Memgraph alternatives: 4 tools compared (October 2026)

Neo4j is a graph database that stores data as nodes and relationships and adds vector search and GraphRAG tools for AI applications. Companies use it for knowledge graphs, fraud detection, recommendations and grounding language models in connected data.

Key functions

• Vector indexes for semantic search inside the graph
• GraphRAG Python package combines knowledge graph and vector search for language models
• Aura Agent builds GraphRAG agents on your own graph data

Compared with Memgraph

• Publishes a price (from $65 per month); Memgraph quotes on request.
• Names 5 security certificates, Memgraph 0.
• Different pricing model: usage-based instead of contact for pricing.

Check before buying: Choose the cloud provider and region when creating an AuraDB instance to meet data residency needs. SSO, fine-grained access control and customer-managed keys require Business Critical or higher. Budget by memory size, since prices scale per GB. If a graph holds employee or customer relationships, check the purpose limitation under the GDPR.

FalkorDB is a graph database designed for GraphRAG, agent memory and other AI workloads that need fast relationship queries. It uses sparse matrices and linear algebra for queries and supports vector search alongside OpenCypher.

Key functions

• Vector similarity, full-text and range indexes in one engine
• Knowledge graphs for low-latency GraphRAG with language models
• Many separate graphs per instance for multi-tenant AI applications

Compared with Memgraph

• Publishes a price (from $73 per month); Memgraph quotes on request.
• Different pricing model: usage-based instead of contact for pricing.
• Strongest elsewhere: United States.

Check before buying: Review the Server Side Public License before offering FalkorDB as part of a hosted service, since it places conditions on providing the software as a service. Check which cloud provider and region your plan uses and whether high availability is included, since Startup lacks clustering. Request the SOC 2 report and a data processing agreement.

TigerGraph is a distributed graph database with built-in vector search. Data teams use it to model connected data such as customers, accounts and transactions. Typical uses are fraud detection, customer analytics and graph-based retrieval for AI, run as a managed cloud service or on their own servers.

Key functions

• GraphRAG and hybrid search combining graph and vector queries in GSQL
• Graph Data Science Library with more than 50 algorithms and graph embeddings for similarity and link prediction
• Agentic AI: an MCP server connects AI agents to graph and vector retrieval

Compared with Memgraph

• Publishes a price (from $1 usage-based); Memgraph quotes on request.
• Can store data in the EU; Memgraph states no EU option.
• Names 2 security certificates, Memgraph 0.
• Different pricing model: usage-based instead of contact for pricing.

Check before buying: Graph modeling is a design task, so plan time for schema design and for learning GSQL or openCypher. Savanna prices by instance size and hour, with a regional factor, and the Business Critical tier adds multi-zone availability. The vendor says its HIPAA controls were still awaiting a third-party audit at the time of the page, so verify the current status. Audit reports are available only under a non-disclosure agreement.

LightRAG is an open-source RAG system that extracts entities and relationships from documents into a knowledge graph and uses both the graph and vector search to answer questions. It targets developers who need answers that connect facts across many documents.

Key functions

• Builds a knowledge graph of entities and relations from documents automatically
• Combines graph and vector retrieval, with a reranker as default for mixed queries
• Parses multimodal content through MinerU or Docling

Compared with Memgraph

• Strongest elsewhere: Hong Kong.

Check before buying: Building the graph sends every document through a language model, so indexing costs grow with the volume of text; test on a sample first. Choose a model provider whose data terms fit your documents, or run open models locally. There is no vendor support contract, so plan who maintains the installation and its storage backends.