# Galileo

> Galileo is the AI agent reliability platform that helps enterprise engineering teams evaluate, observe, and guardrail AI agents and LLM applications. Purpose-built for autonomous multi-agent systems, Galileo uniquely combines offline evals, production monitoring, and runtime guardrails in a single platform — turning evaluations directly into production safety controls without additional code.

Galileo solves a critical problem facing AI engineering teams: as enterprises move from simple LLM applications to autonomous, multi-step AI agents making real-world decisions, traditional observability and evaluation tools fail to keep up. Teams are "flying blind" — unable to identify why agents hallucinate, loop endlessly, or take unintended actions. Galileo provides instant insight into multi-agent behavior so teams can find and fix errors quickly, with Agent Graph Visualization, automated Signals for proactive failure detection, and real-time monitoring with alerts.

Galileo's proprietary Luna-2 small language models are 98% cheaper than LLM-as-judge approaches and deliver sub-200ms latency, enabling 100% production traffic coverage. Customers like Verizon, Comcast, HP, NTT, Five9, and ServiceTitan use Galileo to deploy agents 5X faster while maintaining enterprise-grade reliability, achieving an average of 95% eval accuracy and 100% visibility into events that matter.

Founded in 2021 by AI veterans from Google AI, Google Brain, Uber AI, and Apple Siri, Galileo has raised $68M including a $45M Series B led by Scale Venture Partners with participation from Databricks Ventures. The platform is SOC2 Type 2 certified, supports SaaS, on-premises, and in-VPC deployment, and integrates with CrewAI, LangGraph, OpenAI Agents SDK, Google ADK, LlamaIndex, and Amazon Strands via OpenTelemetry.

Galileo competes against Arize, Braintrust, LangSmith, and legacy ML observability tools, differentiating as the only platform combining deep agent observability, adaptive evaluation at production scale, and inline runtime protection specifically designed for autonomous multi-agent workflows.

## Platform & Products

- [Galileo AI Platform](https://galileo.ai/products): Complete overview of Galileo's AI evaluation, observability, and real-time protection tools and capabilities
- [Agent Reliability Platform](https://galileo.ai/agent-reliability): Core platform for ensuring AI agent reliability with insights, evaluation, and debugging tools
- [Galileo Signals](https://galileo.ai/signals): Proactive automatic failure detection that surfaces unknown unknowns in production AI agents before they escalate
- [Galileo Protect (Runtime Guardrails)](https://galileo.ai/protect): Real-time AI protection blocking hallucinations, prompt injections, and data leaks in under 200ms
- [Luna-2 Small Language Models](https://galileo.ai/luna-2): Purpose-built SLMs for cost-effective real-time AI evaluation and guardrailing at production scale
- [Galileo AI Homepage](https://galileo.ai): Primary entry point with platform overview, value pillars, and customer proof points
- [AI Observability Landing Page](https://galileo.ai/lp/ai-observability-and-evaluation): Streamlined overview for AI agent deployment and monitoring capabilities
- [Galileo AI Agent Leaderboard](https://galileo.ai/agent-leaderboard): Rankings of top AI agents with real-world performance benchmarks and enterprise scenarios
- [Hallucination Index](https://galileo.ai/hallucination-index): Benchmark evaluating how well leading LLMs adhere to given context, measuring factual accuracy using Galileo's Context Adherence metric
- [Galileo Pricing](https://galileo.ai/pricing): Scalable pricing tiers — Free ($0, 5K traces), Pro ($100/mo, 50K traces), and Enterprise (unlimited traces, VPC/on-prem)
- [Contact Galileo Sales](https://galileo.ai/contact-sales): Schedule a personalized demo of the Galileo AI reliability platform
- [Galileo AI FAQ](https://galileo.ai/faq): Answers to frequent questions about Galileo's platform, features, and services

