KEY ADVANTAGES
Why teams choose SurrealDB over MongoDB
MongoDB started as a document store and later added search, vector, and limited graph features through separate systems. SurrealDB runs all of this natively in one engine.
HOW IT COMPARES
SurrealDB vs. MongoDB
As apps demand richer models, temporal logic, and AI retrieval, document-centric systems with external subsystems hit composability limits. SurrealDB runs everything in one distributed engine.
Architecture
MongoDB
Document-first with secondary views, full-text search, and vectors requiring duplicated data and separate async pipelines.
SurrealDB
Unified, distributed multi-model engine. Query execution, indexing, storage, and transactions operate inside a single system.
Models
MongoDB
Document-first. Graph traversal support is limited. Relational semantics are not planner-driven.
SurrealDB
Native support for document, relational, graph, key-value, time-series, vector, full-text search, and geospatial.
Transactional consistency
MongoDB
ACID transactions are limited, costly under contention, and don't extend to search, vectors, or graph traversal.
SurrealDB
ACID transactions across documents, relational joins, graph traversal, vector search, and full-text search.
Query execution
MongoDB
Queries split into multiple stages, with $search required first. No unified planner across document, search, vector, and graph operations.
SurrealDB
Single declarative query language with a unified execution plan. All retrieval primitives are co-planned.
Indexing
MongoDB
Compound indexes may include only one array field. Deeply nested or multi-array document models cannot be efficiently indexed.
SurrealDB
Indexing across arrays, nested fields, vectors, and relationships without compound multikey restrictions.
Temporal capabilities
MongoDB
No temporal graph querying. Time filters cannot participate directly in traversal semantics.
SurrealDB
Temporal querying is first-class. Time constraints participate directly in graph traversal.
Pricing
MongoDB
Costs increase with sharding, coordination overhead, Atlas dependency, and duplicated subsystems.
SurrealDB
Unified engine eliminates the need for separate search, vector, and graph systems. Costs scale linearly.
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.
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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.
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Tencent
Unified infrastructure monitoring
Tencent consolidated nine backend tools into one real-time monitoring platform powered by SurrealDB's multi-model context graph.
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PolyAI
High-performance customer service AI powered by RAG
PolyAI connects SurrealDB to Agent Studio for low-latency, customer-controlled RAG across voice AI experiences.
Read case study
FREQUENTLY ASKED QUESTIONS
SurrealDB vs. MongoDB
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