Software advice
Best AI Graph Databases in 2026
An AI graph database is a database that stores data as nodes and relationships and adds features for language models, such as vector search, GraphRAG retrieval, agent memory or natural language query. It is not a vector database alone, and it does not produce answers without a language model on top. Its job is to give an AI system connected, checkable context.
Quick answer
Neo4j ranks first for teams that want a managed graph with a free tier and published audit reports. TigerGraph is the pick for graph plus vector search on posted prices, and Stardog suits enterprise knowledge graphs.
A buyer of an AI graph database chooses between a graph engine that added vector search and AI memory, a multi-model database that includes graph, and a semantic store built for linked enterprise data. The question behind GraphRAG is simple: can the database return connected facts that a language model can cite? This ranking judges how plainly each vendor describes its AI features, what the entry price covers, whether a free route exists, what security evidence is public and how hard a move away would be. Checked on vendor pages in November 2026. Several vendors publish little, and the entries say so instead of guessing.
The 10 best AI graph databases in 2026, ranked
There is no score. Vendors can pay for more visibility on this website, for example a higher place in a ranking or more room on a page. Commercial relationships can also affect which products we feature and how prominently. How we rank.
| # | Tool | Best for | Free plan | Entry price |
|---|---|---|---|---|
| 1 | Neo4j | Teams that want a managed property graph with a free tier and published compliance reports | Yes | AuraDB Professional from $0.09 per GB per hour |
| 2 | TigerGraph | Teams that want graph and vector search in one engine with a free single-server edition | Yes | Savanna from $45 per GB per month |
| 3 | Stardog | Enterprises building a governed knowledge graph over many data sources with inference | Yes | Quoted |
| 4 | FalkorDB | Developers wiring graph memory into LLM agents with LangChain, LlamaIndex or MCP | Yes | Startup from $73 per GB per month |
| 5 | Memgraph | Teams that want an open source in-memory graph database with GraphRAG and AI memory features | Yes | Quoted |
| 6 | ArangoDB (now Arango) | Teams that want one multi-model engine routing questions to graph, vector or document retrieval | No | Quoted |
| 7 | SurrealDB | Developers who want one database for documents, graph, vectors and agent memory | Yes | Start plan: first instance free, further instances from $0.021 per hour |
| 8 | GraphDB (Graphwise) | Organizations in semantic web and linked data work that want a managed semantic graph store | No | Quoted |
| 9 | Amazon Neptune | Teams already on AWS that want a managed graph database billed hourly | Trial | Neptune Database free trial for 30 days, then hourly on-demand rates |
| 10 | Azure Cosmos DB | Microsoft Azure teams that want graph queries through the Gremlin API inside a multi-model service | Yes | Quoted |
- 1
Neo4j
Neo4j leads this list because it pairs a free managed tier with hourly metered paid capacity, so a graph project can start at $0 and grow without a sales call. The pricing page names Aura Agent, Graph Analytics and vector indexing, though it does not describe GraphRAG itself. Its trust center publishes audit reports, which few rivals match.
Strong on
- AuraDB Free needs no credit card or other payment method
- Trust center lists ISO/IEC 27001:2022, SOC 2 Type 2, SOC 3 and CSA STAR Level 1
- Pricing page lists vector indexing next to Graph Analytics and Aura Agent
Weak on
- Pricing page does not name GraphRAG or LLM integrations, so confirm those with the vendor
- Security page states no EU hosting option or statement on AI training with customer data
- Hourly metering by provisioned capacity makes monthly cost harder to forecast than a flat fee
Skip it if: Buyers who need a stated headquarters, EU hosting commitment or written promise about AI training before signing. Metered hourly on provisioned capacity with a 1 GB minimum instance; AuraDB Free costs $0.
- 2
TigerGraph
TigerGraph ranks second because its pricing page posts every paid tier in dollars and lists hybrid graph and vector search on the trial, Savanna and Community tiers. Community is free on one server. GraphRAG and agentic AI appear only as navigation labels, so the AI story rests on the vector search and ML library that the page does list.
Strong on
- Community edition is free on a single server with up to 300 GB of graph and vector storage
- Hybrid graph and vector search is listed on the trial, Savanna and Community tiers
- Three dollar prices are posted: $0 trial, $45 Savanna and $126 Business Critical
Weak on
- GraphRAG and agentic AI have no description on the pricing page
- Per-GB monthly rates rise steeply between Savanna and Business Critical
- Trust and security page was not checked for this ranking
Skip it if: Teams that need a documented GraphRAG workflow or a stated data-handling policy from the pricing page alone. Usage-based; Savanna Free Trial is $0 and Business Critical is $126 per GB per month.