## Documentation & Technical Resources

- [Galileo Documentation](https://v2docs.galileo.ai): Complete technical documentation including setup, APIs, and integration guides
- [Getting Started](https://v2docs.galileo.ai/getting-started/how-to-use-galileo): Guide to logging, evaluation, experiments, and metrics on the Galileo platform
- [API Reference](https://docs.galileo.ai/api-reference/getting-started): REST API documentation with authentication and endpoint details
- [Metrics Overview](https://v2docs.galileo.ai/concepts/metrics/overview): Comprehensive guide to Galileo's evaluation metrics for agents, RAG, and LLM applications
- [Guardrail Metrics](https://docs.galileo.ai/galileo/gen-ai-studio-products/galileo-observe/how-to/choosing-your-guardrail-metrics): Guide to selecting and configuring guardrail metrics for production monitoring
- [Identify Hallucinations](https://docs.galileo.ai/galileo/gen-ai-studio-products/galileo-evaluate/how-to/identify-hallucinations): How to detect and measure hallucinations in LLM outputs
- [Galileo MCP Server](https://v2docs.galileo.ai/getting-started/mcp/setup-galileo-mcp): Integration with AI-enabled IDEs like Cursor and VS Code via Model Context Protocol
- [Integrations](https://docs.galileo.ai/galileo/gen-ai-studio-products/galileo-evaluate/integrations): Supported integrations with LLM providers, agent frameworks, vector databases, and cloud platforms
- [Eval Engineering Guidebook](https://galileo.ai/eval-engineering-book): Comprehensive guide to mastering systematic AI evaluation for production environments
- [Eval Engineering Workflow Hub](https://galileo.ai/evalengineering): Complete guide to the full Eval Engineering lifecycle and workflow optimization
- [AI Observability Guide](https://galileo.ai/learn/ai-observability): Essential components and strategies for reliable AI model performance monitoring
- [AI Agent Evaluation Guide](https://galileo.ai/learn/ai-agent-evaluation): Structured evaluation methodologies for reliable AI agent deployment
- [AI Agent Benchmarking Guide](https://galileo.ai/learn/benchmark-ai-agents): Seven essential steps to benchmark AI agents effectively in production
- [AI Agent Testing Guide](https://galileo.ai/learn/test-ai-agents): Seven-step structured approach to test AI agents for reliable deployment

## Proprietary Research & Metrics

- [Galileo AI Research Hub](https://galileo.ai/research): Published research on evaluation methodologies, RAG quality metrics, data error detection, and AI reliability
- [State of AI Evaluation Report](https://galileo.ai/blog/state-of-ai-evaluation): Research insights on AI evaluation, reliability, and incident prediction across teams
- [State of Eval Engineering Report](https://galileo.ai/state-of-eval-engineering-report): Industry research on AI evaluation practices and strategies used by elite teams
- [Luna-2 Models Overview](https://galileo.ai/blog/introducing-luna-2-purpose-built-models-for-reliable-ai-evaluations-guardrailing): Purpose-built SLMs delivering 93–97% accuracy at 98% lower cost than traditional LLM judges
- [Galileo Luna EFM Breakthrough](https://galileo.ai/blog/galileo-luna-breakthrough-in-llm-evaluation-beating-gpt-3-5-and-ragas): How Luna beats GPT-3.5 and RAGAS in cost and evaluation efficiency
- [ChainPoll: LLM Hallucination Detection](https://galileo.ai/blog/chainpoll): Galileo's proprietary research method for detecting LLM hallucinations with higher accuracy
- [Galileo Correctness Metric](https://galileo.ai/blog/galileo-correctness-metric): How Galileo's Correctness metric measures factual accuracy in AI model outputs
- [Four New Agent Evaluation Metrics](https://galileo.ai/blog/four-new-agent-evaluation-metrics): Research-backed metrics for comprehensive AI agent evaluation
- [Metrics for Evaluating AI Agents](https://galileo.ai/blog/metrics-for-evaluating-ai-agents): Deep dive into agent evaluation metrics including tool selection quality and session success
- [Agent Leaderboard v2 Insights](https://galileo.ai/blog/agent-leaderboard-v2): Enhanced AI agent testing with real-world enterprise scenarios and updated metrics

