SurrealDB
Open SourceAbout SurrealDB
SurrealDB Cloud is a fully managed offering in a cloud provider and region chosen by the customer. The query language SurrealQL unifies SQL, knowledge graph, vector and full-text search, temporal and key-value queries in one language.
Products
- SurrealDB (Database): A multi-model database that natively supports relational, document, graph, vector and time-series data queried together in one ACID transaction using SurrealQL.
- Agent Memory: A product built on the database that turns conversations, documents and business events into a knowledge graph with provenance.
- Cloud: A managed cloud service running SurrealDB clusters in a chosen provider and region, scaled on demand.
- Studio: A browser-based tool for querying, exploring and designing the schema.
- MCP: A server that lets models speaking MCP reach the database and Agent Memory directly.
Used for
- Combining graph traversal with vector search for grounded, connected context
- Maintaining persistent, queryable memory for agents covering relationships, semantics and recency
- Modeling entities and relationships natively to query structure and meaning together
- Tracking facts that supersede one another while keeping history so an agent knows what changed and when
- Running live queries and change streams so every client sees a write as it commits
Who it is for
- Teams building AI agents that need persistent, governed context.
- Startups building a stack without fragmenting it when adding capabilities.
From SurrealDB's own website, read 2026-10-24. How we build listings.
How to evaluate Database Platforms
This is how the CIOPages Research Team evaluates this category. It is not an assessment of SurrealDB. It comes from our Enterprise Database Platforms buyer guide.
25%
Data Model & Query Fit
Match of the engine to the workload: relational integrity and joins, document/JSON flexibility, key-value latency; transaction isolation levels and ACID guarantees; SQL standard coverage and dialect quirks; secondary indexes, full-text and vector (pgvector / Atlas Search) support; foreign keys and referential integrity where you need them
20%
Consistency, Scale & HA
Single-primary vs. multi-writer; synchronous vs. async replication and resulting RPO; read-replica and sharding story; horizontal write scale and global/multi-region behavior (strong vs. eventual consistency); failover time, achievable RTO, and how partitions are handled under load
20%
Operability & Managed-Service Experience
Quality of the managed offering (RDS/Aurora, Azure SQL, Cloud SQL/AlloyDB) vs. self-managed burden; in-place and major-version upgrade path; backup/PITR, observability, and connection pooling; autoscaling and serverless options; day-2 toil — vacuum/compaction, index bloat, rebalancing — and the depth of the DBA skills it demands
15%
Licensing & Portability
License class and its real constraints: OSI open source (PostgreSQL, MySQL, Valkey, Redis 8 AGPL) vs. source-available (MongoDB SSPL, CockroachDB enterprise/BSL) vs. proprietary (Oracle, SQL Server); cloud lock-in of proprietary engines (Aurora, Spanner, DynamoDB); wire-protocol and SQL-dialect compatibility that preserves an exit; audit exposure on proprietary metrics
12%
Security & Compliance
Encryption at rest and in transit, TLS enforcement, and key management (BYOK/HSM); row- and column-level security and fine-grained RBAC; field-level / queryable encryption for sensitive data; audit logging; data-residency controls and certifications (SOC 2, ISO 27001, HIPAA, PCI, FedRAMP) on the managed service
8%
Ecosystem & Talent
Driver and ORM maturity across your languages; migration and CDC tooling (logical replication, Debezium, DMS); breadth of extensions and integrations; size and hireability of the talent pool; quality of docs and the support path — first-party vendor vs. third-party (EDB, Percona, Crunchy) for open engines
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Quick Facts
surrealdb.com Pricing pageCategoryData & Analytics
SubcategoryDatabase Platforms
DeploymentSelf-hosted, Embedded, Cloud (managed)
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