SurrealDB

Name: SurrealDB
Brand: SurrealDB

AI Native

The multi-model database for AI agents

Est 2021|Valuation $111.4M|Raised $72.8M|31 Ref Customers

LinkedIn 14K|Web traffic 52K|FTEs 39|HQ London, England, GB

MarketsCloud Databases Vector Databases

01Competitive Landscape

| Aspect | SurrealDB | Neo4j | PostgreSQL (+ extensions) | |--------|-----------|-------|---------------------------| | Starting price | Published cloud usage-based hourly pricing; free tier available; dedicated pricing is custom | Varies by deployment (managed and enterprise offerings; community edition available) | Open-source core is free; hosting and managed services incur costs | Most appropriate for | Teams that require a single system to support multiple data models with transactional semantics and mixed workload patterns | Graph-first workloads requiring mature graph tooling and algorithms | Relational-first workloads with a mature ecosystem, broad integrations, and well-established operational practices | Key strengths | Unified multi-model capabilities and transactional semantics across models | Mature graph ecosystem, Cypher query language, and graph analytic tooling | Mature ecosystem, stability, broad integrations, and a large talent pool | Key weaknesses | Ecosystem and tooling maturity compared with incumbents; community discussions highlight debates on stability, performance, and durability configuration | Can be costly at scale and licensing can be complex for some commercial use cases | Requires multiple components to cover graph + vector + realtime features if needed - Direct competitors and differentiators - Neo4j vs SurrealDB - The vendor provides comparison materials that position Neo4j as suitable for mostly static-graph workloads and positions SurrealDB for higher-volume, mixed-model workloads; these are vendor positioning statements and should be validated with workload-specific testing. - Community discussions indicate Neo4j remains stronger for advanced graph algorithms and a more mature graph ecosystem; SurrealDB may be selected when graph is one component of a broader multi-model architecture. - Postgres/Mongo/Redis-style stacks - The product's value proposition includes reducing the need for separate stores for documents, relations, graphs, vectors, and realtime subscriptions; organizations should weigh the trade-offs between consolidation and the operational maturity of specialized systems. - Pricing comparison (limited) - The vendor publishes cloud usage-based per-hour pricing; competitor pricing varies substantially by deployment model and vendor and requires case-specific analysis. - Market share/adoption trends (limited) - Independent press coverage characterizes the product as an emerging alternative with multimodel and vector indexing capabilities; audited customer counts or revenue figures were not available in the reviewed dataset.

01Competitive Landscape

Aspect	SurrealDB	Neo4j	PostgreSQL (+ extensions)
Starting price	Published cloud usage-based hourly pricing; free tier available; dedicated pricing is custom	Varies by deployment (managed and enterprise offerings; community edition available)	Open-source core is free; hosting and managed services incur costs
Most appropriate for	Teams that require a single system to support multiple data models with transactional semantics and mixed workload patterns	Graph-first workloads requiring mature graph tooling and algorithms	Relational-first workloads with a mature ecosystem, broad integrations, and well-established operational practices
Key strengths	Unified multi-model capabilities and transactional semantics across models	Mature graph ecosystem, Cypher query language, and graph analytic tooling	Mature ecosystem, stability, broad integrations, and a large talent pool
Key weaknesses	Ecosystem and tooling maturity compared with incumbents; community discussions highlight debates on stability, performance, and durability configuration	Can be costly at scale and licensing can be complex for some commercial use cases	Requires multiple components to cover graph + vector + realtime features if needed

Direct competitors and differentiators
- Neo4j vs SurrealDB
  - The vendor provides comparison materials that position Neo4j as suitable for mostly static-graph workloads and positions SurrealDB for higher-volume, mixed-model workloads; these are vendor positioning statements and should be validated with workload-specific testing.
  - Community discussions indicate Neo4j remains stronger for advanced graph algorithms and a more mature graph ecosystem; SurrealDB may be selected when graph is one component of a broader multi-model architecture.
- Postgres/Mongo/Redis-style stacks
  - The product's value proposition includes reducing the need for separate stores for documents, relations, graphs, vectors, and realtime subscriptions; organizations should weigh the trade-offs between consolidation and the operational maturity of specialized systems.
Pricing comparison (limited)
- The vendor publishes cloud usage-based per-hour pricing; competitor pricing varies substantially by deployment model and vendor and requires case-specific analysis.
Market share/adoption trends (limited)
- Independent press coverage characterizes the product as an emerging alternative with multimodel and vector indexing capabilities; audited customer counts or revenue figures were not available in the reviewed dataset.

Aspect

SurrealDB

Neo4j

PostgreSQL (+ extensions)

Starting price

Published cloud usage-based hourly pricing; free tier available; dedicated pricing is custom

Varies by deployment (managed and enterprise offerings; community edition available)

Open-source core is free; hosting and managed services incur costs

Most appropriate for

Teams that require a single system to support multiple data models with transactional semantics and mixed workload patterns

Graph-first workloads requiring mature graph tooling and algorithms

Relational-first workloads with a mature ecosystem, broad integrations, and well-established operational practices

Key strengths

Unified multi-model capabilities and transactional semantics across models

Mature graph ecosystem, Cypher query language, and graph analytic tooling

Mature ecosystem, stability, broad integrations, and a large talent pool

Key weaknesses

Ecosystem and tooling maturity compared with incumbents; community discussions highlight debates on stability, performance, and durability configuration

Can be costly at scale and licensing can be complex for some commercial use cases

Requires multiple components to cover graph + vector + realtime features if needed