Knowledge graph vs vector database: how to choose your AI foundation

When enterprises start getting serious about AI—beyond the pilots, beyond the demos—a foundational question surfaces fast: how do we represent our knowledge so AI can actually use it? Underneath most mature AI initiatives, you'll find a knowledge graph, a vector database, or a combination of both—each handling a different side of the same challenge: making structured and unstructured data work together for AI.

On the surface, the choice can look like a technical preference. In reality, it has big implications for how safely your agents behave, how reliably they answer questions, how easily you can govern access, and how fast you can ship new AI capabilities.

In this blog, we’ll define both approaches, compare them honestly, and show how Glean combines them—so you can make a grounded decision for your own AI roadmap.

What is a knowledge graph?

A knowledge graph is a structured model of how things in your organization relate to each other. It doesn't just store content—it stores entities (people, teams, documents, tickets, accounts, systems) and the relationships between them.

Think of it as a living map of your business:

• Nodes represent entities: “Customer: Acme Corp”, “Team: Security”, “Doc: Incident Postmortem 2512”
• Edges represent relationships: “owns”, “reports to”, “references”, “depends on”
• Types and schemas define what entities exist and which relationships are valid

Knowledge graphs are stored and queried in a graph database—purpose-built infrastructure designed for querying relationships at scale. Unlike a traditional relational database, which stores data in rows and columns, a graph database is built to traverse connections—making it far more natural for modeling how work actually flows across an organization.

Strengths of knowledge graphs

Knowledge graphs shine in areas where structure, relationships, and governance matter.

Explainability and reasoning
Because the graph is explicit, you can trace why a result emerged. When an AI agent surfaces a document or recommends a next step, it can show its work: "This incident is linked to that service, which is owned by this team, whose runbook is here." That kind of traceability is essential for trust, debugging, anomaly detection, and regulatory compliance.

Governance and control
Graphs make it natural to encode permissions. Roles, teams, and ownership are first-class entities. Access rules and approval paths can be expressed and enforced through graph relationships—something that's extremely difficult to do robustly with a text-only system.

Complex queries and workflows
Graphs are built for multi-hop reasoning:

• "Show me open incidents affecting our top 20 customers in EMEA."
• "Which dashboards are used by the teams working on this OKR?"
• "What are the downstream services of this database, and who owns them?"

These questions depend on graph traversal—following chains of relationships across entities—not on matching keywords.

Stable representation of core concepts
Organizational structure, customer hierarchies, product lines, regulatory frameworks—these evolve, but not at the pace of every chat message or document edit. Encoding data relationships in a graph gives AI systems a reliable backbone to reason over.

Where knowledge graphs struggle

Knowledge graphs are powerful, but they aren’t a magic bullet.

Upfront modeling and ongoing stewardship
Building a graph means first investing in data modeling—deciding which entities and relationships matter, how they're named, and how they evolve as your business changes. That requires domain expertise, cross-functional alignment, and continuous maintenance.

Messy, unstructured content
Most enterprises hold enormous amounts of information that doesn't fit neatly into schemas—support threads, technical docs, customer emails, chat logs. You can attach these to a graph, but the graph itself won't automatically understand their semantics.

Approximate similarity
Graphs excel at explicit relationships: "this ticket is about this customer." They're less suited to capturing approximate meaning—"these two docs explain similar concepts in different words." That's where vector databases come in.

Scale and infrastructure complexity
At very large scales, graph database storage and graph query optimization become genuinely hard. It's solvable with the right infrastructure—but it's not trivial to build, operate, or maintain as your data and use cases grow.

What is a vector database?

A vector database stores numerical representations—called vector embeddings—of unstructured data. These embeddings are produced by machine learning models and capture semantic meaning: items with similar meaning end up near each other in multi-dimensional space.

Instead of asking "which documents contain these exact words?", a vector search converts your question into a query vector—a numerical representation of what you're looking for—then finds the stored vectors closest to it. This approach enables efficient similarity search at scale—retrieving semantically relevant results across millions of documents in milliseconds.

Strengths of vector databases

Vector databases are the workhorse behind a lot of modern AI applications, especially retrieval-augmented generation (RAG) and semantic search.

Semantic search across unstructured content
You can ingest large volumes of raw data—wiki pages, PDFs, tickets, code comments—and get useful search behavior without hand-crafted taxonomies. The model handles synonyms, paraphrases, and related concepts out of the box.

