Key Takeaways
- The best database for a knowledge graph is a graph database that can model entities and relationships natively, traverse many hops quickly, and plug into the AI stack that builds and queries the graph.
- For knowledge graphs that feed AI agents and GraphRAG: FalkorDB. It runs in memory with disk persistence, uses OpenCypher, has native vector search, and ships GraphRAG-SDK to build graphs from unstructured text.
- For general-purpose graph projects with a large existing ecosystem: Neo4j.
- For teams that want documents, key-value, and graphs in one engine: ArangoDB.
- For AWS-only teams that need RDF and SPARQL: Amazon Neptune.
Graph Databases for Knowledge Graphs Compared
| Database | Data Model | Query Language | Storage | Vector Search | Multi-Tenancy | GraphRAG Tooling | Deployment | License |
|---|---|---|---|---|---|---|---|---|
| FalkorDB | Property graph | OpenCypher | In-memory with disk persistence | Native | 10,000+ graphs per instance | GraphRAG-SDK, LangChain, LlamaIndex | Cloud, self-hosted, Docker | SSPLv1 |
| Neo4j | Property graph | Cypher | Disk-based with page cache | Native | Multiple databases (Enterprise) | GraphRAG package, LangChain, LlamaIndex | Cloud, self-hosted | GPLv3 (Community), commercial (Enterprise) |
| ArangoDB | Multi-model (documents, key-value, graph) | AQL | Disk-based (RocksDB) | Native | Multiple databases | LangChain integration | Cloud, self-hosted | BSL 1.1 |
| Amazon Neptune | Property graph and RDF | openCypher, Gremlin, SPARQL | Managed AWS storage | Neptune Analytics | Separate clusters, or tenant IDs in a shared graph | Bedrock, LangChain | AWS only | Commercial service |
Why AI Needs Knowledge Graphs
A knowledge graph stores information as entities and the relationships between them. A relational database might keep one table of movies and another of actors. A knowledge graph links them directly: Director A filmed Movie B, which stars Actor C.
LLMs predict text well but have no reliable source of facts about your data. When they lack context, they can produce false answers. Knowledge graphs can fix this problem by giving them structured, verifiable context to retrieve at query time. This is Graph Retrieval-Augmented Generation (GraphRAG).
Knowledge graphs help AI in three ways:
- Contextual accuracy: the model follows relationships to the answer instead of guessing from word patterns.
- Explainability: you can trace the path the system took to reach an answer.
- Unified data: siloed data from across an organization connects into one view.
Why use a graph database?
A knowledge graph can only be stored in a specialized graph database. Knowledge graph queries follow relationships across many hops. In a relational database, every hop is a JOIN, and the cost grows quickly as queries go deeper. A graph database stores relationships directly, so multi-hop queries stay fast as the graph grows.
How to Choose a Database for Your Knowledge Graph
Judge each option on the work a knowledge graph actually involves: building it, keeping it current, and querying it for AI.
- Data model. Property graphs (nodes and relationships with properties) suit most application and AI work. RDF suits linked-data and semantic web standards. Pick the model your team and tools already use.
- Schema and ontology support. You need a way to define entity types and relationship types, and to evolve them as the domain changes.
- Building from unstructured data. Most knowledge graphs start from documents. Look for tooling that extracts entities and relationships with an LLM and loads them into the graph.
- Entity resolution. The same company or person appears under different names. The database and its tooling should make it easy to merge duplicates.
- Multi-hop query speed. GraphRAG and agents ask questions that cross several relationships. Latency at p99, not just the average, decides whether answers feel real-time.
- Vector and graph together. Retrieval often starts with vector similarity and then expands through relationships. Native vector search avoids running and syncing a second system.
- Isolation between tenants. SaaS products and per-user agent memory need separate graphs without a database per tenant.
- Deployment. Check for a managed cloud option, self-hosting, and a local setup for development.
Leading Graph Databases for Knowledge Graphs
Here is how the leading options compare on those criteria.
FalkorDB
FalkorDB is a graph database built for knowledge graphs that serve AI agents and GraphRAG. It is the direct successor to RedisGraph, which reached end of life in January 2025, and is built by the team that created it. FalkorDB extended that technology into a standalone graph database focused on AI workloads.
FalkorDB represents graphs as sparse matrices and runs queries with linear algebra (GraphBLAS) instead of following pointers node by node. It runs in memory for millisecond latency and persists data to disk for durability and recovery. Its core engine has been rewritten in Rust.
What it offers for knowledge graphs:
- Low latency on multi-hop queries. In FalkorDB’s benchmark against Neo4j, FalkorDB returned queries in 36 ms at p50 and 83 ms at p99. Neo4j returned 469 ms and 41,157 ms. On a graph of 820K nodes and 9.8M edges, FalkorDB ran PageRank in 18.53 ms (Neo4j: 417.31 ms) and Weakly Connected Components in 17.8 ms (Neo4j: 1,324.32 ms). FalkorDB used 100 MB of memory to Neo4j’s 600 MB.
- GraphRAG-SDK. Builds knowledge graphs from unstructured text with an LLM, including automatic ontology detection, then queries them in natural language.
- OpenCypher. The standard query language for property graphs, so existing Cypher skills and queries carry over.
- Native vector search. Vector indexes live alongside the graph, so one query can find similar nodes and expand through their relationships.
- Multi-tenancy. 10,000+ isolated graphs in one instance, for SaaS products and per-user or per-agent memory.
