Galileo AI

Galileo AI is a freemium AI observability and evaluation platform that helps teams monitor, evaluate, and guard LLM-powered applications — offering a free tier with 10,000 evaluated rows per month, with paid plans starting around $500/month for production workloads and enterprise tiers typically ranging from $2,000 to $10,000+ per month depending on volume and deployment needs.

Unlike general-purpose MLOps platforms, Galileo focuses specifically on the unique challenges of generative AI and large language model applications. The platform's Guardrail Metrics engine automatically scores LLM outputs for hallucination, factual correctness, tone, toxicity, and relevance — without requiring manually labeled ground-truth datasets. This approach allows teams to evaluate thousands of LLM responses in minutes rather than weeks of human review. According to Galileo's published benchmarks, ChainPoll achieves over 90% agreement with human evaluators on hallucination detection tasks, outperforming simple embedding-similarity methods by approximately 25 percentage points.

Galileo supports the full GenAI development workflow from prototyping through production. During development, teams use Galileo's Evaluate module to run experiments, compare prompt variations, and benchmark model performance across diverse test scenarios. The platform integrates with popular frameworks including LangChain, LlamaIndex, OpenAI, Anthropic, and custom model endpoints, making it straightforward to instrument existing applications — typically requiring fewer than 5 lines of code for initial setup. The platform supports evaluation across more than 15 built-in quality metrics out of the box.

In production, Galileo's Observe module provides real-time monitoring of deployed AI systems, surfacing quality regressions, latency anomalies, and cost trends. Teams can set custom alert thresholds on any metric and receive notifications when model behavior degrades. The platform captures full trace-level data for each request, allowing engineers to drill down from aggregate dashboards to individual problematic interactions.

Galileo's approach to hallucination detection is a key differentiator. The platform uses its proprietary ChainPoll methodology, which has been validated in peer-reviewed research, to identify when LLM outputs are not grounded in the provided context or contradict source documents. For RAG applications specifically, Galileo evaluates both retrieval quality (whether the right chunks were fetched) and generation quality (whether the model faithfully used those chunks), giving teams end-to-end visibility into the RAG pipeline.

The platform also includes a Protect module that enables teams to deploy real-time guardrails in production. These guardrails can block or flag responses that fail quality checks before they reach end users, adding a safety layer for customer-facing AI applications. As of early 2026, the platform reports processing over 500 million LLM evaluations across its customer base, serving teams at more than 100 enterprises and AI-native startups alike.

Galileo provides collaborative features including shared dashboards, annotation workflows, and role-based access control, making it suitable for cross-functional teams that include ML engineers, product managers, and domain experts who all need visibility into AI system behavior.