
MongoDB runs full-text and vector search through Atlas Search (Lucene) outside core CRUD, without full transactional consistency.
SurrealDB: executes documents, graphs, vectors, and full-text search in one distributed engine.
Indexing without structural limits
MongoDB supports full-text indexing, but Atlas Search runs as a separate Lucene engine with delayed consistency and added overhead. Compound indexes are also limited with nested arrays, limiting efficient indexing of deeply nested or multi-array document models.
SurrealDB: provides native full-text search and indexing across arrays, nested fields, vectors, and relationships without these constraints.
Flexible schema without trade-offs
MongoDB is a schema-optional database; its schema validation is enforced only at write time, does not encode relationships or retroactively protect existing data, and reduces performance.
SurrealDB: allows you to start schemaless and progressively enforce schemas with DB-level guarantees, with native enforcement, preserving relationships and full transactional consistency.
Platform takeaway
SurrealDB: replaces fragmented document-oriented architectures with a single, consistent platform capable of executing application and AI workloads end to end, including native support for custom in-database logic such as triggers, events, and procedural logic.
Feature-by-feature comparison
Business model
| MongoDB | SurrealDB |
|---|---|
| Source-available, with key capabilities gated behind commercial offerings and Atlas-managed services. | Open source and available for use. Commercial offerings build on the same core engine without fragmenting capabilities. |
Availability
| MongoDB | SurrealDB |
|---|---|
| Runs locally, on-premises, and in major clouds, but advanced search, vectors, and scaling mainly depend on Atlas, with limited self-managed support. | Runs locally, on-premises, and on all major public clouds. Deployable as embedded, single-node, or distributed. |
Architecture
| MongoDB | SurrealDB |
|---|---|
| Document-first with limited graph traversal and no planner-driven relational semantics. Secondary views, FTS and vectors require duplicated data and separate async pipelines. | Unified, distributed multi-model engine. Query execution, indexing, storage, and transactions operate inside a single system. Designed for mixed read and write workloads. |
Scale
| MongoDB | SurrealDB |
|---|---|
| Scales through sharding, which introduces operational complexity. Transaction coordination overhead increases as scale and concurrency grow. | Horizontally scalable across reads and writes. No sharding required for query semantics. Designed for large, continuously evolving datasets. |
Resilience
| MongoDB | SurrealDB |
|---|---|
| Separate subsystems introduce additional operational dependencies. Search and vector results are not transactionally consistent with core data. | Distributed execution without dependency on separate subsystems. Failures do not fragment query correctness or transactional consistency. |
Transactional consistency
| MongoDB | SurrealDB |
|---|---|
| ACID transactions are limited, costly under contention, and don't extend to search, vectors, or graph traversal. | ACID transactions across documents, relational joins, graph traversal, vector search, and full-text search. No global or table-level locks. |
Models
| MongoDB | SurrealDB |
|---|---|
| Document-first. Graph traversal support is limited and non-composable. Relational semantics are limited and not planner-driven. | Native support for document, relational, graph, key-value, time-series, vector, full-text search, and geospatial access patterns in one engine. |
Temporal capabilities
| MongoDB | SurrealDB |
|---|---|
| No temporal graph querying. Time filters cannot participate directly in traversal semantics. | Temporal querying is first-class. Time constraints participate directly in graph traversal and filtering. |
Indexing
| MongoDB | SurrealDB |
|---|---|
| Compound indexes may include only one array field. Deeply nested or multi-array document models cannot be efficiently indexed. | Indexing across arrays, nested fields, vectors, and relationships without compound multikey restrictions. |
Query execution
| MongoDB | SurrealDB |
|---|---|
Queries split into multiple stages, with $search required first. There's no unified planner across document, $search, vector, and graph operations, increasing latency from execution boundaries and data handoffs. | Single declarative query language with a unified execution plan. All retrieval primitives are co-planned and optimised together. |
Pricing
| MongoDB | SurrealDB |
|---|---|
| Costs increase with sharding, coordination overhead, Atlas dependency, and duplicated subsystems. Operational complexity grows as scale increases. | Straightforward pricing with architectural efficiency reducing infrastructure and operational cost as scale grows. Lower TCO than MongoDB Atlas at comparable scale. |
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