Galileo Observe

Detects factual inconsistencies and fabricated information in LLM-generated responses by analyzing semantic coherence between model outputs and source context. Uses research-backed metrics to identify when models generate plausible-sounding but unsupported claims, with real-time flagging of hallucination patterns across production traffic without requiring manual annotation.

Measures how well LLM responses stay grounded in and utilize the retrieved context documents, scoring the degree of semantic alignment between generated answers and source material. Evaluates whether the model is actually using provided context versus relying on parametric knowledge, with scoring that can be customized per use case and tracked across retrieval quality improvements.

Analyzes millions of signals across traces to identify recurring failure patterns (e.g., 'date-based queries fail 40% of the time', 'tool selection fails when context exceeds 5K tokens') and generates prescriptive recommendations for fixes (e.g., 'Add few-shot examples to demonstrate correct tool input'). Uses pattern recognition across models, prompts, functions, context, and datasets to surface hidden issues.

Offers deployment flexibility for Enterprise customers with hosted (default), VPC (private cloud), and on-premises deployment options. Enables organizations with strict data residency, compliance, or security requirements to run Galileo observability infrastructure in their own environments while maintaining access to Luna models and evaluation capabilities.

Blocks unsafe or low-quality LLM outputs in real-time before they reach users, using Luna models and evaluation logic to detect issues and trigger guardrail actions. Available on Enterprise tier with dedicated low-latency inference servers, enabling sub-second evaluation and blocking decisions for production traffic.

Provides enterprise-grade access control with role-based access control (RBAC), single sign-on (SSO), and comprehensive audit logging for compliance. Enables organizations to manage user permissions, enforce authentication policies, and maintain audit trails of all evaluation and monitoring activities for regulatory compliance.

Tracks and displays the cost of running evaluations, including LLM-as-judge costs (e.g., $0.0733 per run with GPT-4o and 3 judges) and Luna model costs (claimed 97% cheaper). Enables teams to understand evaluation economics and optimize evaluation strategies by comparing cost vs accuracy tradeoffs.

Evaluates whether retrieved documents are relevant, complete, and sufficient to answer user queries by analyzing retrieval precision/recall and identifying failure modes like missing documents, ranking errors, or semantic gaps. Surfaces patterns in retrieval failures (e.g., 'queries about Q3 financials consistently retrieve Q2 documents') and recommends fixes like embedding model tuning or chunking strategy changes.

Ingests 100% of production traces from LLM and RAG applications, analyzes them against evaluation metrics in real-time, and triggers alerts when quality degrades or anomalies are detected. Supports trace-based pricing (5K-unlimited traces/month depending on tier) with configurable alert thresholds for hallucination rates, latency, cost, and custom metrics, enabling teams to catch production issues before users report them.

Runs evaluation using distilled, compact Luna models instead of full-size LLM-as-judge evaluators, achieving claimed 97% cost reduction while maintaining evaluation quality. Luna models are proprietary to Galileo and optimized for specific evaluation tasks (hallucination detection, context adherence, etc.), running on dedicated inference servers with low-latency guarantees for production use.

Allows teams to define custom evaluation logic beyond the 20+ built-in evaluators, enabling domain-specific quality checks tailored to application requirements. Supports unlimited custom evaluators on all pricing tiers and integrates with the trace ingestion pipeline to run custom logic against production data, though the mechanism for defining custom evaluators (code, YAML, UI builder) is not documented.

Evaluates agent decision-making by analyzing tool selection accuracy, action sequences, and failure modes in agentic workflows. Tracks whether agents select appropriate tools for tasks, identifies when agents get stuck in loops or make incorrect decisions, and provides visibility into multi-step reasoning patterns across production agent deployments.

Evaluates LLM outputs for safety risks including harmful content, prompt injection vulnerabilities, jailbreak attempts, and policy violations. Provides both evaluation metrics for monitoring safety in production and real-time guardrails (Enterprise tier) that can block unsafe outputs before they reach users, with integration to NVIDIA NeMo Guardrails for additional safety controls.

Manages evaluation datasets built from synthetic data, development data, and live production traces, with support for subject matter expert annotations and versioning. Enables teams to build evaluation datasets from production failures, curate them with expert labels, and use them for continuous evaluation and model improvement without manual data collection.

Ingests application traces through a Model Context Protocol (MCP) server integration, capturing models, prompts, functions, context, datasets, and traces in a structured format. Enables seamless integration with LLM applications and agents without requiring custom API clients, with automatic context extraction and storage for evaluation and analysis.