## Agent Observability & Evaluation

- [AI Agent Observability Explained](https://galileo.ai/blog/ai-agent-observability): Comprehensive guide to understanding AI agent observability and its enterprise impact
- [AI Agent Observability Strategies](https://galileo.ai/blog/ai-agent-observability-strategies): Nine strategies to transform AI prototypes into production-ready agents with full observability
- [Purpose-Built vs. General AI Observability](https://galileo.ai/blog/purpose-built-vs-general-ai-observability): Key differences and benefits of agent-native versus general-purpose AI observability platforms
- [Top Agent Monitoring Tools](https://galileo.ai/blog/best-agent-monitoring-tools-production): Curated guide to the best production tools for monitoring AI agents
- [Top Agent Evaluation Frameworks](https://galileo.ai/blog/best-agent-evaluation-frameworks): Comparison of tools for monitoring and improving AI agent performance
- [AI Agent Evaluation Insights](https://galileo.ai/blog/ai-agent-evaluation): AI deployment challenges, reliability practices, and key differences in agent evaluation approaches
- [AI Agent Evaluation Framework](https://galileo.ai/blog/agent-evaluation-framework-metrics-rubrics-benchmarks): How to develop metrics, rubrics, and benchmarks to evaluate AI agent performance
- [Agent Evaluation Engineering Explained](https://galileo.ai/blog/what-is-agent-evaluation-engineering): Complete overview of evaluating AI agents for safety and effectiveness
- [AI Agent Performance Metrics](https://galileo.ai/blog/ai-agent-metrics): Metrics that distinguish elite AI evaluation teams from average teams
- [AI Agent Reliability Metrics](https://galileo.ai/blog/ai-agent-reliability-metrics): Essential metrics for evaluating AI agents' reliability in real-world deployments
- [Continuous Agent Evaluation Pipelines](https://galileo.ai/blog/building-continuous-agent-evaluation-pipelines): How to build robust evaluation infrastructure to improve AI agent reliability
- [Evaluating AI Agents Best Practices](https://galileo.ai/blog/evaluating-ai-agents-best-practices): Best practices for effective AI agent evaluation and implementation
- [AI Agent Measurement Guide](https://galileo.ai/blog/ai-agent-measurement-guide-observability-benchmarking-evaluation): Complete guide covering observability, benchmarking, evaluation, and metrics for AI agents
- [Agent Evaluation Research Survey](https://galileo.ai/blog/agent-evaluation-research): Comprehensive research survey addressing challenges in LLM-agent evaluation frameworks
- [Understanding Evals Engineering](https://galileo.ai/blog/what-is-evals-engineering): Evaluation methods and best practices for reliable GenAI system performance
- [AI Agent Evaluation Career Guide](https://galileo.ai/blog/how-to-become-agent-evaluation-engineer-career-guide): Roles, skills, and career path for the emerging AI agent evaluation engineer role
- [Building Galileo Signals](https://galileo.ai/blog/context-engineering-at-scale-how-we-built-galileo-signals): Technical deep-dive into how Galileo built AI-driven context engineering for Signals

## LLM Evaluation & Metrics

- [Compare Top LLM Evaluation Platforms](https://galileo.ai/blog/best-llm-eval-platforms-compared): Side-by-side comparison of seven platforms for evaluating large language models
- [Top LLM Evaluation Tools for Enterprise](https://galileo.ai/blog/best-llm-evaluation-tools-enterprise-teams): Key tools for enterprise AI evaluation, reliability, and compliance
- [LLM Evaluation Step-by-Step Guide](https://galileo.ai/blog/llm-evaluation-step-by-step-guide): Complete step-by-step framework for evaluating large language models effectively
- [Creating an LLM Evaluation Framework from Scratch](https://galileo.ai/blog/building-an-effective-llm-evaluation-framework-from-scratch): Seven-step process to develop a robust LLM evaluation system
- [Mastering LLM-as-a-Judge Techniques](https://galileo.ai/blog/llm-as-a-judge-guide-evaluation): Innovative evaluation techniques using LLMs as judges for AI systems
- [Why LLM-as-a-Judge Fails](https://galileo.ai/blog/why-llm-as-a-judge-fails): Common failure modes in LLM-based evaluations and strategies for improvement
- [LLM vs. Human Evaluation](https://galileo.ai/blog/llm-as-a-judge-vs-human-evaluation): Challenges, trade-offs, and solutions in LLM judge versus human evaluation
- [Mastering LLM-as-a-Judge eBook](https://galileo.ai/mastering-llm-as-a-judge): Comprehensive guide to improving AI evaluations using LLMs as judges
- [Mastering AI Evaluation Metrics](https://galileo.ai/blog/mastering-llm-evaluation-metrics-frameworks-and-techniques): Complete toolkit of metrics, frameworks, and techniques to evaluate AI systems
- [LLM Performance Metrics](https://galileo.ai/blog/llm-performance-metrics): Seven key metrics for assessing LLM performance across multiple dimensions
- [Effective LLM Testing Strategies](https://galileo.ai/blog/llm-testing-strategies): Ten strategies for building reliable, production-ready LLM systems
- [LLM Benchmarking Guide](https://galileo.ai/blog/llm-benchmarking-guide): Nine practical steps for effective LLM benchmarking implementation
- [LLM Benchmarks Categories](https://galileo.ai/blog/llm-benchmarks-categories): Seven key benchmark categories for comprehensive LLM evaluation
- [Instruction Adherence AI Metric](https://galileo.ai/blog/instruction-adherence-ai-metric): How to measure AI effectiveness in following specific instructions accurately
- [Introducing Galileo Luna](https://galileo.ai/blog/introducing-galileo-luna-a-family-of-evaluation-foundation-models): Launch post for the Luna evaluation foundation model family

