OpenUBA vs Soda
OpenUBA
openuba.org
Soda
soda.ioSide by side
| Figure | OpenUBA | Soda |
|---|---|---|
| Price from | — | $750/mo |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 3 |
| Spec score · Anomaly Detection Software | 7.4 (#13) | 8.5 (#2) |
| Deployment | The README describes the platform as Kubernetes-native and says it can be deployed to a Kind development cluster or a production Kubernetes cluster. | Soda offers three deployment models based on infrastructure and data privacy needs. |
| Intended users | The project README describes OpenUBA as designed for security analysts curious about how models work under the hood. | Soda describes its workflow as serving data engineers, data producers and consumers, governance teams, and platform teams. |
| Purpose | OpenUBA is an open-source User and Entity Behavior Analytics framework for security analytics. | Soda is a data quality platform for monitoring data quality, catching problems early, understanding issues at source, and taking action. |
| Security | The project README says model executions run in isolated Docker containers or Kubernetes jobs, with resource limits to contain misbehaving models. | Soda’s Trust Center lists SOC 2 Type 2, SOC 3, DORA, and GDPR under compliance. |
Plans
In their own words
OpenUBA
OpenUBA is a free, open-source User and Entity Behavior Analytics framework for security analytics. It analyzes behavior with machine-learning models to identify anomalies, assign risk scores, and produce classifications. The dashboard covers entity monitoring, risk trends, job orchestration, case management, and real-time behavioral baselines for tracking 10,000+ users. Data ingestion supports Spark, Elasticsearch, and CSV, and the project describes the platform as SIEM agnostic. Rule Studio provides a visual flow-graph builder for detection logic, while matching rules can trigger real-time alerts. Cases can be triaged, escalated, and resolved in built-in workflows. Models use a model.yaml configuration and MODEL.py interface with train() and infer() methods; examples use scikit-learn, TensorFlow, PyTorch, Keras, and NetworkX. A Kubernetes-native operator runs jobs in ephemeral containers, with GraphQL logs and monitoring. Deployment is self-hosted on Kind or production Kubernetes clusters.
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.