Do I need a graph database? Framework to evaluate graph dbs

TL;DR: A decision framework for data platform teams evaluating graph databases. In this post we explore when you need a graph database, which category fits your constraints and when the relational baseline is actually the right answer rather than a graph database. Read on for all the details.

Here’s a typical scenario in organizations across the country: Every few months, someone suggests they need a graph database. The reasoning usually goes: “We have entities, we have relationships, we need to traverse them, that’s a graph problem, so we need a graph database.” It sounds right. But having graph-shaped data doesn’t necessarily mean you need graph-shaped infrastructure.

Postgres can take you surprisingly far. Recursive CTEs handle multi-hop traversals. JSONB handles flexible entity properties. Add pgvector and you get hybrid retrieval: Semantic search to find conceptually related entities, then graph traversal to explore their structural connections. For Snowflake users, Snowflake Postgres provides all of this as a managed service with Horizon Catalog governance built in. Learn how we used Snowflake Postgres to build a context graph for agents on Snowflake in this post.

The practical rule of thumb: If your graph fits in a single instance’s working memory, your traversals stay under 3-4 hops and your concurrency isn’t “graph query on every request for thousands of users,” Postgres is probably good enough. Once you’re past any two of the following it’s time to evaluate a dedicated graph database: 

• Multi-million nodes

• 5+ hop traversals

• 100s of simultaneous graph queries

• 100s of interactive graph algorithms

This article is for enterprise teams who’ve reached that point. 

Overall, the market consists of three categories: Dedicated graph databases, cloud-managed graph databases and platform-embedded graphs. Each category makes fundamentally different trade-offs. Read on to learn how they compare.

Neo4j is the product everything else gets compared against in this category. It’s been around for over a decade, it has the largest community ecosystem in the graph space and it’s the graph engine Snowflake partnered with to bring native graph analytics into the AI Data Cloud via the Neo4j Graph Analytics Native App. 

Its core advantage is architectural. Neo4j uses index-free adjacency: Nodes store direct pointers to their neighbors, so traversing an edge is an O(1) pointer chase rather than an O(log n) index lookup. At a small scale, this may not matter. At millions of nodes and tens of millions of edges, it compounds across every hop in every query. 

For workloads like fraud detection in financial networks (billions of transaction edges, 5-7 hop circular money flow detection at sub-100ms latency) or social network analysis (hundreds of millions of nodes, real-time community detection), this architecture is genuinely hard to replicate with relational joins.

The query language, Cypher, is the other major differentiator. A query like MATCH (a)-[:DEPENDS_ON*1..3]->(b)-[:OWNED_BY]->(t:Team) reads almost like English. Variable-length path patterns are native to the syntax. For analysts exploring a graph interactively, asking “what’s connected to this?” and refining in real time, Cypher’s ergonomics are a productivity advantage over writing recursive CTEs in SQL. (Though it’s worth noting that when an LLM agent is generating the queries programmatically, the ergonomic gap narrows considerably.)

Neo4j also ships with the Graph Data Science library, a complete graph algorithm suite. Centrality measures (PageRank, betweenness, closeness), community detection (Louvain, label propagation), similarity scoring (Jaccard, cosine, overlap), path finding and graph embeddings, all running inside the graph engine against live data. No export step. If your product needs interactive graph algorithms, this is where Neo4j pulls furthest ahead of the other players on the market.

AuraDB is the managed service, available across AWS, Azure and Google Cloud marketplaces. If you want Neo4j without operating Neo4j, AuraDB is the path. The multi-cloud availability also makes Neo4j a common choice for organizations that aren’t locked into a single cloud provider.

The trade-off is everything that comes with a dedicated system. Neo4j is another database to operate and secure, separate from your primary data platform. Unless the graph is your system of record, you need ETL to keep it in sync and graph sync pipelines have their own failure modes. Additionally, your team will need to pick up a second query language and you’ll spend integration cycles aligning your warehouse or data lake’s governance model (e.g. Horizon Catalog) with Neo4j’s access control patterns.

For teams where the graph is the product, such as fraud detection tools, recommendation engines, or network analysis solutions, the added overhead of a graph database is justified. For teams where graph is supplementary to a broader data platform, it’s the central question to wrestle with: is the graph workload demanding enough to warrant a whole additional system in your architecture?

