PyOD vs Soda
PyOD
pyod.readthedocs.ioSoda
soda.ioSide by side
| Figure | PyOD | Soda |
|---|---|---|
| Price from | Free | $750/mo |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 3 |
| Spec score · Anomaly Detection Software | 8.4 (#3) | 8.5 (#2) |
| Founded | 2017 | 2019 |
| Integrations | Optional pip extras enable PyTorch, SUOD, XGBoost, model combination, thresholding, embeddings, OpenAI embeddings, Hugging Face encoders, graph models, MCP, and audio features. | Soda advertises integrations with data-stack tools, catalogs, orchestration systems, incident-management tools and CI/CD workflows. |
| Intended users | The project says PyOD serves academic research and commercial products worldwide. | Soda describes its workflow as serving data engineers, data producers and consumers, governance teams, and platform teams. |
| Purpose | PyOD is a Python library for anomaly detection. | Soda is a data quality platform for monitoring data quality, catching problems early, understanding issues at source, and taking action. |
| Support | The FAQ invites users to open an issue or contact the maintainer at [email protected]. | Soda lists premium support as an Enterprise feature and provides a support contact at [email protected]. |
Plans
In their own words
PyOD
PyOD is a free Python library for anomaly detection, with documented detectors for tabular, time-series, graph, text, image, and audio data. Its documentation lists 61 detectors available through a common API. Users can work with the classic detector API or ADEngine, which profiles data, selects benchmark-backed detectors, runs multiple detectors in parallel, computes consensus scores, and reports diagnostics. ADEngine also works as a standalone Python API without an LLM. PyOD includes an agentic investigation workflow and activation paths for Claude Code, Codex, and MCP-compatible agents, including an od-expert skill and optional MCP server. Installation is available through pip, conda-forge, or source, and requires Python 3.9 or higher. Optional pip extras enable capabilities including PyTorch, graph detectors, embeddings, audio, and MCP support. The project serves academic research and commercial products. Its model persistence guidance warns that pickle and joblib can execute arbitrary Python code during deserialization, requiring callers to pass trusted=True when loading artifacts. ADEngine describes its quality verdict as a heuristic rather than a guarantee and recommends validation against held-out labels or domain review.
Soda
Soda is a data quality platform for monitoring production data, testing data through development and deployment, and investigating issues. Its machine-learning-powered observability looks for unexpected changes without requiring every rule to be set in advance. Teams can test data during development, deployment, transformation, and CI/CD workflows. Data contracts let producers and consumers specify expected schemas, data types, value ranges, and other constraints. Soda lists integrations including Databricks, Snowflake, PostgreSQL, Airflow, dbt, Slack, Microsoft Teams, Jira, and PagerDuty, and offers three deployment models for different infrastructure and privacy needs. Its listed database support includes Athena, BigQuery, Databricks SQL, Dremio, DuckDB, Fabric, MySQL, Oracle, PostgreSQL, Redshift, Spark Dataframe, Snowflake, SQL Server, Synapse, and Trino. The Free plan is free and includes pipeline testing, metrics observability, and alerting and ticketing integrations. Team is $750 a month; Enterprise has no listed price. Soda lists SOC 2 Type 2, SOC 3, DORA, and GDPR under compliance. It was founded in 2019 and lists locations in Chicago, Illinois, and Brussels, Belgium.