Other tools guess.
We trace.
Plain-language differences between OWLGraph and the three retrieval architectures most teams evaluate alongside it: classic vector RAG, HippoRAG, and Microsoft's GraphRAG. If you've heard context engine, that's the category — OWLGraph is the one that reasons over a typed schema and shows its evidence chain. Honest about where each one wins.
Head-to-head across 8 capabilities.
| Capability | OWLGraph | Vector RAG | HippoRAG | GraphRAG (MSR) |
|---|---|---|---|---|
| Multi-hop questions | Native — typed traversal is the default path | Often fails silently; chunks don't carry relationships | Personalized PageRank over a knowledge graph; good but no typing | Hierarchical community summaries; multi-hop via summarization |
| Constraint joins ("approved for X AND not Y") | Direct via typed edges + disjointness rules | Cosine similarity can't enforce conjunctions | Possible but expensive; no constraint primitive | Possible via summarized communities; lossy |
| Evidence chain for every answer | Yes — typed entities + edges + source passages | Top-k chunks only; no inference trace | PPR scores per passage; no semantic trace | Community summaries cited; not chain-shaped |
| Schema / type enforcement | Typed schema — typed entities, typed edges, type rules | None | Implicit from KG triples | None at retrieval time |
| Setup time | 10 min with the SDK + a starter preset; longer with custom ontology | Minutes — paste in chunks | Hours — graph construction step | Hours — community detection at index time |
| Per-query latency | ~20s on complex multi-hop (agentic retrieval loop); ~2s on simple lookup | ~1-5s | ~5-15s (PPR computation) | ~10-30s (summary retrieval + reasoning) |
| Per-correct-answer cost | +11.3pp accuracy vs vector RAG (controlled benchmark) offsets the 4× per-query cost in trust-sensitive use | Lowest cost-per-query, but cost-per-correct-answer climbs with eval rigor | Cost scales with graph size + traversal depth | Front-loaded indexing cost; per-query cheap |
| Hosted as a service | Yes — owlgraph.ai DBaaS, SLA-backed | Yes (Pinecone, Weaviate, etc.) | No — self-host | No — self-host (Microsoft research code) |
When to choose what.
Choose OWLGraph when
- Your users ask multi-hop or constraint-based questions and you're already plateauing on vector RAG.
- You need to show users where an answer came from — regulated, professional-services, or trust-sensitive product.
- Your domain has natural types and relationships (medical concepts, products + suppliers + jurisdictions, legal entities, scriptural references).
- You want a hosted service with an SLA, not a research codebase to operate.
Stick with vector RAG when
- Your questions are mostly paraphrase or simple lookup — vector RAG hits parity here at lower cost.
- You can tolerate the 10–20% wrong-answer rate on harder questions (consumer-facing, low-stakes).
- Your corpus is homogeneous and well-chunked (e.g., a single product's documentation) — there's no graph to traverse.
Look at HippoRAG when
- You're a research team comfortable operating PPR pipelines.
- Your KG is untyped triples and you're not building toward a typed ontology.
Look at GraphRAG when
- Your corpus is large + heterogeneous and you want community summaries rather than precise retrieval.
- You have indexing budget upfront and can amortize the community-detection step.
Microsoft GraphRAG vs OWLGraph.
Microsoft GraphRAG is the free, DIY answer a platform team reaches for first — LLM-extracted entities plus community summaries, run in your own Azure tenancy. It's a real option when you have Python and Azure ops budget and want summaries, not precise retrieval. The difference is structural: their graph has no schema and no reasoning, so "audit" means a community-summary trace. OWLGraph's graph is typed and reasoned, so the audit is an actual inference path back to the source passage — and there's no pipeline for you to operate.
| Dimension | OWLGraph | Microsoft GraphRAG |
|---|---|---|
| Schema fidelity | Typed entities + typed edges, enforced | LLM-extracted entities; no schema |
| Reasoning at query time | Yes — type + relationship inference | No — retrieval over precomputed summaries |
| Evidence shape | Inference path to the source passage | Community-summary trace |
| Ops burden | Hosted; nothing to run | You operate the Python + Azure pipeline |
| Time to first query | ~90s from a corpus (schema induced for you) | Hours — community detection at index time |
What we don't do (yet).
- Self-hosted on your infra. We're hosted-only today. On the roadmap; not a 2026 deliverable.
- Sub-second latency on complex multi-hop. The agentic retrieval loop runs ~3 outer iterations on hard questions; mean latency on multi-hop is ~20s. We'll bring this down with caching + streaming over time, but it's a real tradeoff today.
- HIPAA BAA. See /security for current compliance posture.
- Free-tier without a credit card. Developer tier is unmetered for 14 days at signup; no card required.
See the difference on a
multi-hop question.
Start free and point it at your corpus, or try the live demo first — both show the evidence chain behind every answer. The eval report has the full +11.3pp-vs-vector-RAG breakdown on 100 controlled questions.