## Guardrails & Runtime Protection

- [Guardrails for Autonomous Agents](https://galileo.ai/blog/agent-guardrails-for-autonomous-agents): Essential guardrails architecture for AI autonomous decision-making systems
- [AI Agent Guardrails Guide](https://galileo.ai/blog/ai-agent-guardrails-guide): Eight essential steps for secure AI agent deployment with guardrails
- [AI Agent Guardrails Framework](https://galileo.ai/blog/ai-agent-guardrails-framework): Implementation guide for AI agent safety controls and governance frameworks
- [AI Guardrails Framework](https://galileo.ai/blog/ai-guardrails-framework): How to implement AI safety and security guardrails effectively in production
- [Scaling AI with Guardrails Architecture](https://galileo.ai/blog/scaling-ai-guardrails-architecture-patterns): Architecture patterns for AI guardrails effective at enterprise scale
- [Build vs. Buy AI Guardrails](https://galileo.ai/blog/build-vs-buy-ai-guardrails): Decision framework for building or buying AI guardrails and compliance controls
- [AI Deployment Quality Guardrails](https://galileo.ai/blog/ai-deployment-quality-guardrails): Quality guardrails ensuring reliable AI model deployment in production
- [Introducing Galileo Protect](https://galileo.ai/blog/introducing-protect-realtime-hallucination-firewall): Launch post for Galileo's real-time hallucination and threat interception firewall
- [Introducing Agent Control](https://galileo.ai/blog/announcing-agent-control): Galileo's centralized agent governance and control layer for enterprise AI
- [AI Prompt Injection Defense](https://galileo.ai/blog/ai-prompt-injection-attacks-detection-and-prevention): How to detect and prevent prompt injection attacks in AI systems
- [Preventing Excessive Agency in LLMs](https://galileo.ai/blog/prevent-excessive-agency-llms): Risks and mitigation strategies for excessive agency in LLM-powered agents