- Agent memory integrations. Works as the graph backend for Graphiti and Mem0, with an MCP server for agents.
FalkorDB Pricing
FalkorDB offers four plans (see plans):
- Free: multi-graph and multi-tenancy, graph access control, and community support.
- Startup: from $73/month for 1 GB. Adds TLS and automated backups every 12 hours.
- Pro: from $350/month for 8 GB. Adds cluster deployment, high availability, multi-zone deployment, continuous persistence, and business-hours support.
- Enterprise: tailored pricing. Adds VPC, advanced monitoring, custom backups, 24/7 support, and a dedicated account manager.
Neo4j
Neo4j is a long-established general-purpose graph database. Its Desktop app makes local prototyping simple, and its large community means documentation, tutorials, and third-party integrations are easy to find.
Tradeoffs to weigh for knowledge graphs that serve real-time AI:
- JVM overhead. Neo4j runs on the Java Virtual Machine. The JVM needs memory of its own, and garbage collection can pause the database briefly, which shows up as latency spikes at high percentiles.
- Write scaling. In a cluster, each database accepts writes through a single primary, so heavy ingestion is limited by one server.
- Edition costs. Clustering and hot backups are part of the commercial Enterprise Edition. The Community Edition is GPLv3.
For a closer look at AI workloads, read FalkorDB vs Neo4j: choosing the right graph database for AI.
PAGE RANK
Graph Algorithm
Speed: 22x faster
WCC
Weakly Connected Components
Speed: 74x faster
LATENCY
(Lower is Better)
Superior: 496x faster
MEMORY USAGE
(Lower is Better)
6x less memory
ArangoDB
ArangoDB is a multi-model database. It stores documents (JSON), key-value pairs, and graphs in one engine, so user profiles can live as documents and their connections as graph edges in the same system.
Tradeoffs to weigh:
- Graph traversal via indexes. ArangoDB follows relationships through an edge index rather than direct references between nodes. Deep multi-hop traversals involve repeated index lookups.
- Its own query language. ArangoDB uses AQL rather than Cypher, so Cypher skills and queries do not carry over.
- Sharding across servers. When a traversal crosses shards, each hop can mean a network call. ArangoDB’s SmartGraphs feature reduces this by keeping related data together.
- License. Since version 3.12, ArangoDB source code is licensed under the Business Source License 1.1.
Amazon Neptune
Amazon Neptune is a fully managed graph database on AWS. It supports both major graph models: property graphs queried with openCypher or Gremlin, and RDF queried with SPARQL. That makes it a fit for teams that need RDF and semantic web standards inside AWS.
Tradeoffs to weigh:
- AWS only. Neptune runs only as an AWS service, so you cannot run the same database locally or on another cloud.
- Pricing model. The standard configuration charges for storage I/O on top of instance hours, which makes costs depend on query patterns. Neptune I/O-Optimized removes I/O charges in exchange for higher instance and storage prices.
- Network latency. Applications reach Neptune over network endpoints, which adds round-trip time to every query compared with an in-memory database running next to the application.
Final Thoughts
Every database here has a place. If your knowledge graph feeds AI agents or GraphRAG, the deciding factors are multi-hop latency, vector and graph retrieval in one system, and tooling that builds the graph from your documents. FalkorDB was built for that workload.
You can start with a local proof of concept and scale to a production cluster without changing databases. Run it yourself with Docker, or use a managed instance on FalkorDB Cloud.
Ready to build?
Run FalkorDB locally with docker run -p 6379:6379 falkordb/falkordb or start free on FalkorDB Cloud (no credit card required).
Then follow the tutorial to build your first knowledge graph.
Frequently Asked Questions
What is the best database for knowledge graphs?
A graph database is the best fit, because knowledge graphs are made of relationships. For knowledge graphs that serve AI agents and GraphRAG, FalkorDB offers low multi-hop latency, native vector search, and GraphRAG-SDK. Neo4j suits general-purpose projects that rely on its ecosystem, and Amazon Neptune suits AWS teams that need RDF.
What is a knowledge graph?
A knowledge graph stores information as entities connected by explicit relationships. Instead of separate tables for movies and actors, it links them directly, so a system can follow how facts connect. That structure lets an LLM retrieve verifiable context, which is the basis of GraphRAG.
Can I use a relational database like MySQL for a knowledge graph?
Not effectively. A knowledge graph needs a graph database, because in a relational database every relationship hop becomes a JOIN. Queries that cross several hops get slower and harder to write as the graph grows. A graph database stores relationships directly and keeps those queries fast.
How does FalkorDB compare to Neo4j?
Both support Cypher and both are used for knowledge graphs. Neo4j runs on the JVM and stores data on disk with a page cache. FalkorDB runs in memory with disk persistence and uses sparse matrix algebra for traversals. In FalkorDB’s benchmark, on 820K nodes and 9.8M edges, FalkorDB ran PageRank in 18.53 ms against 417.31 ms for Neo4j, and Weakly Connected Components in 17.8 ms against 1,324.32 ms. Query latency was 36 ms versus 469 ms at p50, and 83 ms versus 41,157 ms at p99. FalkorDB used 100 MB of memory where Neo4j used 600 MB. Neo4j’s strengths are its maturity, community, and tooling. FalkorDB’s strengths are low latency on multi-hop queries, multi-tenancy, and native GraphRAG tooling.
Does FalkorDB persist data to disk?
Yes. FalkorDB runs in memory for speed and writes data to disk for durability and recovery.
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