Fast path to value
Because you don’t need a detailed schema, vector databases make it easy to bootstrap semantic search and basic Q&A. Many teams start here: embed content, store vectors, hook up an LLM, and you’ve got a prototype.

Tolerance for messy and heterogeneous data
Vector systems handle diverse datasets well—mixed-language content, inconsistent formatting, complex data, streaming data, and partially structured information—which matches the reality of most enterprise environments.

Natural fit for LLM workflows
Vector databases plug cleanly into large language model pipelines: embed query → retrieve neighbors → pass to the model → generate answer. This pattern is now standard for grounding AI on internal knowledge.

Where vector databases struggle

Used alone, vector databases also introduce risks.

Opacity and limited explainability
Embeddings are dense, opaque representations. You can rarely explain why two things are similar—only that the model thinks they are. That makes it harder to debug unexpected behavior or verify that the system is following business logic.

Weak relationship modeling
A vector-only setup has no native way to represent complex relationships like "this team owns that service" or "only managers can access this doc." You can approximate these constraints with metadata filters, but you're forcing a problem that belongs in a traditional relational database or graph database into a similarity engine.

Governance and safety gaps
Without a separate structure for permissions, ownership, and hierarchies, it's easy for AI to retrieve content that shouldn't be visible for a given user or role. That risk compounds as you move from answering questions to taking actions.

Sensitivity to model drift
Change the embedding model or update preprocessing, and the entire similarity landscape can shift—sometimes subtly breaking ranking behavior across your application.

Comparing—or combining—the two

The vector database vs graph database debate is tempting to frame as an either/or. But the key differences between vector and graph databases aren't about which is better—they're about which problem each one is built to solve.

Structure vs semantics

Knowledge graphs specialize in explicit structure: entities, relationships, and rules—the natural home for structured data. Vector databases specialize in implicit semantics: finding meaning across vast volumes of unstructured data where no schema exists.

Human reasoning relies on both. We understand that "this team owns this API and that ticket is about that API" (graph-like structure). We also recognize that two incident reports describe similar root causes even when they use completely different language (vector-like similarity). Effective enterprise AI needs to mirror that duality.

Governance, safety, and compliance

For governance-heavy environments—finance, healthcare, legal, public sector—graph databases stand out:

• Access control can be expressed through explicit relationships and roles.
• Data lineage is traceable: you can see which sources contributed to a decision.
• Policies around retention, sharing, and residency can anchor in the graph.

Vector databases alone struggle here. Metadata filters help, but the core retrieval primitive is still "closest in vector space"—not "appropriate given this user's role, location, and business context." In many enterprises, that distinction isn't a nice-to-have. It's the difference between a useful assistant and an unacceptable risk.

Relevance, recall, and ranking

Retrieval quality has multiple dimensions. Precision—are the top results highly relevant? Recall—are we capturing enough of the useful long tail? Ranking—are the right items surfacing first? The answer depends on how well your system understands the relationships between different data points, not just their individual content.

Where vector databases excel at recall and coverage, graph databases excel at precision and ranking—encoding authority, ownership, and relationships that pure similarity search can't capture. The strongest systems blend both—graph signals, classical information retrieval, and vector similarity feeding into a single ranking pipeline.

Developer experience and lifecycle

Vector-centric approaches let teams get from zero to prototype quickly. Graph-centric approaches demand more upfront design, but provide the structure needed to safely encode nuanced business logic.

Most organizations discover they need both: a structured backbone for entities and permissions that traditional databases can't model well at scale, and a semantic layer for everything else. The question isn't which to use. It's how to combine them well.

Choosing the right approach for your workflows

Rather than debating abstractly, it helps to look at specific workflow scenarios.

Workflows that benefit from knowledge graphs

Knowledge graphs are particularly valuable when:

• Multi-step workflows cross teams and systems: onboarding, incident response, customer escalations, deal cycles
• Traceability matters: audits, compliance reviews, executive sign-offs
• Ownership and responsibility are central: "Who should act on this alert?" or "Which team maintains this runbook?"
• Relationships are as important as content: recommendation systems, customer hierarchies, partner ecosystems, system dependencies

For these workflows, AI agents need more than “what looks relevant.” They need to understand the complex relationships between entities and who is allowed to do what.