## Multi-Agent Systems

- [Why Multi-Agent Systems Fail](https://galileo.ai/blog/why-multi-agent-systems-fail): Root causes of multi-agent system failures with decision frameworks for prevention
- [Multi-Agent AI Systems Overview](https://galileo.ai/blog/multi-agent-ai-systems): How collaborative AI agents are revolutionizing business efficiency
- [Multi-Agent Coordination Strategies](https://galileo.ai/blog/multi-agent-coordination-strategies): Coordination strategies and risk management frameworks for multi-agent systems
- [Stability Strategies for Dynamic Multi-Agent Systems](https://galileo.ai/blog/stability-strategies-dynamic-multi-agents): Nine concrete strategies for ensuring stability in multi-agent AI environments
- [Challenges of Multi-Agent Systems](https://galileo.ai/blog/multi-agent-llm-systems-fail): Coordination costs, failure modes, and operational challenges in multi-agent systems
- [Multi-Agent AI Benchmarks](https://galileo.ai/blog/benchmarks-multi-agent-ai): Multi-agent AI benchmarks and practical evaluation frameworks
- [Debugging Multi-Agent AI Systems](https://galileo.ai/blog/debug-multi-agent-ai-systems): Troubleshooting guide for collaborative AI environments and coordination failures
- [Mitigating Multi-Agent Coordination Failures](https://galileo.ai/blog/multi-agent-coordination-failure-mitigation): Strategies to prevent and recover from multi-agent AI coordination failures
- [Multi-Agent AI Failure Recovery](https://galileo.ai/blog/multi-agent-ai-system-failure-recovery): Why traditional recovery approaches fail in multi-agent AI and what works instead
- [Monitoring Multi-Agent Systems Challenges](https://galileo.ai/blog/challenges-monitoring-multi-agent-systems): Eight key challenges and practical solutions for monitoring multi-agent AI systems
- [Real-Time Anomaly Detection in Multi-Agent AI](https://galileo.ai/blog/real-time-anomaly-detection-multi-agent-ai): Strategies for detecting anomalies and failures in multi-agent AI systems
- [Multi-Agent Workflow Analysis](https://galileo.ai/blog/analyze-multi-agent-workflows): Agent roles, collaboration patterns, and performance analysis in multi-agent systems
- [Success Metrics for Multi-Agent AI](https://galileo.ai/blog/success-multi-agent-ai): Metrics and strategies for measuring efficiency in multi-agent AI systems
- [Multi-Agent Collaboration and Competition](https://galileo.ai/blog/multi-agent-collaboration-competition): Collaborative versus competitive paradigms in multi-agent system design
- [Choosing Single vs. Multi-Agent Architecture](https://galileo.ai/blog/choosing-the-right-ai-agent-architecture-single-vs-multi-agent-systems): Framework for comparing single and multi-agent systems for optimal AI solutions
- [Mastering Multi-Agent Systems eBook](https://galileo.ai/mastering-multi-agent-systems): Comprehensive guide covering design, coordination, and performance in multi-agent systems
- [Compliance in Multi-Agent AI](https://galileo.ai/blog/regulatory-compliance-multi-agent-ai): Compliance requirements and solutions for deploying multi-agent AI systems

## LLM Observability & Monitoring

- [LLM Observability Tools (Debugging & Tracing)](https://galileo.ai/blog/best-llm-observability-tools-debugging-tracing): Top tools for tracing and debugging LLM applications in production
- [Top LLM Observability Tools Compared](https://galileo.ai/blog/best-llm-observability-tools-compared-for-2024): Comparison of 15 leading AI monitoring platforms
- [LLM Observability Essentials](https://galileo.ai/blog/understanding-llm-observability): Complete guide to building effective LLM observability strategies and systems
- [Effective LLM Monitoring (8 Steps)](https://galileo.ai/blog/effective-llm-monitoring): Eight-step framework to transform LLM complexity into measurable reliability
- [Production LLM Monitoring Strategies](https://galileo.ai/blog/production-llm-monitoring-strategies): Strategies for scalable, consistent LLM performance monitoring in production
- [LLM Monitoring: Real-Time vs. Batch](https://galileo.ai/blog/llm-monitoring-real-time-batch-approaches): Real-time and batch LLM monitoring techniques with trade-off analysis
- [Top Enterprise LLM Monitoring Solutions](https://galileo.ai/blog/best-llm-monitoring-solutions-enterprise): Leading enterprise LLM monitoring platforms for compliance and scalability
- [AI Agent Monitoring Stack Guide](https://galileo.ai/blog/how-to-build-ai-agent-monitoring-stack): How to build a complete AI agent monitoring stack for operational clarity
- [Top AI Observability Platforms](https://galileo.ai/blog/top-ai-observability-platforms-production-ai-applications): Eight AI observability platforms for effective production monitoring

