Directory Data & AnalyticsDatabase PlatformsSurrealDB

SurrealDB

Open Source

About SurrealDB

SurrealDB is an advanced multi-model database platform designed to unify documents, graphs, vectors, time-series, and relational data into a single, strongly consistent transactional system. It eliminates the complexity of stitching together multiple specialized databases by providing one query language, one transaction boundary, and one deployment model. This platform is particularly suited for enterprises building AI agents, real-time applications, knowledge graphs, and embedded or edge solutions that require reliable context and memory management.

Targeted at enterprise organizations across industries such as finance, healthcare, gaming, and manufacturing, SurrealDB offers scalable, horizontally distributed storage with compute-storage separation and object storage compatibility. Its unique architecture integrates persistent agent memory and context layers, enabling AI agents to reason over consistent, queryable history without the need for separate memory middleware. This reduces latency, operational overhead, and failure modes associated with managing multiple data systems, delivering a robust foundation for AI-driven intelligent systems.

How to evaluate Database Platforms

CIOPages Research Team evaluation framework for this category — not an assessment of SurrealDB. 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

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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This profile was compiled by CIOPages from public sources with AI assistance, and may be incomplete or out of date. It is informational only and not an endorsement. Represent this vendor? Claim this listing or .