GraphRAG Infrastructure: Graph Stores, Vector Indexes, and Pipelines

GraphRAG — retrieval that walks a knowledge graph of entities and relationships instead of, or alongside, semantic search over embeddings — answers questions vector search structurally cannot: multi-hop reasoning across documents, relationship questions, and corpus-wide synthesis. Its infrastructure cost is equally structural: a second store, a construction pipeline that must run before retrieval works, and an entity-resolution duty that never ends. This page maps the full component anatomy, prices the delta over vector RAG, explains how the two retrieval systems cooperate, and ends with the deployment and residency decision the combined data set forces.

The GraphRAG Stack, Component by Component

A GraphRAG stack adds a graph store holding entities and relationships, a graph construction pipeline (entity extraction and resolution), and a traversal-plus-retrieval layer on top of the familiar vector index, embedding pipeline, and serving tier — five cooperating components where vector RAG has three.

Products bundle these components differently — some databases blur store roles, some platforms own the pipeline — but the anatomy holds: whatever you buy or build, you are operating five components, and the two new ones carry the cost and the risk.

What GraphRAG Adds Over Vector RAG Infrastructurally

The additions are material: a graph construction pipeline that must run before retrieval works, entity-resolution maintenance as an ongoing operational duty, a second store to operate and secure, and implementation timelines reported in months rather than the weeks a vector index takes.

What the graph layer actually costs you:

• Construction before value: the vector index is useful after one ingestion pass; the graph is useful only after extraction and resolution have run over the corpus — comparison coverage reports multi-month foundational investments versus roughly two months to production for vector RAG.
• Entity resolution never finishes: new documents introduce entity variants that must merge into existing nodes, and resolution errors compound into retrieval errors; this is a standing pipeline with owners, not a setup task.
• A second store to operate: backups, security, scaling, and patching for the graph store — with residency and access-control obligations matching the corpus it holds.
• Retrieval complexity: tuning when to traverse and when to embed is an ongoing engineering question vector-only stacks never face.

Costs scale with corpus size and entity density, so pilot measurements on your corpus beat published timelines. The honest summary: GraphRAG is an infrastructure commitment, justified by the question classes it unlocks — not an upgrade you apply to make RAG "better" generally.

How Graph and Vector Retrieval Cooperate

In production they cooperate by design: semantic search over the vector index proposes candidate content, graph traversal expands it along relationships for multi-hop questions, and the orchestration layer fuses both into the prompt context — hybrid patterns documented in reference architectures and benchmarks.

Tracing a multi-hop question through the hybrid path:

1. Vector search proposes: "Which suppliers of component X changed certification status this year?" embeds and retrieves candidate documents semantically.
2. Graph traversal expands: entities in those candidates — suppliers, components, certifications — resolve to nodes, and traversal walks the relationships the question implies (supplies, certified-by, status-change events) across documents the vector search never surfaced.
3. Fusion assembles context: the orchestration layer merges retrieved passages with graph-derived facts (paths, attributes, relationship evidence) into the prompt, citing both provenances.

Each path contributes what the other lacks: vector retrieval handles the fuzzy language of the question; the graph contributes precision on relationships and multi-hop structure that embedding similarity cannot represent. Independent benchmark work comparing vector, graph, and hybrid pipelines confirms the hybrid pattern is where the advantages concentrate — with the size of any gain being workload-specific, which is why your own evaluation set matters more than any published accuracy number.

Ingestion and Refresh: The Real Operating Commitment

Keeping GraphRAG healthy means re-running extraction on new documents, resolving entities against the existing graph, repairing resolution errors, and propagating deletions through both stores — a scheduled pipeline with monitoring, not a one-time build, and the usual source of production decay.