## AI Agent Reliability & Failure Modes

- [Guide to AI Agent Failures](https://galileo.ai/blog/agent-failure-modes-guide): Comprehensive guide to understanding and preventing common AI agent failure modes
- [Debugging AI Agents](https://galileo.ai/blog/debug-ai-agents): How to discover and fix common AI agent failure modes effectively
- [Preventing AI Agent Failures](https://galileo.ai/blog/prevent-ai-agent-failure): Strategies to proactively detect, prevent, and fix AI agent failures
- [AI Agent Reliability Strategies](https://galileo.ai/blog/ai-agent-reliability-strategies): Strategies for building robust, reliable AI agent systems
- [AI Agent Reliability Checklist](https://galileo.ai/blog/production-readiness-checklist-ai-agent-reliability): Eight critical production readiness checklists for AI agent reliability
- [AI Agent Architecture Guide](https://galileo.ai/blog/ai-agent-architecture): Essential architecture patterns and scalable AI agent design strategies
- [AI Agent Lifecycle Governance](https://galileo.ai/blog/ai-agent-lifecycle-governance): Strategic AI governance framework for effective agent lifecycle management
- [AI Agent Cost Optimization with Observability](https://galileo.ai/blog/ai-agent-cost-optimization-observability): How to optimize AI agent costs using observability and monitoring
- [Human-in-the-Loop Agent Oversight](https://galileo.ai/blog/human-in-the-loop-agent-oversight): Guide to building reliable AI systems with structured human oversight
- [AI Risk Management for Agents](https://galileo.ai/blog/risk-management-ai-agents): Systematic risk management framework for autonomous AI agents
- [LLM Reliability Guide](https://galileo.ai/blog/llm-reliability): Strategies and frameworks for improving LLM reliability in production

## RAG Systems

- [Understanding RAG Evaluation Tools](https://galileo.ai/blog/rag-evaluation-tools): Galileo's framework for measuring RAG system accuracy and efficiency
- [RAGChecker Evaluation Framework](https://galileo.ai/blog/ragchecker-fine-grained-rag-evaluation-framework): Fine-grained, claim-level diagnostic framework for identifying RAG failures
- [Optimizing RAG: Techniques & Metrics](https://galileo.ai/blog/rag-evaluation-techniques-metrics-optimization): Techniques for evaluating and enhancing RAG system performance
- [RAG Architecture Guide](https://galileo.ai/blog/rag-architecture): RAG architecture benefits, components, and enterprise implementation strategies
- [RAG Performance Optimization](https://galileo.ai/blog/rag-performance-optimization): Strategies for refining Retrieval-Augmented Generation system performance
- [Mastering RAG eBook](https://galileo.ai/mastering-rag): Complete guide to building scalable, accurate RAG-based AI systems
- [Agentic RAG Integration](https://galileo.ai/blog/agentic-rag-integration-ai-architecture): AI systems with enhanced reasoning using agentic RAG architecture
- [LLM Hallucination Index: RAG Special](https://galileo.ai/blog/llm-hallucination-index-rag-special): Analysis of LLM hallucinations specifically in RAG contexts

## AI Hallucinations

- [AI Hallucination Detection Tools](https://galileo.ai/blog/best-hallucination-detection-tools-llm): Comparison of AI tools ensuring factual consistency and compliance in LLMs
- [AI Hallucination Examples & Mitigation](https://galileo.ai/blog/ai-hallucination-examples): Real-world AI hallucination examples, business impacts, and mitigation strategies
- [Deep Dive into LLM Hallucinations](https://galileo.ai/blog/deep-dive-into-llm-hallucinations-across-generative-tasks): LLM hallucinations analyzed across different generative task types
- [Why Language Models Hallucinate](https://galileo.ai/blog/why-language-models-hallucinate): Root causes of hallucinations despite training on accurate data
- [5 Techniques for Detecting LLM Hallucinations](https://galileo.ai/blog/5-techniques-for-detecting-llm-hallucinations): Five practical methods to detect and mitigate LLM hallucinations in production
- [ChainPoll Hallucination Detection](https://galileo.ai/blog/chainpoll): Galileo's proprietary ChainPoll method for detecting LLM hallucinations
- [Galileo Hallucination Index](https://galileo.ai/blog/hallucination-index): Benchmark framework for assessing leading LLM models on context adherence