- 3
Stardog
Stardog takes third place as the enterprise knowledge graph choice with a real free route. Stardog Free is a renewable one-year license that may be used commercially. The platform bundles graph machine learning, an NLP pipeline and an inference engine. Its pricing page never mentions GraphRAG, LLMs or vector search, so the AI angle here is reasoning over linked data.
Strong on
- Stardog Free is a renewable one-year license allowed for commercial use
- Built-in inference engine, NLP pipeline and graph machine learning
- REST API gives programmatic access to the unified data layer
Weak on
- Free edition lacks high availability, caching, backups and the full connector set
- Enterprise pricing is unpublished and the billing model is not stated
- Pricing page does not mention GraphRAG, LLM or vector search
Skip it if: Developers who want self-serve metered pricing and documented vector or GraphRAG features at the start. Enterprise pricing is not published and needs a sales call; the free license is renewed yearly.
- 4
FalkorDB
FalkorDB sits fourth because it describes the AI use most directly: GraphRAG, agentic memory that persists across sessions, and integrations with LangChain, LlamaIndex, MCP and Graphiti. A free tier supports building an MVP. Billing by memory size means the per-GB rate is high for large graphs, and nothing checked covers its security posture or certifications.
Strong on
- Free tier covers multi-graph and multi-tenancy with community support
- Lists GraphRAG, agentic memory and MCP, LangChain, LlamaIndex and Graphiti integrations
- Prices are posted: Startup $73 per GB, Pro $350 for 8 GB
Weak on
- Memory-based billing makes large graphs expensive per GB
- Security page returned an error, so certifications were not verified
- Free tier comes with community support only, no paid support
Skip it if: Companies that need audited certifications or a stated headquarters before the first pilot. Billed by memory size; the Pro plan starts at $350 for 8 GB per month.
- 5
Memgraph
Memgraph ranks fifth as the open source option. Community Edition is free, an enterprise trial lasts 30 days, and the page says no per-query or replica charges apply. The AI section lists GraphRAG, vector search, AI memory and agentic AI. No dollar entry price is published on the page, so budgeting needs a quote or the calculator.
Strong on
- Community Edition is described as free and open source
- No per-query or replica charges are stated, pricing scales with memory
- AI section covers GraphRAG, vector search, AI memory and agentic AI
Weak on
- No dollar entry price appears on the pricing page
- Security page returned an error, so certifications and hosting were not checked
- Memory-scaled pricing ties cost to the size of the working graph
Skip it if: Buyers who want a posted monthly price and a documented compliance list before talking to sales. Community Edition is free and open source; Cloud instances run from 1 GB to 32 GB RAM.
- 6
ArangoDB (now Arango)
Arango, the company formerly marketed as ArangoDB, comes sixth. The site describes GraphRAG, Deep Search that routes a query to graph, vector or document retrieval, and AQLizer for turning plain questions into AQL. It lists an HTTP API, drivers and MCP integrations. Without a reachable pricing page, cost and free options remain unknown in this ranking, so treat its place as provisional.
Strong on
- Deep Search routes a query to graph, vector or document retrieval
- AQLizer turns natural language questions into AQL queries
- Docs list an HTTP API, official drivers and pre-built MCP integrations
Weak on
- No pricing page was reached, so plans and free options are unconfirmed
- Security page states no certifications, hosting or AI-training policy
- Rebranding to Arango means older ArangoDB links and articles may point to outdated pages
Skip it if: Buyers who need published prices or a free tier confirmed before they shortlist a vendor. No pricing page was reached; the old ArangoDB pricing address redirects to the Arango site.
- 7
SurrealDB
SurrealDB lands seventh because it is a multi-model database more than a pure graph product. Its Start plan gives one free instance, vector search indexing is on every plan, and Agent Memory records what an agent learned with its source and time. Models that support MCP can reach the database directly, which suits agent builders.
Strong on
- Vector search indexing is included on all plans
- Agent Memory stores what an AI agent learned with source and time
- Start plan gives the first instance free with 1 GB of storage
Weak on
- Compute and storage are billed separately, which complicates forecasting
- Privacy policy page returned an error, so AI-training and hosting terms were not checked
- The site lists two different postal addresses, so confirm the company address
Skip it if: Teams that need a dedicated graph analytics engine rather than a general multi-model database. Compute and storage are priced separately; the free instance has 1 GB of storage.
