ModelRed's answer

Our primary audience is teams building and deploying large language models who need to ensure safety, reliability, and compliance. This includes AI startups bringing new products to market, research labs testing frontier models, and security or compliance teams at larger organizations. These users care about understanding vulnerabilities, proving trustworthiness, and avoiding costly failures before their models are widely adopted.

Dynamiq.ai's answer:

Heads of: Data Science, Engineering, Innovation at big enterprise companies

ModelRed's answer

ModelRed is built as a microservices-based platform using Go and TypeScript for backend services, with PostgreSQL for data storage and AWS S3/SQS for distributed job handling. The frontend is built with Next.js and React, with Prisma for data access. We integrate with major LLM providers including OpenAI, Anthropic, AWS Bedrock, AWS Sagemaker, Google Vertex, and Hugging Face, as well as custom REST endpoints. NVIDIA GPUs and related tools are planned for accelerating large-scale adversarial probe execution and model evaluations.

Dynamiq.ai's answer:

Kubernetes, OpenAI, Anthropic, Cohere, Golang, Python

ModelRed's answer

ModelRed is unique because it combines universal probes that catch common risks with domain-specific probes that uncover issues in sensitive fields like finance, healthcare, legal, and government. It also provides the ModelRed Score, a benchmark that makes it easy to compare models and track improvements over time. With support for OpenAI, Anthropic, AWS Bedrock, AWS Sagemaker, Google Vertex, Hugging Face, and custom endpoints, ModelRed delivers comprehensive and automated red-teaming across the AI stack.

ModelRed's answer

A person should choose ModelRed because it offers both universal and domain-specific probes, giving a more complete picture of how large language models perform in real-world and high-stakes scenarios. The ModelRed Score makes results easy to understand and compare across models, while automated reports help with compliance and ongoing monitoring. ModelRed also works across all major providers and custom endpoints, so teams can evaluate their entire AI stack in one place instead of relying on fragmented tools.

ModelRed's answer

ModelRed was born from the realization that large language models are being deployed faster than they are being secured. Early in our work with AI platforms, we saw that most models went live without rigorous testing for jailbreaks, data leakage, or misuse. At the same time, enterprises and startups alike were asking how they could prove their models were safe for real-world use. We created ModelRed to close that gap by offering automated red-teaming, domain-specific evaluations, and a standardized security benchmark. Our goal is to make security and trust a natural part of every LLM deployment, just as cloud and network security became essential in earlier technology waves.