Senzing Entity Resolution vs Splink

Senzing Entity Resolution combines fragmented records into entity profiles and surfaces links among them for fraud, compliance, and AI applications. The maker describes its engine as real-time, relationship-aware, self-tuning, and self-correcting, without custom training or tuning. It says the engine starts in under one second and scales from tens to billions of records within a customer’s infrastructure. Decisions can include an audit trail explaining matches, contributing features, and a confidence score. Senzing says customer data stays in that infrastructure and does not flow to Senzing, Inc.

Deployment options include bare metal, Docker, Kubernetes, private cloud, AWS, Azure, and air-gapped environments. Integrations include Python, Java, C#, Rust, Node.js, and an MCP server; AWS Marketplace and Microsoft Marketplace handle real-time JSON requests and responses. The SDK’s free license covers 500 source records, while the AWS Marketplace trial covers 250,000. A listed subscription is $58,560 a year for 10M DSRs, with support included.

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.