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SurrealDB vs.
MongoDB

Migrate from MongoDBContact us
MongoDB. A document database with search, vectors and limited graph features in separate subsystems.
SurrealDB. A unified, transactional database for documents, graphs, vectors, full-text and time-series.
The difference. One transaction spans a document write, a graph edge, a vector and a full-text index.
  1. Babcock
  2. ING
  3. British Airways
  4. Nvidia
  5. Apple
  6. SpaceX
  7. Samsung
  8. adidas
  9. Tencent
  10. Alibaba
  11. PolyAI
  12. Later
  13. Verizon
  14. Liberty Mutual
  15. Walmart
  16. Carrier
  17. Saks Fifth Avenue
  18. San Francisco Compute Company
  19. Shield AI
  20. Wix

01 |THE DIFFERENCE

Where MongoDB adds subsystems, SurrealDB runs natively

MongoDB added search, vector and limited graph features as separate systems. SurrealDB runs all of them in one engine, in one transaction.

MODELS

Relational and graph beside documents

MongoDB is document-first, with limited graph traversal and relational semantics the planner does not drive. SurrealDB carries document, relational, graph, vector and time-series data as first-class models in one engine.

Explore the data models
TRANSACTIONS

ACID across search, vectors and graph

MongoDB transactions cover documents and cost more under contention; Atlas Search and vectors sit outside them. In SurrealDB one transaction spans a document write, a graph edge, a vector and a full-text index.

Explore transactions
INDEXING

Indexes across arrays and nested fields

A compound index in MongoDB may include one array field, so deeply nested and multi-array documents index poorly. SurrealDB indexes arrays, nested fields, vectors and relationships without that restriction.

Explore indexes

02 |THE SAME QUERY

Similar documents, from one team, this quarter. A staged aggregation with a search stage and a lookup in MongoDB. One statement in SurrealQL.

MongoDB Atlas

1// Atlas: $vectorSearch first, then a $lookup, then the filter
2db.documents.aggregate([
3  { $vectorSearch: { index: "vec", path: "embedding", queryVector: q,
4                    numCandidates: 100, limit: 10 } },
5  { $lookup: { from: "people", localField: "authorId",
6               foreignField: "_id", as: "author" } },
7  { $match: { "author.team": team, published: { $gt: since } } },
8  { $project: { title: 1, score: { $meta: "searchScore" } } }
9])

SurrealQL

1-- SurrealQL: the similarity and the path, together
2SELECT
3    title,
4    vector::similarity::cosine(embedding, $q) AS score
5FROM document
6WHERE embedding <|10|> $q
7    AND <-authored<-person->member_of->team CONTAINS $team
8    AND published > time::now() - 90d
9ORDER BY score DESC;

03 |HOW IT COMPARES

How SurrealDB and MongoDB differ

Richer models, temporal logic and AI retrieval push document stores with external subsystems to their composability limits. SurrealDB runs everything in one distributed engine.

Data and relationships

SurrealDB

MongoDB

Models
NativeNative support for document, relational, graph, key-value, time-series, vector, full-text search, and geospatial.
Document-firstDocument-first. Graph traversal support is limited. Relational semantics are not planner-driven.
Indexing
UnrestrictedIndexing across arrays, nested fields, vectors, and relationships without compound multikey restrictions.
RestrictedCompound indexes may include only one array field. Deeply nested or multi-array document models cannot be efficiently indexed.
Temporal capabilities
First-classTemporal querying is first-class. Time constraints participate directly in graph traversal.
NoneNo temporal graph querying. Time filters cannot participate directly in traversal semantics.
Consistency and queries
Transactional consistency
Every modelACID transactions across documents, relational joins, graph traversal, vector search, and full-text search.
LimitedACID transactions are limited, costly under contention, and do not extend to search, vectors, or graph traversal.
Query execution
One planSingle declarative query language with a unified execution plan. All retrieval primitives are co-planned.
StagedQueries split into multiple stages, with $search required first. No unified planner across document, search, vector, and graph operations.
Operations and cost
Architecture
One engineUnified, distributed multi-model engine. Query execution, indexing, storage, and transactions operate inside a single system.
SubsystemsDocument-first with secondary views, full-text search, and vectors requiring duplicated data and separate async pipelines.
Pricing
UsageUnified engine eliminates the need for separate search, vector, and graph systems. Costs scale linearly.
InfrastructureCosts increase with sharding, coordination overhead, Atlas dependency, and duplicated subsystems.

TRUSTED BY

Enterprise teams building on SurrealDB

See more case studies

Three data stores collapsed into one

Samsung

An AI assistant for 10,000 technicians

Verizon

Nine backend tools consolidated into one

Tencent

Low-latency RAG, under the customer's control

PolyAI

45 million recommendation queries a month

Saks Fifth Avenue

MongoDB,answered

MongoDB is a document database that adds search, vector and limited graph features through separate subsystems. SurrealDB is one engine for documents, graphs, vectors, full-text, temporal and relational queries.

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
Read more

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
Read more

Cloud

Dedicated clusters by region, or serverless on SurrealDB Earth.

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

Studio

Query, explore and design the schema from the browser.

A SurrealQL query in Studio and the schema graph under it
Read more

MCP

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

Three models connected through MCP to the database and Agent Memory
Read more

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 MongoDB. Documents, plus the graphs, vectors and SQL MongoDB leaves to other systems.

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