## AI Governance & Compliance

- [AI Governance Framework Guide](https://galileo.ai/blog/ai-governance-framework): Seven-step framework for effective AI governance implementation
- [AI Governance in Engineering](https://galileo.ai/blog/ai-governance-organizational-change-rules-compliance): How to integrate AI compliance into engineering workflows without sacrificing velocity
- [AI Agent Compliance & Governance](https://galileo.ai/blog/ai-agent-compliance-governance-audit-trails-risk-management): Strategies for audit trails and regulatory compliance in AI deployments
- [AI Trust, Transparency & Governance](https://galileo.ai/blog/ai-trust-transparency-governance): AI transparency and governance considerations in regulated industries
- [AI Risk Management Strategies](https://galileo.ai/blog/ai-risk-management-strategies): Frameworks and guidelines for effective AI risk management
- [Model Risk Management Framework](https://galileo.ai/blog/model-risk-management-aspects-framework): Frameworks for managing AI model risks and ensuring compliance
- [Financial AI Compliance Benchmark Framework](https://galileo.ai/blog/financial-ai-compliance-benchmark-framework): Guide to benchmarking AI for regulatory compliance in banking
- [EU AI Act Preparation Guide](https://galileo.ai/blog/ready-for-regulation-preparing-for-the-eu-ai-act): EU AI Act compliance requirements and risk category classification

## Competitive Comparisons

- [Galileo vs. LangSmith](https://galileo.ai/blog/galileo-vs-langsmith): Side-by-side comparison of Galileo and LangSmith for observability and efficiency
- [Galileo vs. Arize](https://galileo.ai/blog/galileo-vs-arize): Key differences between Galileo and Arize AI observability platforms
- [Galileo vs. Braintrust](https://galileo.ai/blog/galileo-vs-braintrust): Comparison of Galileo and Braintrust AI observability platform features
- [Galileo vs. Langfuse](https://galileo.ai/blog/galileo-vs-langfuse): Comparison of Galileo and Langfuse for enterprise reliability and flexibility
- [Galileo vs. Vellum AI](https://galileo.ai/blog/galileo-vs-vellum): Comparing Galileo and Vellum for production reliability and development speed
- [Galileo vs. Weights & Biases](https://galileo.ai/blog/galileo-vs-weights-biases): Comparing AI reliability and experiment tracking capabilities
- [Galileo vs. Patronus AI](https://galileo.ai/blog/galileo-vs-patronus): Comparison of AI reliability and evaluation approaches
- [Galileo vs. Athina AI](https://galileo.ai/blog/galileo-vs-athina-ai): Comparing AI observability and production protection capabilities
- [Galileo vs. Promptfoo](https://galileo.ai/blog/galileo-vs-promptfoo): Detailed analysis of platform architectures, capabilities, and enterprise readiness
- [Top Braintrust Alternatives](https://galileo.ai/blog/best-braintrust-alternatives): Six tools for AI development and evaluation beyond Braintrust

## Agent Frameworks & Integrations

- [Comparing AI Agent Frameworks: LangGraph vs. AutoGen vs. CrewAI](https://galileo.ai/blog/mastering-agents-langgraph-vs-autogen-vs-crew): Evaluation guide for LangGraph, AutoGen, and CrewAI for building agents
- [AutoGen vs. CrewAI vs. LangGraph vs. OpenAI](https://galileo.ai/blog/autogen-vs-crewai-vs-langgraph-vs-openai-agents-framework): Differences and trade-offs among major AI agent frameworks
- [AutoGen Framework Overview](https://galileo.ai/blog/autogen-framework-multi-agents): AutoGen's multi-agent orchestration capabilities and key enterprise benefits
- [OpenAI Swarm Framework Overview](https://galileo.ai/blog/openai-swarm-framework-multi-agents): Building reliable multi-agent systems with OpenAI's Swarm framework
- [Google Agent2Agent (A2A) Protocol Guide](https://galileo.ai/blog/google-agent2agent-a2a-protocol-guide): Understanding Google's A2A protocol for seamless AI agent integration
- [LangChain vs. LangGraph vs. LangSmith](https://galileo.ai/blog/langchain-vs-langgraph-vs-langsmith): Key differences and optimal use cases for each LangChain ecosystem tool
- [Galileo Agent Evals MCP](https://galileo.ai/blog/bringing-agent-evals-into-your-ide-introducing-galileo-s-agent-evals-mcp): How to integrate AI evaluations directly into your development environment

