Enterprise AI retrieval decisions often start in the wrong place: which tool should we buy? The better question is what kind of evidence the workflow needs.
A customer-risk agent asking 'which accounts are likely to churn and why?' may need structured usage metrics, support-ticket text, contract clauses, account hierarchy, stakeholder relationships, and recent escalation history. No single retrieval pattern handles all of that cleanly.
SQL retrieval is best for structured numerical and state-based questions. Vector RAG is best for finding semantically relevant text.
GraphRAG is best when relationships and multi-hop context matter. Gartner and the broader market are highlighting GraphRAG because enterprises are realizing that vector similarity alone does not represent relationships well.
But GraphRAG is not a blanket replacement either. The right architecture is a decision framework.
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The Decision Matrix
Use SQL retrieval when the question can be answered through governed tables, metrics, filters, joins, and aggregations. Use vector RAG when the answer depends on unstructured text and semantic similarity. Use GraphRAG when the answer depends on entities and relationships across a corpus or system.
Relationship-heavy questions include ownership, dependency, risk propagation, customer-product-contract links, and root-cause paths. Numerical business questions belong in SQL or a semantic metrics layer. Document retrieval belongs in vector or hybrid RAG.
Multi-hop reasoning often belongs in GraphRAG or a graph-enhanced retrieval path. Entity resolution needs graph or master-data support because similar names and related identities are not enough. Governance cuts across all three.
Latency and cost vary: SQL is often cheapest and fastest for structured facts, vector RAG adds embedding and retrieval cost, and GraphRAG adds indexing, graph construction, and traversal complexity.
Problem type Best fit
Revenue by segment SQL retrieval + semantic layer
Find related policy clauses Vector RAG or hybrid RAG
Vendor risk across relationships GraphRAG
Customer churn explanation SQL + vector RAG + graph
Entity dependency analysis GraphRAG
Exact account state SQL retrieval
Similar past support tickets Vector RAG
Multi-hop corpus reasoning GraphRAGRelated: AI Data Infrastructure, Data Engineering, Data Governance and Security Engineering, Book a diagnostic
When SQL Retrieval Wins
SQL retrieval wins when the question depends on structured business truth. Counts, totals, status, balances, dates, inventory, usage, billing, SLA breaches, and operational state should not be answered from embedded documents unless there is no structured source. SQL is explainable, mature, governable, and fast when the schema and indexes are designed well.
Paired with a semantic layer, SQL retrieval can answer natural-language business questions without forcing the model to invent joins or metric definitions. It also keeps permissions close to the database or serving layer. The failure mode is that raw schema alone is not enough.
If table names and columns do not express business meaning, an agent may generate plausible but incorrect SQL. The fix is a semantic layer, verified query examples, access-aware serving, and evaluation against known business questions.
When Vector RAG Wins
Vector RAG wins when users ask questions in fuzzy human language and the evidence lives in text. It is strong for policies, support tickets, meeting notes, contracts, research, incident reports, product docs, and knowledge-base content. It helps when the user does not know exact terms.
A good implementation rarely uses vector search alone. Snowflake Cortex Search, for example, describes a hybrid approach using vector search, keyword search, and semantic reranking. That pattern matters because enterprise data contains exact IDs, legal terms, product names, and customer-specific language.
Vector-only retrieval can miss exact matches or retrieve semantically similar but irrelevant passages. The architecture should chunk documents carefully, preserve source metadata, filter by role and tenant, cite sources, and measure retrieval quality. Vector RAG is a workhorse, but it should not be asked to calculate metrics or understand relationship graphs by itself.
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Book a Diagnostic CallWhen GraphRAG Wins
GraphRAG wins when relationships are the evidence. Microsoft's GraphRAG documentation describes global search for broad corpus-level questions, local search for specific entities and neighbors, DRIFT search for combining local and global approaches, and basic search when baseline RAG is enough. In enterprise terms, GraphRAG helps with questions like: which vendors connect to the products affected by this vulnerability, which customer accounts depend on the impacted service, which policies conflict across regions, or what themes connect support escalations to product areas?
GraphRAG can also help with entity resolution and multi-hop reasoning where the path matters. The trade-off is cost and complexity. Graph construction, community summaries, graph updates, and evaluation require more infrastructure than a simple vector index.
Use it where relationship quality changes the answer, not because it sounds more advanced.
Governance, Latency, and Cost
Governance should shape the retrieval choice as much as answer quality. SQL can enforce row and column access close to the source. Vector RAG needs metadata filters, tenant isolation, source permissions, and citation controls.
GraphRAG needs permissions over nodes, edges, communities, and source documents, which can be more complex. Latency also differs. SQL over indexed data can return in milliseconds.
Vector retrieval plus reranking adds time. GraphRAG can add more time depending on traversal, summarization, and context assembly. Cost follows the same pattern.
SQL uses database compute. Vector RAG adds embedding, index storage, retrieval, reranking, and larger prompts. GraphRAG adds graph indexing, graph storage, summarization, and maintenance.
The cheapest architecture is the one that routes each question to the minimum evidence needed, not the one that uses the newest retrieval method everywhere.
How to Build the Routing Layer
The routing layer is where retrieval architecture becomes reliable. Start by classifying the user's intent before calling a retriever. Is the question asking for a metric, a list of records, a policy clause, a similar case, an entity relationship, or an explanation that combines several evidence types?
Map those classes to tools. A metric question goes to a semantic SQL tool. A policy question goes to document retrieval.
A relationship question goes to graph retrieval. A mixed question calls more than one tool and labels the evidence. Add confidence rules so the agent can ask a clarification question when intent is ambiguous.
Add budget rules so the system does not run graph retrieval for every casual question. Add evaluation examples for each route, including cases where the correct behavior is to refuse, clarify, or use a cheaper path. This layer is often where DharmOps-style architecture work pays off fastest because it reduces cost and improves answer quality at the same time.
Related: retrieval architecture consulting, AI agent cost stack
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Contact UsA Simple Selection Rule
If the answer must be numerically correct, start with SQL and a semantic layer. If the answer must cite text, start with vector or hybrid RAG. If the answer must explain relationships, start with graph retrieval.
If the answer must do all three, build a routed workflow and keep the evidence labeled. This simple rule prevents the most expensive mistake: choosing one retrieval style and forcing every enterprise question through it. Retrieval architecture should reduce ambiguity before the prompt, not ask the model to repair architectural shortcuts after the fact.
GraphRAG, vector RAG, and SQL retrieval are parts of one enterprise retrieval architecture. Use SQL for governed facts and metrics, vector RAG for unstructured text, and GraphRAG for relationships and multi-hop context. The mature pattern is routing, not replacement.
Frequently Asked Questions
When should I use SQL retrieval for AI?
Use SQL retrieval when the question depends on structured facts, business metrics, current state, filters, aggregations, or governed database records.
When should I use vector RAG?
Use vector RAG when the evidence is unstructured text and users may ask in natural language without knowing exact terms, such as policies, tickets, documents, and transcripts.
When should I use GraphRAG?
Use GraphRAG when relationships, entities, dependencies, themes, or multi-hop reasoning materially change the answer.
Is GraphRAG more expensive than vector RAG?
Usually yes, because graph construction, summaries, storage, traversal, and maintenance add cost. It is worth it when relationship quality improves decisions enough to justify that cost.
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