Amazon Neptune is the product of AWS applying its “managed service for everything” philosophy to graph databases. If you’re already all-in on AWS, Neptune slots in like any other AWS database (IAM for authentication, VPC for networking, CloudWatch for monitoring). Your infrastructure team already knows how to operate it.

Neptune supports three query languages: 

1. openCypher for property graph queries

2. Gremlin for traversal-oriented programming

3. SPARQL for RDF graphs 

These languages can coexist on the same graph instance, which sounds like flexibility but in practice creates a standardization decision. Most teams pick one and live with the choice. The documentation, tooling and community content will fragment accordingly. Many teams that choose Neptune today go with openCypher, which gives them a query surface close to Neo4j’s Cypher.

Neptune Serverless handles autoscaling, eliminating the cluster sizing guesswork that comes with traditional managed databases. And Neptune Analytics is an in-memory analytics engine positioned for fast graph analytics over very large graphs with tens of billions of connections, using a built-in algorithm library and the ability to pause and resume. If you need both a transactional graph database and large-scale graph analytics, Neptune’s two-product story (database + analytics engine) covers both within AWS.

A recent addition strengthens Neptune’s data integration story: The neptune.read() procedure lets openCypher queries federate directly with S3 data without loading it into the graph. This means graph queries can incorporate warehouse data on the fly. This is a meaningful step toward zero-copy graph analytics that reduces the ETL burden that has traditionally been one of the biggest pain points with dedicated graph databases.

The nuanced cost is the lack of fine-grained access controls. Neptune’s security model is strong at the AWS infrastructure level (IAM policies on clusters and databases) but finer-grained graph-level security (subgraph access control, row-level permissions within the graph) typically gets pushed to the application layer. If your use case requires different teams to see different subsets of the same graph, you’ll be building that enforcement yourself.

Pricing has two modes: Standard (instance-hours plus I/O charges) and I/O-Optimized (higher base rate, no I/O charges). For traversal-heavy workloads with unpredictable I/O patterns, the I/O-Optimized tier can make costs significantly more predictable. It’s worth modeling both options against your actual query patterns before committing as the difference can be substantial.

In this category, Azure Cosmos DB is the standard that others are compared to. Cosmos DB’s graph capability is exposed through the Gremlin API and it’s important to understand what that means architecturally. This is not a purpose-built graph engine. It’s a globally distributed multi-model database that supports graphs as one of several API surfaces. The graph is running on top of Cosmos DB’s partitioned document store, not on a native graph storage engine.

The documentation makes the positioning clear. It explicitly directs high-scale 99.999% SLA scenarios toward Cosmos DB for NoSQL and points OLAP graph workloads toward Graph in Fabric. The message is: Gremlin in Cosmos is primarily an OLTP graph capability with bounded operational queries with global distribution, not a graph analytics engine.

That said, the global distribution story is genuinely unmatched. If your application serves users across continents and needs single-digit millisecond graph query latency in every region, now this is the right choice. Multi-region writes, configurable consistency levels and turnkey geo-replication. Cosmos handles the hard distributed systems problems that could take years to build on top of Neo4j or Neptune. 

CosmosAIGraph is an emerging architecture pattern that combines Cosmos DB’s document, vector and graph capabilities in a single instance for knowledge graph use cases. The result is document storage plus vector search plus graph traversal all within a single system.

Gremlin’s verbosity is a daily friction. Queries that take two lines in Cypher can take ten in Gremlin. The repeat().emit() pattern for variable-depth traversals is powerful but not something you want to debug at 2am. Additionally, graph algorithm support is limited: No native PageRank, no community detection, no centrality measures.

Cost unpredictability tends to be the largest concern here. Cosmos DB prices in Request Units (RUs). For graph workloads, RU consumption depends on the shape of the traversal: How many documents are read, how many partitions are touched. A traversal that hits a high-degree node can consume dramatically more RUs than one through sparse neighborhoods. Cross-partition traversals compound the problem, because the partitioning model wasn’t designed with graph traversals as the primary access pattern. Partitioning a graph effectively requires careful data modeling up front and mistakes are expensive to fix after the fact.

