GraphRAG vs RAG: The Break Even Analysis Before You Switch
The GraphRAG vs RAG question usually gets asked backwards. Teams ask whether a knowledge graph produces better answers, and the honest answer is that it depends entirely on the shape of the query you are serving. This article gives you the benchmark split by query type, the real GraphRAG indexing cost picture, and a decision rule you can apply to your own traffic. By the end you will be able to tell whether your workload justifies building a graph, or whether you are about to pay for structure your users never actually query.
Quick definitions, because the two systems differ before retrieval even starts. Plain vector RAG embeds your documents, retrieves the chunks nearest to the question, and hands them to the model. GraphRAG first runs entity extraction across the whole corpus, builds a knowledge graph of entities and the relationships between them, precomputes community summarization over clusters in that graph, and then retrieves by traversing structure instead of by vector distance alone.
All of the extra cost lives in that indexing pipeline. So does all of the advantage, for a narrow band of queries.
The benchmark: plain RAG vs GraphRAG by query type
The single most useful thing you can do before switching is stop evaluating retrieval quality as one number. Split it by query type and the picture separates cleanly.
On simple single hop factual retrieval, the two approaches are effectively tied. GraphRAG scores 60.9 against plain vector RAG at 60.1. That gap is noise. If you paid for a graph index to serve those queries, you paid for nothing.
The separation shows up elsewhere. On multi hop reasoning, GraphRAG gains roughly 10 points, 53.4 against 42.9. On contextual summarization, it gains roughly 13 points, 64.4 against 51.3.
| Query type | GraphRAG | Plain vector RAG | Gap |
|---|---|---|---|
| Single hop factual | 60.9 | 60.1 | tie |
| Multi hop reasoning | 53.4 | 42.9 | about 10 points |
| Contextual summarization | 64.4 | 51.3 | about 13 points |
Two things are worth pulling out of that table. First, the tie on single hop retrieval is not a small win for the graph, it is a genuine tie, and single hop factual lookup is the majority of traffic in most production question answering systems. Second, even where GraphRAG wins, absolute scores on multi hop reasoning stay low for both. A graph makes hard queries less bad. It does not make them solved.
So before you cost anything out, go and sample a few hundred real queries from your logs and label them. If fewer than one in five needs to cross a relationship between documents, the benchmark is already telling you the answer.
When GraphRAG wins: multi hop and global queries
GraphRAG multi hop reasoning wins for a structural reason, not a magical one. A multi hop question requires you to find entity A, follow a relationship to entity B, and only then retrieve the fact you actually wanted. Vector similarity has no mechanism for that second step. The chunk containing B might sit nowhere near the question in embedding space, so it never enters the context window and the model confabulates the join.
The graph makes that traversal explicit. Entity extraction has already recorded that A relates to B, so the retriever walks the edge rather than hoping the embedding lands.
The second win is on global queries. There is a real difference between a local query, meaning "what did this specific contract say about termination", and a global query, meaning "what themes run across all of these contracts". Vector retrieval answers the first well and the second badly, because top k chunk retrieval can only ever show the model a sample. Community summarization gives GraphRAG a precomputed view of the whole corpus, which is exactly what a global query needs.
The numbers back that up. On global dataset wide queries, GraphRAG won 72 to 83 percent of comprehensiveness comparisons against plain vector RAG, and 62 to 82 percent of diversity comparisons.
If your product does corpus level sensemaking, competitive analysis across a document set, thematic review, or anything a human would describe as "read all of this and tell me what is going on", that is the workload the graph was built for.
When plain RAG wins: single hop factual retrieval
Plain RAG wins the common case, and it wins it on economics rather than on quality, because quality is a tie.
Single hop factual retrieval means the answer lives in one place. A policy clause, a specification value, an error code, a price. You embed, you retrieve, you answer. The graph adds an indexing pipeline, an entity resolution problem, a schema to maintain, and a second failure mode where a wrong edge routes the retriever confidently into the wrong neighbourhood.
There is also a cheaper upgrade path that teams skip past on the way to a graph. If your vector retrieval is missing things, hybrid retrieval combining lexical and dense search, merged with reciprocal rank fusion, usually recovers a meaningful slice of the misses for a fraction of the operational weight. It is not as interesting as a knowledge graph. It ships in an afternoon.