- 8
GraphDB (Graphwise)
GraphDB, shown on the Graphwise site as a semantic graph database for enterprise data, ranks eighth. Graphwise describes GraphRAG as a context-aware retrieval workflow engine and runs an AI Accelerator Program for customers. The pages checked give no price, free edition or hosting terms, which makes it hard to compare against posted-price rivals on cost.
Strong on
- Presented as a semantic graph database for storing enterprise data
- GraphRAG described as a context-aware retrieval workflow engine
- Offices listed in Vienna, Sofia and New York
Weak on
- No price, trial or free edition is stated on the pages checked
- Product page for GraphDB was not read separately from the homepage
- Data handling and certifications were not checked at all
Skip it if: Buyers who need self-serve signup, public pricing or a free tier to test with. No price, free edition or trial terms are stated on the pages checked.
- 9
Amazon Neptune
Amazon Neptune ranks ninth because it is built for AWS customers rather than for GraphRAG work. The pricing page offers a 30-day trial and hourly billing with no upfront fee or long-term commitment. Neptune Analytics is an in-memory graph engine billed per m-NCU hour with vector search listed. GraphRAG and Amazon Bedrock integration are not mentioned there.
Strong on
- 30-day trial with 750 hours, 10 million I/O requests and 1 GB storage
- On-demand hourly billing with no upfront fee or long-term commitment
- Neptune Analytics lists vector search among its capabilities
Weak on
- Pricing page does not mention GraphRAG or a Bedrock integration
- Storage, I/O and instance hours are billed separately, so cost needs an estimate
- No permanent free plan after the 30-day trial ends
Skip it if: Teams outside AWS or those that want a free tier that does not expire after 30 days. The trial covers 750 hours monthly on db.t3.medium or db.t4g.medium; later rates are on the pricing page.
- 10
Azure Cosmos DB
Azure Cosmos DB closes the list because its graph support is the Gremlin API inside a broader database service. The page offers a free tier and serverless billing, but its dollar fields were placeholders, and it mentions a semantic reranker rather than vector search or GraphRAG. It suits Azure shops that accept request-unit billing and a graph feature set shaped by Gremlin.
Strong on
- Free tier available on one account per subscription
- Provisioned throughput is billed hourly per RU/s, and serverless is also offered
- Gremlin API is billed on request units and consumed storage
Weak on
- Free tier figures differ on the same page: 1,000 RU/s and 25 GB versus 400 RU/s and 5 GB
- Price fields showed placeholders, so no dollar entry price could be confirmed
- Page mentions neither vector search nor GraphRAG for the Gremlin API
Skip it if: Teams that want a graph-first product with a clear GraphRAG story or a predictable flat price. Dollar amounts show as placeholders on the fetched page; billing is per RU/s, with serverless also offered.
How these AI graph databases were judged
How well do AI graph databases support GraphRAG and agent memory?
Support for GraphRAG varies from a described feature to a navigation label. FalkorDB lists GraphRAG, agentic memory and MCP integrations. Memgraph places GraphRAG beside vector search and AI memory. Arango describes Deep Search and AQLizer, and SurrealDB sells Agent Memory. TigerGraph shows GraphRAG only as a menu item, and Stardog's pricing page does not mention it. No pricing page read for this ranking publishes retrieval accuracy figures, so run your own questions against your own graph, and score the answers against source records, before you commit.
How much does an AI graph database cost?
Pricing shapes differ more than amounts, as of November 2026. Neo4j AuraDB Professional starts at $0.09 per GB per hour, TigerGraph Savanna at $45 per GB per month and FalkorDB Startup at $73 per GB per month. SurrealDB bills further instances from $0.021 per hour. Memgraph scales with memory, and Neptune and Cosmos DB follow cloud meters. Stardog and Graphwise publish no enterprise price. Estimate the size of your graph in memory first, because several vendors charge by it, and ask whether replicas, backups and support are inside the quoted rate.
Where is my graph data stored, and is it used for AI training?
Public evidence on data handling is thin for this category. Neo4j's trust center lists ISO/IEC 27001:2022, SOC 2 Type 2, SOC 3 and CSA STAR Level 1, yet its security page states no EU hosting or AI-training policy. Security or privacy pages for Memgraph, FalkorDB and SurrealDB returned errors when checked, and Arango's page lists no certifications. The vendors' policies were not verified for the rest. Ask each vendor in writing about hosting region, retention, backups and model training, and keep the answers with the contract.
How hard is it to leave a graph database?
Leaving a graph database costs more than exporting rows. Query languages differ across products, so queries, stored procedures and indexes must be rewritten. Arango uses AQL, and the Cosmos DB graph API uses Gremlin. Cloud services add a second tie: Neptune and Cosmos DB bind you to AWS or Azure billing and networking. Open source editions, such as Memgraph Community, let you run the same engine yourself. Test a full export and a reload into a second system before production, and time how long it takes on a realistic data volume.
