Agentic Soc Platform vs Mostly AI

Mostly AI centers on high-fidelity synthetic data generation using the TabularARGN model, with multi-table referential integrity, time-series support, and differential privacy. Its agentic data science layer automates training and sampling, and the natural-language AI Assistant can generate Python code on production data. It integrates deeply with major data platforms like Databricks, Snowflake, and AWS, and offers an open-source SDK under Apache v2. Conversely, Agentic SOC Platform (ASP) is an open-source SOC workspace that unifies SIEM, SOAR, and knowledge management. It ingests alerts from Splunk and ELK, correlates them into cases, and runs playbooks combining LLM analysis with SOAR-style actions. ASP provides AI investigation drafts with severity and confidence scores, automatic IOC enrichment, and a knowledge loop to capture learnings. It supports Python modules for custom rules and offers on-prem deployment with no data leaving the network. While Mostly AI focuses on data privacy and analytics, ASP targets security triage and automation. Both leverage open-source components, but Mostly AI is commercially licensed (contact pricing), whereas ASP is entirely free under MIT.

Mostly AI requires contacting sales for a custom quote, indicating enterprise-level pricing without a free tier or transparent pricing. This likely includes support and infrastructure costs, making it suitable for larger organizations with budget. In contrast, Agentic SOC Platform is completely free and open-source under the MIT license, allowing unlimited use, customization, and on-prem deployment. There are no hidden costs or paid tiers. For teams that need synthetic data at scale and can invest in infrastructure (Kubernetes/OpenShift), Mostly AI may be justified, but for security teams wanting AI-augmented SOC capabilities without financial barriers, ASP is the clear choice.