Qualifire AI - 2026 Pricing, Features, Reviews & Alternatives

Qualifire AI is a cloud-based platform for evaluating, securing, observing, and managing LLM applications in production. It delivers real-time guardrail enforcement, continuous output evaluation, pre-production agentic testing, and full observability across RAG pipelines, chatbots, and agentic frameworks. Deployable via proxy gateway or API, with on-premises and hybrid (customer-hosted cloud) options available, including listing on AWS Marketplace. Engineering and AI teams across finance and other industries use it to enforce safety, reliability, and compliance standards without major code changes.

At the core of the platform is a suite of fine-tuned small language models (SLMs), each specialized for a distinct evaluation task: prompt injection detection, content safety moderation, context grounding verification, MCP tool selection quality assessment, and governance policy enforcement. SLM-based evaluation adds under 50ms latency per inference and runs on low-cost GPUs, making it significantly more cost-efficient than general-purpose LLM-based judges. At runtime, the platform blocks, retries, redacts, or returns default responses when risks are detected, with sub-20ms guardrail latency. Build-time evaluation and the Rogue CI/CD pipeline red-team and stress-test AI agents across adversarial and real-world scenarios before deployment, reproducing failures with deterministic artifacts.

Qualifire AI supports ISO/IEC 42001:2024 compliance through real-time output evaluation, policy enforcement, and automated audit logging and reporting. It is SOC 2 Type II certified. Policy enforcement supports ready-made templates or custom policies in multiple file formats, monitored around the clock via a dashboard. All logs and evaluation results are encrypted in transit and at rest, with an opt-out available for prompt and completion storage. Observability tooling provides complete tracing and logging of every LLM interaction, enriched with evaluation results and action tracking, giving enterprises the auditability required for production-scale AI deployment.