Splink vs Tilores
Splink
github.com
Tilores
tilores.ioSide by side
| Figure | Splink | Tilores |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 4 |
| Spec score · Data Deduplication Software | 7.8 (#20) | 8.3 (#5) |
| Matching | It supports term frequency adjustments and user-defined fuzzy matching logic. | The platform combines deterministic rules and fuzzy matching, with explainable links, a quality score, and an audit trail. |
| Purpose | Splink is a Python package for probabilistic record linkage that deduplicates and links records without unique identifiers. | Tilores resolves duplicate and conflicting records across systems into unified entity profiles in real time. |
| Support | The documentation directs users with questions to the project’s GitHub discussion forum. | Production quotes can scope support from standard technical support to a dedicated customer success engineer. |
| What it does | Splink is a Python package for probabilistic record linkage that deduplicates and links records without unique identifiers. | Tilores links duplicate and conflicting records from separate systems into a single resolved entity for fraud, compliance, Customer 360 and AI workloads. |
Plans
Tilores
- Tilores StudioFree
- Tilores Studio Local ModeFree
- Production TiloresPriced on request
- Tilores ProductionPriced on request
In their own words
Splink
Splink is a free Python package for probabilistic record linkage: it deduplicates and connects records without unique identifiers. Its core algorithm is based on the Fellegi–Sunter model, and it can train models without training data using an unsupervised approach. Matching supports term-frequency adjustments and user-defined fuzzy logic. Interactive outputs help users inspect models and diagnose linkage problems. Splink documents DuckDB, Spark, PostgreSQL, and SQLite backends; DuckDB is installed by default, while Spark and PostgreSQL have optional installs. The project says it can run with big-data backends such as Spark on datasets of 100 million or more records, and can link a million records on a laptop in around a minute. It works best with standardised data containing multiple columns that are not highly correlated. Installation is through pip or conda and requires Python 3.10 or later. The project lists Linux and self-hosted use and an MIT license.
Tilores
Tilores resolves duplicate and conflicting records into unified entity profiles in real time. It maps, normalizes, and enriches fields such as names, phone numbers, and addresses before applying deterministic rules and fuzzy matching. Results can include explainable links, a quality score, and an audit trail. A real-time GraphQL API supports entity retrieval, ranked fuzzy searches, and aggregation of source records. Listed connectors include Salesforce, HubSpot, and Snowflake, alongside webhooks and event streaming. Deployment options include managed AWS, a customer's AWS account or another cloud, and on-premise installations. Developers can connect entity resolution to LangChain and Amazon Bedrock through IdentityRAG; the company says it works with any LLM through LangChain. Tilores Studio runs locally on macOS Apple Silicon, Windows x64, or Linux x86_64, storing data on the user's computer. Its free local mode allows up to 100,000 records per deployment. Production pricing is by custom quote, based on data volume, deployment, service requirements, and support.