Graph RAG vs. Vector RAG vs. SQL: Which Retrieval Architecture Should Ground Your AI Product?

The decisive question in retrieval-augmented generation is not which model will compose the answer. It is which retrieval system will decide what the model is permitted to know.

Three approaches now deserve serious consideration: vector RAG, Graph RAG, and direct retrieval from SQL. Each can ground a model in proprietary information. Yet they preserve different forms of truth. Vector search preserves semantic resemblance. A graph preserves relationships. SQL preserves explicit records and constraints.

Choosing among them is therefore an architectural judgment, not a feature comparison. The correct choice depends on whether the answer lives in a passage, across a network, or inside a row.

The Three Architectures at a Glance

Vector RAG: Find the Passage That Sounds Like the Question

Vector RAG begins by splitting documents into chunks and converting each chunk into an embedding: a numerical representation of its semantic content. The user’s query is embedded in the same space. The system retrieves nearby chunks, optionally reranks them, and passes the best candidates to a language model.

This architecture excels when terminology varies. A query about “ending a subscription” can retrieve a policy written under “account cancellation” without requiring an exact keyword match. That makes vector RAG particularly effective for manuals, policies, research archives, support articles, and other prose-heavy collections.

Its central limitation is subtle: semantic proximity does not establish that a passage answers the question. A chunk describing an old cancellation policy may resemble the query more closely than the current policy. A passage discussing an exception can outrank the general rule. Metadata filters, date constraints, hybrid keyword search, and reranking can mitigate these failures, but the retrieval pipeline must be designed deliberately.

Worked example: a policy assistant

Suppose an employee asks, “Can a contractor approve travel expenses?” A vector system may retrieve passages mentioning contractors, approval, and travel. If the answer is contained in one policy section, this works elegantly. If authority depends on business unit, expense threshold, delegation status, and effective date, similarity alone becomes fragile. The relevant words may be distributed across several documents whose relationships are not explicit.

Vector RAG should be the default when the source material is mostly narrative and users ask exploratory or paraphrased questions. It should not be mistaken for a general-purpose reasoning database.

Graph RAG: Retrieve the Path, Not Merely the Passage

Graph RAG represents knowledge as entities and relationships. A corporate graph might connect a contractor to a project, the project to a cost centre, the cost centre to an approver, and that approver to a delegation policy. Retrieval can follow these edges before supplying the resulting subgraph and supporting text to the model.

The advantage appears when no single passage contains the answer. Questions such as “Which suppliers are indirectly exposed to this sanctioned entity?” or “What services depend on the component affected by this incident?” require traversal across multiple facts. A graph makes those chains addressable.

Graph RAG also offers a distinctive form of explainability. Instead of presenting only semantically similar excerpts, the system can expose a path: supplier A is owned by company B; company B shares a director with company C; company C appears on a restricted list. The language model still needs to phrase the finding carefully, but the retrieval logic is inspectable.

The cost is ontology. Someone must decide what qualifies as an entity, which relationships matter, how aliases are resolved, and how uncertainty is represented. Automated extraction can propose nodes and edges from documents, but extraction errors become structural errors. “May supply” must not silently become “supplies.” A graph assembled from probabilistic claims needs provenance, confidence, and temporal validity attached to its relationships.

Worked example: incident impact analysis

Imagine a payment service failing because a certificate expired. A vector search may find the certificate runbook and previous incidents. A graph can traverse from the certificate to the gateway, from the gateway to dependent services, and from those services to customer-facing products and responsible teams. The graph answers the blast-radius question; the vector index supplies operational guidance. This is why mature implementations often combine the two.

SQL Retrieval: Ask the System of Record

When an answer depends on current structured data, SQL is often the most honest retrieval layer. Rather than finding passages about an order, the system queries the order record. Rather than inferring revenue from reports, it aggregates the appropriate transactions under defined rules.

A language model can translate a user request into a constrained query, select from approved query templates, or call a semantic layer that exposes business concepts such as “active customer” and “recognized revenue.” The safest design is rarely unrestricted text-to-SQL. Read-only credentials, table allowlists, row-level permissions, query validation, execution limits, and separation of query generation from execution are essential boundaries.

SQL is strongest when correctness depends on predicates: status equals shipped, region equals Europe, cancellation date is null, and created date falls within a defined period. It also preserves freshness naturally because the query runs against current records.

Its weakness is meaning expressed in prose. A database may show that a claim was denied, while the rationale lives in an adjuster’s note. SQL can locate the claim precisely but cannot, by itself, retrieve the most relevant explanation from unstructured text.

Worked example: customer operations

Consider the question, “Why has order 4821 not shipped?” SQL can establish that payment cleared, inventory is reserved, and the fulfillment status is on hold. Vector retrieval can find the warehouse note explaining that the address failed validation. A graph might reveal that several held orders share the same carrier integration. Each architecture illuminates a different layer of the event.

Where Each Approach Fails Under Pressure

The most consequential failures arise when an architecture is asked to represent a kind of truth it does not natively hold.

• Vector RAG fails through plausible adjacency. It retrieves material that is conceptually close but outdated, exceptional, or contextually wrong. Citation does not cure this if the cited passage was the wrong evidence.
• Graph RAG fails through false structure. An incorrect entity merge or edge can contaminate every traversal that depends on it. Graph precision often matters more than graph size.
• SQL retrieval fails through semantic mismatch. A valid query can still answer the wrong business question if “customer,” “churn,” or “completed” has not been formally defined.

Permissions deserve equal attention. Vector indexes must inherit document access controls. Graph traversal must not reveal a restricted node through an unrestricted neighbour. SQL must enforce authorization at execution time, not merely instruct the model to avoid sensitive tables.

Hybrid Retrieval Is a Composition, Not a Compromise

Many valuable systems need more than one architecture, but adding all three indiscriminately creates routing complexity and contradictory evidence. The better pattern is to assign each layer a precise role.

1. Use SQL to identify the object. Retrieve the exact customer, asset, claim, or transaction under current permissions.
2. Use a graph to expand relevant relationships. Find dependencies, ownership, related incidents, or policy applicability.
3. Use vector search to recover explanatory language. Retrieve notes, procedures, contracts, and narrative evidence linked to those objects.
4. Generate only after evidence is assembled. Require the model to distinguish recorded facts, inferred relationships, and textual interpretation.

For the delayed order, SQL confirms the operational state, the graph detects a shared integration dependency, and vector retrieval supplies the incident note. The final answer can say what is known, what is connected, and what remains an inference. That separation is more valuable than a fluent but undifferentiated response.

Which Architecture Should You Choose?

Choose vector RAG if your knowledge primarily lives in documents and the dominant task is finding relevant language despite varied phrasing. Begin with metadata, hybrid search, reranking, and visible citations before adding architectural complexity.

Choose Graph RAG if the product’s distinctive value lies in connections: dependencies, ownership, provenance, fraud rings, supply chains, research relationships, or organizational knowledge. Invest early in identity resolution, temporal edges, and source provenance.

Choose SQL retrieval if users need exact, current answers from operational systems. Put a governed semantic layer between natural language and raw schemas, and make authorization part of query execution.

Choose a hybrid when the question crosses evidence types, but nominate one system as authoritative for each claim. Records should govern state, graphs should govern explicit relationships, and documents should govern narrative context.

The emerging opportunity is not a universal retrieval engine. It is a retrieval constitution: a clear declaration of which system may establish which kind of fact. Products that define that boundary well will do more than produce grounded answers. They will reveal why an answer deserves belief.

This post was drafted with AI assistance and reviewed against our editorial policy before publication. Corrections are made at the source, on the page, with the date shown.