Features, pricing, ratings, and pros & cons — compared head-to-head.
JFrog ML is a commercial mlsecops tool by JFrog. Pebblo is a commercial mlsecops tool by Daxa.ai. Compare features, ratings, integrations, and community reviews side by side to find the best mlsecops fit for your security stack.
CST VerdictBased on our analysis of NIST CSF 2.0 coverage, core features, integrations, company size fit, here is our conclusion:
Enterprise security and ML ops teams deploying models across multiple clouds need JFrog ML to enforce governance and detect anomalies before models reach production. The platform's centralized security controls, real-time monitoring with alerts, and multi-cloud support mean you're not stitching together separate tools for compliance, model tracking, and deployment,a real pain point at scale. The NIST DE.CM coverage is solid, but JFrog skews toward continuous monitoring and asset management over incident response automation, so teams expecting sophisticated breach containment workflows should look elsewhere.
Enterprise security teams building RAG applications and AI agents need Pebblo to enforce data access controls at the model layer, where traditional DLP and identity tools can't reach. The platform's permissions-aware connectors and Safe Retriever enforce policy compliance across vector databases and LLM calls, addressing the PR.AA and PR.DS gaps that emerge when AI apps bypass your existing governance stack. Skip this if your AI workloads are isolated experiments; Pebblo's value compounds only when you're operationalizing generative AI across sensitive data at scale.
Platform for building, deploying, managing & monitoring AI/ML workflows & models
AI security platform enforcing access control & governance for AI apps/agents.
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Common questions about comparing JFrog ML vs Pebblo for your mlsecops needs.
JFrog ML: Platform for building, deploying, managing & monitoring AI/ML workflows & models. built by JFrog. Core capabilities include Model training and fine-tuning, Model deployment via API endpoints and Kafka streams, Real-time model monitoring and alerts..
Pebblo: AI security platform enforcing access control & governance for AI apps/agents. built by Daxa.ai. Core capabilities include Permissions-aware data connectors with classification for enterprise data sources (Safe Connectors), Role-appropriate and compliant data retrieval from vector databases (Safe Retriever), Secure MCP agent data access with identity and policy control, including prompt injection protection (Safe MCP)..
Both serve the MLSecOps market but differ in approach, feature depth, and target audience.
JFrog ML differentiates with Model training and fine-tuning, Model deployment via API endpoints and Kafka streams, Real-time model monitoring and alerts. Pebblo differentiates with Permissions-aware data connectors with classification for enterprise data sources (Safe Connectors), Role-appropriate and compliant data retrieval from vector databases (Safe Retriever), Secure MCP agent data access with identity and policy control, including prompt injection protection (Safe MCP).
JFrog ML is developed by JFrog. Pebblo is developed by Daxa.ai. Vendor maturity, funding stage, and team size can be important factors when evaluating long-term viability and support quality.
JFrog ML integrates with AWS, Google Cloud, Microsoft Azure, Kafka. Pebblo integrates with OpenAI, MCP (Model Context Protocol). Check integration compatibility with your existing security stack before deciding.
JFrog ML and Pebblo serve similar MLSecOps use cases: both are MLSecOps tools, both cover Mlsecops. Review the feature comparison above to determine which fits your requirements.
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