Lexer Identity Resolution vs Splink
Lexer Identity Resolution
lexer.ioSplink
moj-analytical-services.github.ioSide by side
| Figure | Lexer Identity Resolution | Splink |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | Yes |
| Web | Yes | — |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | — | Yes |
| Plans published | 1 | 2 |
| Spec score · Identity Resolution Software | 7.1 (#20) | 8.4 (#2) |
| Matching | The identity resolution engine uses deterministic and probabilistic matching across identifiers such as email, phone, name, address, device IDs, loyalty numbers, and customer IDs. | It supports term frequency adjustments and user-defined fuzzy matching logic. |
| Support | Lexer offers implementation options described as self-serve guided, hybrid, or fully managed, and says support can be scoped to customer needs. | Users are directed to a public GitHub discussion forum for questions after consulting the documentation. |
Plans
In their own words
Lexer Identity Resolution
Lexer Identity Resolution has no write-up on Specifiction yet; its figures are above.
Splink
Splink is a free Python package for probabilistic record linkage, used to deduplicate records and connect them when unique identifiers are absent. Its linkage method follows the Fellegi-Sunter model and can be trained without labeled examples. Matching can include term-frequency adjustments and user-defined fuzzy logic. Splink creates SQL for a selected backend, with documented support for DuckDB, Spark, SQLite, and PostgreSQL. DuckDB is recommended for most users; Spark is recommended for very large linkages or when a Spark cluster is more accessible. The maker says it can run on DuckDB or big-data backends such as Spark for linkages of 100+ million records. It works best with standardized data across several columns that are not highly correlated, rather than a single bag-of-words column. Interactive visualizations help examine and diagnose models, predictions, and clusters. Install with pip or conda, with optional backend-specific installation for Spark and PostgreSQL. It runs on Linux, macOS, and Windows, as well as self-hosted environments.