The sweet spot for Cosmos Gremlin is narrow but real: Globally distributed applications with bounded, operational graph queries where you’re already running Cosmos for other workloads. Think “operational knowledge graph for a global SaaS application” rather than “fraud detection engine.”

For most of the last decade, adding graph capabilities to your stack meant adding a graph database. A separate system, with storage, a query language and an operational surface. Not anymore. The major data platforms have decided graph is a capability they should ship themselves. The thesis is straightforward: If you’re already paying for a platform that stores your data and runs your queries, why are you also paying for, operating and integrating with a second system just to traverse relationships? 

There are two different bets on what platform native graph actually means. 

Microsoft built their own engine. Graph in Fabric is a first party graph database that runs directly on OneLake, queryable via the GQL standard, with NL2GQL for business users who want to ask questions in plain English. The pitch is that there's nothing to integrate. Your data is already in OneLake, the graph engine reads it natively and you write queries against the same data your analysts are already running SQL against. It reached general availability at FabCon 2026 in Atlanta, making it the newest option here and the one most aligned with where data platforms are heading.

Graph in Fabric is also visibly evolving. Features like schema evolution that were documented as not-yet-supported a few months ago appear to be shipping now. That's the upside of betting on a single vendor’s first-party engine: The roadmap moves quickly and you get the compatibility without doing any of the integration work. 

Snowflake took a different bet. Rather than build a first-party graph engine they built a framework. Native Apps and Snowpark Container Services that lets third party graph engines run inside your Snowflake account’s security boundary. The data never leaves Snowflake. The engine runs in a compute pool you control. Horizon Catalog governance and access controls pass through intact. Maybe most importantly, you get to pick which engine. 

FalkorDB on Snowflake is the option that maps most directly to the “graph engine on your warehouse” thesis. It's a Cypher-native graph database that runs in an SPCS compute pool, uses reference binding to get scoped read access to your Snowflake tables and processes Cypher queries against in-memory graph storage built from that data. The architectural compromise: FalkorDB loads your Snowflake data into its own in-memory graph structure, so you have to think about refresh patterns when your underlying tables change. The flip side is that traversals are fast because they’re operating on an in-memory representation optimized for the workload. If your graph needs to be queried at interactive latencies and your underlying data updates on a tractable cadence, this is a clean architecture. 

Neo4j Graph Analytics for Snowflake is the option for shops that want the most mature graph algorithm library available–PageRank, Louvain community detection, Weakly Connected Components, betweenness centrality, FastRP embeddings and the rest of the GDS catalog–exposed as SQL procedures and functions you call from Snowsight worksheets or Streamlit apps. The graph is projected from your own Snowflake tables, the algorithms run in dedicated compute pools managed by the Native App and the results write back to Snowflake as ordinary tables. You’re getting Neo4j’s algorithmic depth without standing up Neo4j infrastructure outside the platform. 

RelationalAI is the third option in this category and it’s positioned slightly differently from the other two. It's a declarative reasoning system that happens to use graph structures underneath. The GraphRAG reference implementation that Snowflake shares is built on RelationalAI, which tells you where they see the engine fitting best: Knowledge graph construction from Cortex LLM extraction, community detection over the resulting graph and structured retrieval into LLM prompts. 

The choice between Microsoft’s approach and Snowflake’s approach comes down to what you actually want from a platform-embedded graph. If you want one engine, one query language, one roadmap and tight first party integration, Fabric seems like the right choice. 

If you want to keep your data inside the platform you’ve already standardized on but don’t want to commit to a single vendor, the Native App pattern gives you optionality without giving up the governance perimeter. For an enterprise where the data is in Snowflake and the procurement reality means you’ll keep adopting best for the job tools rather than commit to one vendor’s full stack, the Snowflake approach is a cleaner mapping. FalkorDB for Cypher-heavy traversal workloads, Neo4j Graph Analytics for the library of algorithms, RelationalAI for GraphRAG and rule-based reasoning: All three live inside the same account boundary, all three are governed the same way and you pick the right one for the right workload without rearchitecting.

