> ## Documentation Index > Fetch the complete documentation index at: https://docs.bensevern.dev/docs/llms.txt > Use this file to discover all available pages before exploring further. # ER vendor comparison > How GoldenMatch compares to other entity-resolution engines across OSS, identity-graph, cloud-managed, and enterprise MDM tiers. This page summarizes the entity-resolution landscape as recorded in the repository's vendor comparison. The intent is honest positioning: where GoldenMatch leads, and where other engines lead. Pricing is indicative, vendor surfaces change quickly, and academic F1 numbers are not enterprise outcomes on your real data. GoldenMatch numbers are reproducible from `docs/reproducing-benchmarks.md` in the repository. ## GoldenMatch baseline | Axis | GoldenMatch | | ------------------- | --------------------------------------------------------------------------------------- | | License | MIT (every package). | | Languages | Python (headline) + TypeScript (parity) + Rust (Postgres/DuckDB). | | Runtimes | Polars (≤500K), DuckDB (500K–50M), Ray (≥50M); Postgres via pgrx; DuckDB UDFs; edge JS. | | Throughput | 1M dedupe in \~12.3 min on 4-core / 16 GB; 100K fuzzy \~39s. | | Accuracy (DBLP-ACM) | F1 0.964 zero-config (hand-tuned ceiling 0.918). | | Accuracy (NCVR) | F1 0.972 zero-config. | | Zero-config | Introspective auto-config controller with cross-run memory and LLM fallback. | | PPRL | Bloom-filter PPRL, F1 0.924 on FEBRL4. | | AI-native surface | 69 MCP tools, agent skills, REST API. | ## OSS engines | Engine | License | Where it beats GoldenMatch | Where GoldenMatch beats it | | --------------------- | ---------------------------- | ------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Splink (UK MoJ) | MIT | Distributed Fellegi-Sunter at 1B+ rows (Spark); mature interactive m/u + comparison-viewer charting UI. | Non-PII accuracy (DBLP-ACM 0.964 vs 0.728); zero-config; polyglot; AI-native; PPRL. **Probabilistic head-to-head closed and reversed:** under the shared `bench_er_headtohead` evaluator (pairwise F1), GoldenMatch's probabilistic (Fellegi-Sunter) auto-config beats Splink on every dataset Splink scores — historical\_50k (Splink's flagship) 0.778 vs 0.757, febrl3 0.991 vs 0.965, synthetic\_person 0.998 vs 0.996 (and at the cluster level on historical\_50k, B-cubed F1 0.844 vs 0.789). These are **deterministic** as of #829. *(Pairwise F1 under one shared evaluator; the often-cited \~0.97 on historical\_50k is a cluster/entity-level metric, not exhaustive within-cluster pairwise F1 — Splink itself is \~0.75 pairwise here under the same harness. Splink skips bibliographic dblp\_acm; the probabilistic path is weak there (0.377) — use the weighted controller, which scores 0.964. Full bake-off: `docs/benchmarks/2026-06-09-splink-bakeoff.md`.)* The `type: probabilistic` matchkey also has EM-trained m/u, supervised m from labels, model persistence, match-weight waterfall, posterior calibration, and label-driven threshold analysis, plus FS scale-out on the bucket/Ray path (measured 6M dedupe in 162.6 s / 11.3 GB single-node). | | dedupe.io | BSD | Cleanest active-learning UX in OSS. | Accuracy without labels; throughput at scale; polyglot; AI-native; PPRL. | | RecordLinkage Toolkit | BSD | DBLP-ACM F1 0.923 (narrowly). | Throughput, scale, PPRL, AI-native, zero-config, polyglot. | | Zingg | AGPL + commercial | Native Spark scale. | MIT license; polyglot; AI-native; zero-config; PPRL. | | Senzing G2 CE | Apache wrapper + closed core | Native identity graph; bundled reference data; real-time latency. | OSS license; zero-config; AI-native; bibliographic/product matching; PPRL. | ## Identity-graph and cloud-managed | Engine | Where it beats GoldenMatch | Where GoldenMatch beats it | | ---------------------- | -------------------------------------------------------------- | --------------------------------------------------------------------------------- | | Quantexa | Graph-native output; decisioning layer; enterprise governance. | OSS and composable; time-to-value; AI-native; cost. | | Tilores | Identity-graph engine; GraphQL API; hosted by default. | OSS; edge runtimes; zero-config; AI-native; PPRL. | | AWS Entity Resolution | Scale is AWS's problem; Glue/Lake Formation integration. | Portability (laptop, Postgres, edge worker); zero-config; AI-native; multi-cloud. | | Snowflake Cortex Match | Runs in-warehouse, no data movement. | Full engine (blocking, clustering, golden records); portable; published F1. | ## Research SOTA | Engine | Where it beats GoldenMatch | Where GoldenMatch beats it | | --------------- | --------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------- | | Ditto (Megagon) | Abt-Buy F1 0.893 (product-matching SOTA) vs GoldenMatch 0.722 (+LLM) / 0.817 (+Vertex). | No training labels; non-product domains; throughput; AI-native; cheaper than fine-tuning. | ## Cross-cutting observations * **Zero-config is uncontested.** No other vendor publishes zero-config benchmark numbers. * **Identity graph is the next frontier.** Mainstream OSS engines emit clusters; Senzing, Quantexa, and Tilores emit a graph. * **AI-native is uncontested but unproven.** Nobody else ships MCP, agent skills, or LLM-budget-controlled borderline scoring. * **License matters.** MIT and BSD engines have outgrown AGPL and closed-core peers partly on embedding permissions. * **Portability is the wedge** against warehouse-native managed services: the same engine runs in app code, an edge worker, or a Postgres trigger.