Better. Faster. Cheaper.
One platform, measured against the five stacks it replaces — document extraction, data unification, graph engines, governance suites, and AI consumption layers. Every benchmark is public — run it yourself.
 | AI Readiness → | The Core | ← Trusted AI | ||
|---|---|---|---|---|---|
Unstructured vs. parsing pipelines | Structured vs. MDM suites | Graph + Ontology vs. property-graph engines | AI + Data Governance vs. governance platforms | AI Consumption vs. enterprise search & BI | |
| Better what only Fluree does | Extraction that lands as a knowledge graph.1
| Query your lake without moving it.2
| A graph you can fork, sign, and rewind.3
| Governance as a property of the data, not a tool watching it.4
| Answers with receipts.5
|
| Faster the benchmarks | ≈8 ms per document.6 The accuracy runners-up take seconds to minutes per doc. Escalates to a model only when a page earns it — most never do. | No pipeline in the path.7 Queries push down to Iceberg partitions directly — partition pruning, zero copy steps between the lake and the graph. Read more ↗ | 10.4× the next-fastest engine.8 19.4 ms geometric mean, all 105 queries answered at every scale to 21.5B triples — and fastest on reads and durable writes, on a competitor’s own suite. Read more ↗ | Governed at retrieval speed. Policy travels with the data — no metadata sync jobs, no catalog drift, no policy-engine hop per query. | 115K answers per hour.9 At 32 concurrent clients — 2–5× the nearest engines in the same throughput test. Agent JSON streams schema-once, byte-budgeted results built for LLM loops. Read more ↗ |
| Cheaper the bill | No GPU. No API key. Apache 2.0.10 Model inference only where the page demands it — the competition pays it on every page. Read more ↗ | No ETL line item. Nothing moves and nothing is stored twice — the lake stays the single copy. | Quarter the hardware, keep the speed.11 Cut 16 cores to 4 and queries move 19 ms → 25 ms — still 8× the runner-up at full spec. Zero sidecar bill: search, policy, reasoning all in-engine. Read more ↗ | Three line items become one. The catalog, the policy engine, and the lineage tool collapse into things the ledger simply does. | ≈22× fewer tokens. 77% lower cost per user.12 Modeled across realistic usage tiers: ≈$2.4M back over five years at 50→500 seats — an AI bill you can actually forecast. Read more → |
sources1#1 accuracy of 17 engines · opendataloader-bench2R2RML + partition pruning · docs318 differentiating features — most of any engine measured4Policy & SHACL enforced in-engine · docs5Fluree memory · docs6opendataloader-bench · CPU-only tier7Iceberg graph sources · docs8SPARQLoscope · WGPB · benchgraph — published results9BSBM throughput ramp · Agent JSON spec10Open source · fluree/fluree-doc-parse11Published resource-scaling run12Modeled TCO · The Tokenomics of Enterprise AI
The stack you don’t buy is the cheapest line item you’ll ever have.
Open standards at every layer. Pay as you go.
Named comparisons, claim by claim.
The matrix above is category-level by design. When you’re weighing specific vendors, these guides do the line-item work — sourced, dated, and honest about where the other side wins.