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Memgraph

Open Source

About Memgraph

Memgraph complements vector search with structured, connected context and multi-hop reasoning across enterprise data. The same architecture is used both for AI context and for real-time graph analytics. It uses the Cypher query language and offers Python client libraries along with LangChain and LlamaIndex integrations. It is positioned for migration from Neo4j with familiar interfaces and protocols.

Products

  • GraphRAG: Traverses a knowledge graph to follow multi-hop relationships across entities.
  • AI Memory: Stores and connects semantic, episodic, and procedural long-term memory as a unified graph queryable by AI systems.

Used for

  • Detecting fraud by mining relationships between entities.
  • Unifying siloed data into a queryable knowledge graph.
  • Checking access rules in identity and access management systems.

Who it is for

  • Developers building and evaluating applications with the database.
  • Companies running graph workloads such as fraud detection, network analysis, supply chain, and infrastructure monitoring.
  • Developers and small teams prototyping or working on early-stage projects.
  • Organizations using AI and embedding-heavy workloads.

From Memgraph'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 Memgraph. 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

CIOPages put this listing together from public sources. It’s information, not an endorsement. How we build listings. Work here? Claim this listing or .

Quick Facts

memgraph.com Pricing page
CategoryData & Analytics
SubcategoryDatabase Platforms
PricingUsage-based (memory capacity)

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