About
Galileo Luna-2 is a fast, low-cost family of small language models (SLMs) purpose-built for production-grade AI evaluation and real-time guardrailing. Unlike relying on expensive frontier models such as GPT-4 for evaluation pipelines, Luna-2 achieves 0.95 accuracy at just $0.02 per million tokens and 152ms average latency—dramatically outperforming alternatives on cost, speed, and accuracy for safety and hallucination benchmarks. Luna-2 is designed to power always-on agentic evaluation workflows, providing simultaneous assessment of multiple metrics with low latency. It supports a broad range of evaluation categories including agentic metrics (tool error rate, tool selection quality, action advancement, and action completion) and safety metrics (PII leak detection, sexism, bias, and prompt injection). These capabilities enable teams to guardrail complex agentic pipelines before risky tool calls are executed—something that traditional content-safety APIs miss. The models use a decoder-only architecture with lightweight metric heads for deterministic, production-grade evaluation. Luna-2 compares favorably against Azure Content Safety and NVIDIA Nemo across key metrics. It is available to enterprise customers and can be integrated into any custom LLM evaluation pipeline for applications ranging from customer support bots to financial services automation. Luna-2 is ideal for AI engineering teams, MLOps practitioners, and enterprises that need scalable, cost-effective guardrails for their AI systems.
Key Features
- Ultra-Low-Cost Evaluation: Delivers production-grade AI evaluation at just $0.02 per million tokens—over 250x cheaper than GPT-4-class models—making always-on monitoring economically viable.
- Real-Time Agentic Guardrails: Intercepts risky agent actions before tool execution by evaluating tool selection quality, action completion, and action advancement in milliseconds.
- Comprehensive Safety Metrics: Detects PII leaks, bias, sexism, and prompt injection attacks, covering safety dimensions that content-safety APIs often miss in agentic workflows.
- Multi-Metric Simultaneous Evaluation: Supports parallel evaluation of multiple custom and built-in metrics with low latency, ideal for high-throughput, real-time deployment pipelines.
- Fine-Tuned SLM Architecture: Uses decoder-only small language models with lightweight metric heads for deterministic verdicts, matching or beating frontier LLM judges on safety and hallucination benchmarks.
Use Cases
- Guardrailing customer support chatbots to prevent unauthorized actions such as number porting or identity data exposure without proper verification.
- Monitoring financial services AI agents to detect and block transactions that violate policy limits or follow unsafe execution paths.
- Running always-on, high-throughput production evaluation pipelines for LLM outputs across safety, hallucination, and quality dimensions.
- Replacing expensive frontier model judges in CI/CD evaluation workflows to reduce costs while maintaining or improving accuracy.
- Detecting prompt injection attacks and PII leaks in real time across enterprise AI deployments before they reach end users.
Pros
- Industry-Leading Cost Efficiency: At $0.02 per 1M tokens and 152ms average latency, Luna-2 is dramatically cheaper and faster than GPT-4-class evaluators, with higher measured accuracy.
- Agentic-First Design: Purpose-built to evaluate and guardrail agentic AI systems—catching tool misuse and unsafe actions that generic safety APIs are not equipped to handle.
- Broad Safety Coverage: Covers a wide range of safety and quality metrics out of the box, reducing the engineering effort required to build comprehensive evaluation pipelines.
Cons
- Enterprise Access Required: Luna metrics and full capabilities are only available to customers upon request, limiting accessibility for smaller teams or individual developers.
- Narrow Specialization: Luna-2 is optimized specifically for AI evaluation and guardrailing; it is not a general-purpose language model suitable for content generation or reasoning tasks.
- Sales-Gated Onboarding: Full access requires speaking with a sales team, which can slow adoption for teams that prefer self-serve or instant sign-up workflows.
Frequently Asked Questions
What is Galileo Luna-2?
Luna-2 is a family of small language models (SLMs) developed by Galileo, specifically fine-tuned for real-time AI evaluation, safety guardrailing, and production monitoring of LLM and agentic AI systems.
How does Luna-2 compare to GPT-4 for AI evaluation?
Luna-2 achieves 0.95 accuracy at $0.02 per 1M tokens with 152ms latency, versus GPT-4's 0.94 accuracy at $5.00 per 1M tokens and 3200ms latency—making Luna-2 over 250x cheaper and 20x faster while slightly more accurate.
What types of metrics does Luna-2 support?
Luna-2 supports agentic metrics (tool error rate, tool selection quality, action advancement, action completion) and safety metrics (PII leak, bias, sexism, prompt injection), as well as custom application-specific metrics.
Can Luna-2 guardrail agentic AI workflows?
Yes. Luna-2 is specifically designed to evaluate and intercept risky agent actions before tool execution, enabling real-time guardrailing for complex multi-step agentic pipelines.
How do I get access to Luna-2?
Luna metrics are available to enterprise customers upon request. You can get started for free on the Galileo platform and contact sales to enable Luna-2 capabilities for your account.
