Confident AI vs RedAmon

Confident AI is an AI quality platform for evaluating applications during development and monitoring them in production. Teams can experiment with prompts, models, and parameters, run automated regression evaluations in CI/CD, and assess results with more than 50 metrics. Production tracing records inputs, outputs, latency, tokens, and execution spans, while live alerts indicate declining quality. Red-team testing addresses safety vulnerabilities and adversarial attacks. The platform supports Python and TypeScript SDKs, OpenTelemetry, and more than 20 framework and gateway integrations. APIs cover prompt versioning, dataset creation, trace ingestion, project provisioning, and governance policies. It is available through web and API access, or can be self-hosted in AWS, Azure, or GCP using Docker; setup typically takes 1–2 weeks. The free plan includes two seats, one project, five weekly test runs, and 1 GB-month of trace spans. Paid plans start at $200 a month.

RedAmon is a free, open-source framework for authorized red-team operations, including reconnaissance, exploitation and post-exploitation. It maps attack surfaces from domains, IP/CIDR targets or batches of domains, then merges findings into a Neo4j graph that its AI agent can query for planning and exploitation. GVM/OpenVAS provides network vulnerability scanning with more than 170,000 NVTs; separate tools detect secrets and scan for vulnerable or malicious packages against a local OSV database. The Secret Multiscanner has 1,060 detectors across 14 sources and optional live API verification. RedAmon supports twelve AI providers and more than 400 language models, and its MCP server lets external agents drive the framework. The Dockerized application runs on Linux, macOS and Windows and supports on-prem deployment. Tools, scanners and agents run in separate containers with per-job ephemeral filesystems and network namespaces. Rules of Engagement, approval gates and scope controls govern operations, with a guardrail blocking government, military and intergovernmental targets. The project warns that configured API keys and credentials are stored unencrypted in PostgreSQL, leaving database security to the user.