The refresh pipeline's four duties:

• Incremental extraction: tie extraction runs to document change events rather than rebuilding the graph, or refresh cadence collapses under corpus growth.
• Entity-resolution repair: schedule review of merged-and-split candidates; unresolved duplicates and over-merged entities quietly corrupt retrieval quality.
• Dual-store deletion: a deletion must remove the source passage, its embedding, the extracted entities (where no longer referenced), and the relationships — partial deletion leaves the graph asserting facts for content that no longer exists.
• Decay monitoring: track retrieval-quality signals (answer groundedness, graph hit rates, stale-entity flags) so decay announces itself before users notice.

Refresh cadence follows source volatility: contracts and policy documents decay differently than tickets or messages, and the monitoring signals should differ accordingly.

Deployment and Residency: Where the Stores Live

Managed graph and vector services minimize operations but extend the PHI-or-IP data set across another processor, while self-hosted stores keep the combined corpus — documents, embeddings, entities, and their relationships — inside one boundary you control.

Deployment pattern	What you trade	Fits when
Managed graph + vector services	Operations convenience for another processor in the data path	Public or low-sensitivity corpora; fast start
Self-hosted stores on your infrastructure	Operating burden for full boundary control	Regulated or IP-sensitive corpora; residency obligations
Mixed (managed vector, self-hosted graph)	Split security model across two data sets	Transitional states; needs explicit data-class routing

Serving Decision Matrix: Enterprise LLM Inference Infrastructure

Serving Infrastructure Model	Compute & Memory Contention	P99 Tail Latency Predictability	Multi-GPU Tensor Parallelism Support	Optimal Enterprise Workload Fit
Shared Multi-Tenant Model APIs	Multi-tenant shared workers; opaque resource pooling	Severe tail latency jitter during peak concurrency spikes	Black-box; no control over model parallelism or KV cache sizing	Low-volume prototyping or asynchronous background tasks
Virtualized Cloud GPU Instances	Hypervisor vGPU slices subject to CPU/PCIe interrupts	Moderate jitter caused by neighboring tenant network bursts	High inter-node latency limits multi-GPU tensor scaling (TP=4/TP=8)	General internal apps with modest throughput requirements
OneSource Dedicated Private GPUs	Dedicated bare-metal hardware with 100% VRAM & compute reservation	Deterministic microsecond P99 response times under peak load	Dedicated RoCE v2 RDMA fabric enables low-latency TP=4/TP=8 scaling	Mission-critical, low-latency, regulated enterprise production serving

The residency question lands harder in GraphRAG than vector RAG because the graph itself is a derived corpus: entities and relationships re-identify individuals even when the source passages are anonymized, so the graph store inherits the full data classification. Database selection follows that boundary — managed cloud graph services on one side, self-hosted options positioned for security-focused deployments on the other — and for teams that need the whole stack inside one boundary, private environments such as OneSource Cloud's dedicated infrastructure host both stores under a single governed perimeter.

FAQ

Do GraphRAG accuracy claims hold up in practice?

Directionally yes, with conditions: vendor-cited benchmarks report large improvements over baseline vector RAG on multi-hop questions, and independent academic comparisons confirm hybrid advantages — but the size of the gain is workload-specific, so run the comparison on your own question set before committing the graph investment.

Can we add GraphRAG to an existing vector RAG deployment?

Yes — the hybrid pattern is the standard adoption path: keep the vector index serving, add the graph store and construction pipeline alongside, and let the orchestration layer fuse both retrievals. The incremental cost is exactly the graph-side pipeline and operations this page describes.

How often does the knowledge graph need refreshing?

As often as your sources change meaningfully: tie incremental extraction to document change events, schedule entity-resolution repair runs on a fixed cadence, and monitor retrieval-quality signals so decay announces itself before users notice.

Why deploy latency-sensitive LLM inference on OneSource private GPUs?

OneSource private GPU infrastructure delivers 100% dedicated bare-metal compute and VRAM, completely isolated from cross-tenant contention. This eliminates hypervisor scheduling jitter and shared-network packet collisions, ensuring deterministic P99 tail latency, sustained token throughput, and optimal tensor parallel scaling for production enterprise LLM serving.