Which AI graph database fits my team and data size?
Match the product to who runs it. A developer team prototyping an agent can start on a free tier at Neo4j, TigerGraph, FalkorDB or SurrealDB. A single-server edition such as TigerGraph Community suits larger experiments, up to 300 GB. An enterprise modeling business meaning across sources should look at Stardog or Graphwise. Teams standardized on a cloud may prefer Neptune or Cosmos DB for procurement reasons, even with a thinner AI story, and then add vector search or retrieval tooling from other parts of the same cloud.
How to choose an AI graph database in three steps
- Write down the question the graph must answer. List five questions your users or agents will ask, such as which suppliers share an owner or which policy a clause belongs to. Each should need a connection across records, not a lookup in one table. If most questions are lookups, a vector database or a relational store may do the job. Pair every question with the expected answer and its source record. Those pairs become your test set later, and they show whether a graph is worth its modeling effort.
- Load a slice of real data on a free tier. Use a free route to test on your own records: AuraDB Free, a TigerGraph trial, the FalkorDB free tier, SurrealDB Start or Memgraph Community. Load a slice of one or two entity types, run your five questions, and note setup time. Watch how much modeling work each product needs before the first useful answer, and how readable the error messages are when a query fails. Record which queries you wrote by hand and which the vendor's AI features generated, since that gap predicts your ongoing effort.
- Check memory sizing and the exit before you commit. Estimate the graph size in memory and multiply by the vendor's per-GB rate, because TigerGraph, FalkorDB and Memgraph tie price to memory or storage. Then ask for the export format and a tested path to reload elsewhere. Request written answers about hosting region, retention and model training, since public pages are thin. Record who owns the vendor relationship internally so a renewal does not catch the team unprepared a year later, and calendar the notice date and the export test in the same place.
Frequently asked questions
What is the best AI graph database in 2026?
On this ranking Neo4j is first for teams that want a managed graph with a free tier and published audit reports. TigerGraph is second for graph plus vector search at posted prices, and Stardog third for enterprise knowledge graphs. The right choice depends on data size, cloud, and how much GraphRAG tooling you need built in.
What is a graph database used for in AI?
A graph database gives a language model connected facts instead of isolated text chunks. In GraphRAG, the system retrieves related entities and relationships, then passes them to the model as context. FalkorDB, Memgraph and Arango describe this use. It helps where answers depend on links, such as ownership chains or dependencies.
Is there a free graph database for AI projects?
Several vendors offer a free route, as of November 2026. Neo4j lists AuraDB Free at $0 with no card, and TigerGraph Community is free on one server up to 300 GB. FalkorDB has a free tier, SurrealDB gives the first Start instance free, and Memgraph Community is open source. Free routes usually exclude support and high availability.
What is the cheapest AI graph database to start with?
The cheapest start is a free tier, which Neo4j, FalkorDB, SurrealDB, TigerGraph and Memgraph all offer in some form. For paid use, SurrealDB lists $0.021 per hour for extra instances, Neo4j Professional $0.09 per GB per hour, and TigerGraph Savanna $45 per GB per month. Compare against your memory footprint.
Which graph database works with LangChain, LlamaIndex or MCP?
FalkorDB lists integrations with LangChain, LlamaIndex, MCP and Graphiti on its pricing page. Arango lists pre-built MCP integrations in its docs menu, and SurrealDB says MCP-capable models can reach the database and its Agent Memory. For others, check the vendor's integration docs, because the pages read for this ranking did not state it.
Is a vector database enough, or do I need a graph database?
A vector database finds text that sounds similar, while a graph database follows explicit relationships. If your questions are about meaning alone, a vector database may be enough. If they depend on links between entities, add a graph. TigerGraph, Neo4j, SurrealDB and Neptune Analytics list vector features beside graph storage.
Does an AI graph database send my data to a language model?
That depends on which features you switch on. The database itself stores and queries data, but features such as natural language query or AI agents may call an external model. Most pricing pages read for this ranking do not state where those calls go. Ask the vendor which models are used and whether your data trains them.
Should I pick Amazon Neptune or Azure Cosmos DB for GraphRAG?
Pick either only if your team is already tied to that cloud. Neptune lists a 30-day trial and Neptune Analytics with vector search, while Cosmos DB offers a free tier and the Gremlin API. Neither pricing page read here describes GraphRAG. A graph-first product usually documents more of it.