Workflows that benefit from vector databases

Vector databases are ideal when:

• You're doing exploratory research: "What have we written about this standard?" or "Find similar past incidents."
• You need Q&A over large datasets: support knowledge bases, API documentation, internal wikis
• You want fast semantic search without waiting on schema design
• Content is highly heterogeneous and doesn't fit cleanly into structured tables

The priority here is coverage and semantic understanding, not deeply modeled relationships.

Hybrid implementation: how the two work together

In practice, the most robust enterprise AI architectures adopt a hybrid approach:

• A knowledge graph to represent entities, relationships, permissions, and usage signals across interconnected data.
• A vector database to represent the semantics of unstructured content.
• A retrieval and ranking layer that combines both as inputs to AI models and agents.

This is how you move from "search" to AI that can plan, decide, and act. Typical hybrid implementations include:

• Graph-scoped search: First narrow the search space using a graph query (e.g., “find only content owned by this team and relevant to this account”), then apply vector similarity search within that subset.
• Graph-informed ranking: Use graph signals—recency, popularity, organizational proximity—to rerank semantically relevant results.
• Entity-aware agents: Let agents reason about entities and relationships via the graph, while using vector retrieval to pull detailed context from vector-backed content.

Key evaluation criteria for enterprises

When deciding how to invest in knowledge graphs, vector databases, or both, these questions clarify the path.

Data and content reality

• How fragmented is your content across tools and systems?
• Which parts of your domain already have a clear data structure (relational databases, CRM, HRIS, finance) and which are essentially raw data without any schema (chat, docs, tickets)?
• Where are your current taxonomies and ontologies—are they centralized or scattered?

If most of your critical knowledge lives in structured systems—or you’ve already invested in domain taxonomies or even a formal ontology that defines your key entities and relationships—a graph-based approach can capitalize on that investment. If your differentiating knowledge is mostly in free-form text and high-dimensional data without any schema, vectors may be the faster on-ramp, with structure layered in over time.

Risk profile and compliance needs

• What are the consequences of an AI system surfacing the wrong content to the wrong person?
• Do you operate under strict regulatory requirements?
• How important is explainability when AI recommends a next step or triggers an action?

The higher your risk profile, the more you need a graph-backed representation of permissions, roles, and lineage—with vector search layered on top, instead of the other way around.

AI roadmap and use cases

• Are you building for search and summarization, or for agents that take actions across tools?
• Do you plan to expand from a few narrow workflows to a broad, cross-functional AI platform?

If you’re only planning a lightweight assistant, a vector-centric stack may suffice. If your roadmap includes agents that need to answer complex queries across tools—routing work, updating systems, synthesizing context from multiple sources—you'll likely need a graph-backed architecture to safely coordinate that behavior.

Operational and organizational considerations

• Who in your organization will own the graph—data teams, platform engineering, business owners?
• How will you keep it aligned with real-world changes in teams, systems, and processes?
• Do you prefer to build and operate infrastructure yourselves, or lean on a platform that handles that complexity?

These questions often matter as much as the technical tradeoffs.

How Glean approaches knowledge graphs and vector databases

From Glean's perspective, the knowledge graph versus vector database debate isn't really the right frame. We've seen teams stall trying to choose one stack for everything. The better question: how do we combine structure and semantics into a reliable system of context for AI?

Our view: you need both

In real-world enterprise use cases, AI has to operate under constraints:

• Permissions and privacy must be respected at all times.
• Agents need to understand workflows, ownership, and dependencies, not just words in documents.
• Search and retrieval must be relevant, fast, and explainable, even as content changes constantly.

We’ve found that you cannot meet these requirements with a graph-only or vector-only architecture. You need both:

• A knowledge graph that models the entities, relationships, and signals that define how your organization works.
• A vector and classical retrieval layer that understands the semantics of unstructured content and surfaces the right evidence.

Glean was built around this premise from the start.

The Enterprise Graph

At the core of Glean is the Enterprise Graph—a dynamic, continuously updated model of your organization's people, teams, content, and activity. When Glean connects to your tools—docs, tickets, code, email, chat, dashboards, and more—it doesn’t just ingest raw text. It builds and maintains a graph database that encodes:

• Entities: people, teams, documents, tickets, dashboards, repositories, customers, accounts, projects, and so on.
• Relationships: who owns what, who collaborates with whom, which docs support which services, which tickets impact which customers.
• Signals: views, edits, shares, comments, approvals, and other interactions that indicate importance and relevance.