Knowledge graph RAG production systems also carry costs that no benchmark measures: the graph goes stale when documents change, entity resolution drifts as new sources arrive, and someone has to own the schema. Budget for that before you commit.
The cost cliff: $33,000 to $33 in eighteen months
The classic objection to GraphRAG was price, and for a while it was a fair objection. Running entity extraction and summarization through a language model across an entire large corpus meant one model call per chunk before you served a single user query.
That number has collapsed. Indexing a large corpus went from roughly $33,000 to approximately $33 over eighteen months, a reduction of about 99.9 percent. The driver was replacing language model based entity summarization with NLP driven concept extraction.
If your internal position on GraphRAG was formed while the indexing bill looked like a headcount, that position is stale. The GraphRAG indexing cost argument has essentially stopped being the deciding factor. What remains is whether your query mix benefits at all, which is a product question rather than a budget one.
LazyGraphRAG: 0.1% indexing cost, comparable quality
LazyGraphRAG, from Microsoft Research, is the specific piece of work that moved the cost floor. The LazyGraphRAG cost reduction comes from one design decision: defer all language model use to query time instead of spending it during indexing.
Full GraphRAG pays up front. It walks the corpus, extracts entities, summarizes communities, and stores the result, whether or not anyone ever asks a question that needs it. LazyGraphRAG builds a much cheaper structural index and only spends model calls once a real query arrives and it knows which part of the graph matters.
The reported result is that indexing costs 0.1 percent of full GraphRAG. Global search queries cost about 4 percent of full GraphRAG at comparable answer quality, which works out to more than 700 times cheaper per query.
For most teams this changes the sequencing. You no longer have to be certain that graph retrieval will pay off before you build the index, because being wrong now costs very little. That is a different risk profile from the one that made this a committee decision.
The break even rule: which workload justifies the graph?
Here is the practical rule for when to use GraphRAG, stated so you can apply it to a query log rather than to a vibe.
Use plain vector RAG when queries are single hop and document centric. The answer lives in one place, and you only need to find that place.
Switch to GraphRAG or LazyGraphRAG when queries require multi hop relationship traversal, reasoning across documents, or global sensemaking over the corpus as a whole.
You can encode that as routing rather than as an either or platform choice, which is what I would do in most systems:
GRAPH_QUERY_TYPES = {"multi_hop", "cross_document", "global_sensemaking"}
def route(query_type: str) -> str:
"""Pick a retriever per query instead of committing the whole system."""
if query_type in GRAPH_QUERY_TYPES:
return "lazy_graph_rag"
return "vector_rag"
# Classify once, cheaply, then route. Most production traffic
# lands on vector_rag, which is the point.
Routing beats migrating. You keep the cheap path for the traffic that is tied on quality anyway, and you pay for traversal only on the queries that actually need it. It also gives you a real measurement: log which branch fired, and after a week you will know what share of your traffic ever justified the graph.
FAQ
When is GraphRAG better than plain RAG?
On multi hop reasoning, where it gains about 10 points, and on contextual summarization, where it gains about 13 points. It also wins on global dataset wide queries, taking 72 to 83 percent of comprehensiveness comparisons and 62 to 82 percent of diversity comparisons. On single hop factual retrieval it is a tie at 60.9 against 60.1, so there is no quality reason to switch for that traffic.
How much does GraphRAG indexing cost now?
Far less than it used to. Indexing a large corpus fell from roughly $33,000 to approximately $33 over eighteen months, driven by NLP driven concept extraction replacing language model based entity summarization. Cost is no longer the main reason to avoid a graph.
What is LazyGraphRAG?
A variant from Microsoft Research that defers all language model use to query time. It indexes at 0.1 percent of full GraphRAG cost and runs global search queries at about 4 percent of full GraphRAG cost with comparable answer quality, which is more than 700 times cheaper per query.
Can I use GraphRAG without an expensive graph index?
Yes. That is precisely what LazyGraphRAG is for. Because indexing costs 0.1 percent of the full pipeline, you can build the index to test whether your query mix benefits, rather than having to prove the benefit before you are allowed to build it.