Maturity is the obvious concern. Neo4j has had a decade-plus of production hardening. Fabric Graph just went GA March 2026. The tooling ecosystem is thin, community content is sparse and third-party integrations are limited. It’s early.

In addition, Schema evolution isn’t supported. Structural changes to your graph model require re-ingestion into a new model. For knowledge graphs that grow organically, this is a significant constraint. Lastly, the graph algorithm story is still developing. If you need PageRank, community detection, or centrality measures today, validate that Fabric Graph covers your specific requirements before committing.

What’s worth noting is the convergence happening across the industry. Both Snowflake (via Snowflake Postgres- Postgres with pgvector and recursive CTEs) and Microsoft (via Fabric Graph on OneLake) are converging on the same thesis: Graph capabilities belong inside the data platform, not bolted on as a separate system. The philosophy is identical, the implementation is different and both systems are betting against the dedicated graph database model that Neo4j pioneered.

Here’s how the four products stack up on the criteria that actually drive enterprise decisions.

	Neo4j	Neptune	Cosmos DB	Fabric Graph
Access control	RBAC + subgraph policies	IAM, cluster/DB level	Azure RBAC, partition-key	Entra ID, workspace
Ops maturity	Most mature. AuraDB or self-hosted	Fully managed, minimal ops	Fully managed, multi-model	GA March 2026. Early
Query language	Cypher	openCypher + Gremlin + SPARQL	Gremlin	GQL (ISO) + NL2GQL
Data integration	ETL required (APOC, Kafka, custom)	ETL required (S3 load, Streams)	ETL required (change feed)	Zero ETL (OneLake only)
Cost model	Instance-based (AuraDB)	Instance-hrs + I/O	RU-based, hard to predict	Fabric CUs, shared
Algorithms	GDS library—most complete	Neptune Analytics—maturing	Limited	Early
Ecosystem	Multi-cloud, broadest integration	AWS-native	Azure-native	Fabric-first
Snowflake integration	Native App: Neo4j Graph Analytics for Snowflake	External connector required	External connector required	None—Microsoft-only

It might seem hard to know which option to pick. The decision tends to come down to a few honest questions about your constraints. 

1. Are you sure you need a graph database? If your graph fits in Postgres, your traversals are shallow and your concurrency is moderate, you might not need a dedicated graph database. The overhead might outweigh the value. Focus on the right architecture for the workload.

2. Do you need the best graph tooling available, regardless of operational cost? If the answer is yes, consider Neo4j. It has a strong traversal performance, a rich algorithm library, the most mature ecosystem and the most community content. You’ll pay for it in operational complexity and integration work, but if graph is central to your product, it’s the benchmark for a reason.

3. Are you AWS-native and want managed infrastructure that just works? If yes, look into Neptune. It integrates with IAM, VPC and CloudWatch the way you’d expect. Neptune Analytics extends the story for large-scale graph analytics alongside the transactional graph.

4. Do you need global distribution for operational graph queries? If you do, consider Cosmos DB Gremlin. Nothing else matches Cosmos’s multi-region latency guarantees. But scope your graph queries tightly, model your partitioning carefully and accept that graph analytics isn’t what this product is for.

5. Are you Fabric-first and want a graph inside your platform boundary? This is what Graph in Fabric was built for. Unified governance, zero data movement, GQL standardization, NL2GQL for business users. But go in with eyes open about maturity as the product just reached GA in March, 2026.

6. Do you want graph capabilities without standing up a separate database and you’re on Snowflake? Snowflake Native Apps. FalkorDB for interactive Cypher traversal workloads. Neo4j Graph Analytics for the full GDS algorithm catalog. RelationalAI for GraphRAG and rule-based reasoning. All three run inside your Snowflake account boundary via SPCS and no separate database to operate. Horizon Catalog governance throughout, your data never leaves the platform. Choose based on workload pattern; interactive traversal, batch analytics or declarative reasoning.  

The graph database market is at an inflection point. The dedicated-engine model that Neo4j pioneered is being challenged from both sides by cloud providers embedding graph into managed services and by data platforms absorbing graphs as a native capability. For teams making this decision today, the right choice depends less on which product is the best and more on where the graph fits in your specific architecture: Is it the center of gravity, or is it one capability among many? That answer determines everything else.