On top of this enterprise-wide graph, Glean builds a per-user perspective: a view that reflects what's relevant and permitted for each individual based on their role, teams, and work history. Models and agents operate with a contextual understanding of your world—not just the company's world in aggregate.

And if you already maintain domain taxonomies or ontologies, Glean can ingest them as metadata and tags so they become first-class signals inside the Enterprise Graph.

Hybrid retrieval

The Enterprise Graph is one part of the story. To power search, question-answering, and agents, Glean combines:

• Semantic retrieval via vector embeddings: capturing meaning even when wording varies
• Classical information retrieval signals: term statistics, recency, authority, engagement patterns
• Graph structure: ownership, organizational proximity, link structure, and usage signals from the Enterprise Graph

These signals feed into a single ranking system that determines what the model actually sees. When a Glean-powered AI experience retrieves context—whether for a search result or an agent's tool call—it benefits from higher relevance, better safety, and more explainability than any single retrieval approach could provide alone.

This is what allows Glean to deliver consumer-grade search and grounding in complex, highly permissioned enterprise environments.

Orchestrating context for agents

Search is only the first step. Enterprises increasingly want AI agents that can interpret a request, plan a sequence of actions, call tools across systems, iterate based on what they find—and do all of this safely, within the bounds of business rules.

Glean's system of context uses the Enterprise Graph and hybrid retrieval to orchestrate what agents can see and do at every step:

• The graph provides relational context to help interpret who and what the request is about—teams, systems, customers, projects.
• The retrieval layer surfaces just enough high-quality, permissioned evidence from across tools.
• The system updates context continuously as the agent moves through a workflow, so it maintains awareness instead of operating on a static snapshot.

The result: agents that can act with the same contextual understanding a human has when they know how the people, systems, and work products in their organization connect.

Getting started: principles for a hybrid foundation

Regardless of whether you adopt Glean or build your own stack, a few principles can guide how you combine knowledge graphs and vector databases.

Start from workflows, not technologies

Instead of asking “Do we need a graph or a vector DB?”, start with:

• Which workflows matter most—support triage, onboarding, incident management, revenue operations, data analytics, or engineering productivity?
• What information does a human rely on today to perform those workflows well?
• Which of that information is structured and stable, and which is unstructured and fluid?

Use that analysis to decide:

• Where to invest in graph structure (entities, relationships, permissions).
• Where to lean on semantic retrieval over unstructured content.
• Where you need both, tightly integrated.

Layer a knowledge graph onto successful semantic search

If you already have a vector-based RAG system running, you don't need to start over. Instead:

• Introduce entity extraction and linking to identify people, teams, systems, and customers in your content.
• Start building a lightweight graph around those entities and their relationships.
• Use graph signals to scope and rerank what your vector system retrieves.

Over time, you can evolve from “search over a flat corpus” to “search grounded in a knowledge graph that reflects how your company actually works.”

Invest in a shared context layer

As organizations stand up multiple AI experiences—chat assistants, embedded copilots, domain-specific agents—one pattern becomes clear: managing data ingestion, ranking, and permissions for each initiative doesn’t scale. Instead, invest in a shared context layer—where data is ingested once, modeled as a graph, and served through a consistent retrieval service. Every AI experience becomes faster, safer, and easier to govern. This is the role Glean is designed to play.

Ultimately, the real question isn't graphs vs vectors

Framed as an either/or decision, this debate pushes most enterprises in the wrong direction. The architectures that deliver trustworthy, high-performing enterprise AI almost always use both—knowledge graphs to represent entities, relationships, governance, and signals about how work actually happens; vector databases to capture semantic meaning across unstructured content and power rich retrieval; and a system of context that brings these together into a unified layer. When that layer understands your people, projects, systems, and content—and can deliver just the right slice of that world to a model at the right moment—you get AI that is not only capable, but also reliable, safe, and aligned with how your business actually runs.

If you're exploring how to build that kind of foundation, Glean provides the Enterprise Graph, hybrid retrieval, and system-of-context orchestration out of the box—so your teams can focus on designing workflows and agents that advance your business, rather than rebuilding infrastructure from scratch.

Next steps

• Learn how Glean's system of context works.
• See how Glean builds your Enterprise Graph—request a demo.

‍