Confident AI vs RedFang

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.

RedFang is a cloud AI red-team scanner aimed at indie builders. It scans AI agents, GitHub repositories and application URLs, including listed customer-service chatbot targets. Before scanning, it requires a TXT-record or file-upload challenge to verify target ownership. RedFang says scans take five minutes and return grades from A+ to F. Seven v1 categories cover direct prompt injection, tool misuse, sensitive data leakage, output-as-attack-vector, agent overreach, denial-of-wallet and system-prompt extraction. Repository scans look for exposed keys, broken authentication and AI-introduced bugs. The stated methodology follows the OWASP LLM Top 10 threat model and tests 12 known direct prompt-injection patterns per scan. Reports can include plain-language findings, the successful prompt, a curl reproduction command, fix code and a retest. PDF and HTML exports are listed. Free preview costs nothing; Full report costs $19 a month. A shareable badge shows the latest grade, with weekly, daily or continuous scan cadence by tier.