## Case Studies & Social Proof

- [Galileo Case Studies Hub](https://galileo.ai/case-studies): Real-world AI reliability solutions with customer outcomes and metrics
- [Fintech AI Monitoring: Days to Minutes](https://galileo.ai/case-studies/galileo-helps-a-leading-fintech-solution-reduce-mean-time-to-detect-from-days-to-minutes): How Galileo reduced mean time to detect AI issues from days to minutes in fintech
- [AI Personalization at Scale (50K Companies)](https://galileo.ai/case-studies/enterprise-ai-at-startup-speeds-how-a-leading-customer-engagement-platform-reliably-made-ai-personalization-available-to-50-000-companies-in-weeks): How one platform scaled AI personalization to 50,000 companies in weeks with Galileo
- [Entertainment Tech AI Precision](https://galileo.ai/case-studies/a-leading-entertainment-tech-company-leverages-galileo-to-deliver-last-mile-conversational-ai-precision): How an entertainment tech company used Galileo for last-mile conversational AI precision
- [Fortune 50 CPG Risk Reduction](https://galileo.ai/case-studies/galileo-lowers-a-fortune-50-cpg-s-risk-associated-with-monitoring-prompts): Galileo cuts Fortune 50 CPG evaluation time and monitoring risk significantly
- [GenAI for 7.7M Customers](https://galileo.ai/case-studies/case-study-genai-for-7-7m-customers-with-galileo): Scaling academic services AI to 7.7 million customers using Galileo

## Model Hub & Model Comparisons

- [Galileo AI Model Hub](https://galileo.ai/model-hub): Comprehensive catalog of AI models with detailed performance overviews and evaluations
- [Gemini 2.5 Pro Overview](https://galileo.ai/model-hub/gemini-2-5-pro-overview): Gemini 2.5 Pro's conversation handling, capabilities, and cost efficiency analysis
- [Gemini 2.5 Flash Overview](https://galileo.ai/model-hub/gemini-2-5-flash-overview): Google's cost-effective, speed-optimized AI model features and benchmarks
- [Claude Sonnet 4 Overview](https://galileo.ai/model-hub/claude-sonnet-4-overview): Claude Sonnet 4's capabilities and impact on AI development economics
- [GPT-4.1 Mini Overview](https://galileo.ai/model-hub/gpt-4-1-mini-overview): Speed, cost, and performance analysis of GPT-4.1 Mini
- [Llama 3.3 70B Overview](https://galileo.ai/model-hub/llama-3.3-70b-overview): Performance benchmarks, costs, and deployment tips for Llama 3.3 70B
- [DeepSeek-V3 Overview](https://galileo.ai/model-hub/deepseek-v3-overview): DeepSeek-V3's efficiency and performance in complex task environments
- [DeepSeek R1 vs. OpenAI O1](https://galileo.ai/blog/deepseek-r1-vs-openai-o1-comparison): Comprehensive comparison of DeepSeek R1 and OpenAI O1 on key dimensions
- [GPT-4 vs. GPT-4o vs. GPT-4 Turbo](https://galileo.ai/blog/gpt-4-vs-gpt-4o-vs-gpt-4-turbo): Key differences among GPT-4 model variants for deployment decisions

## Company & Trust

- [About Galileo](https://galileo.ai/about): Galileo's mission, founding story, and vision for trustworthy and reliable AI
- [Galileo Trust & Security Center](https://galileo.ai/trust-security): Galileo's AI security posture, SOC 2 Type 2 compliance, and trust center resources
- [Enterprise Deployment](https://docs.galileo.ai/deployments/overview): Deployment options including cloud-hosted, on-premises, and in-VPC installations
- [Data Privacy and Compliance](https://docs.galileo.ai/deployments/data-privacy-and-compliance): SOC2 Type 2 certification, data isolation, and compliance documentation
- [Galileo Series B Announcement](https://galileo.ai/blog/announcing-our-series-b): $45M Series B funding announcement and Galileo's AI Evaluation Platform vision
- [Galileo Privacy Policy](https://galileo.ai/privacy-policy): How Galileo handles personal information and data security
- [Galileo Responsible Disclosure Program](https://galileo.ai/responsible-disclosure-program): Security disclosure guidelines and researcher recognition framework

## Optional

- [Galileo AI Blog](https://galileo.ai/blog): Central hub for all AI insights, platform updates, and thought leadership from Galileo
- [Mastering Agents eBook](https://galileo.ai/mastering-agents-ebook): Complete guide to building reliable, scalable AI agent systems
- [Mastering GenAI Series](https://galileo.ai/mastering-genai-series): Comprehensive content series covering RAG, LLM evaluation, and AI agents
- [Building Trust in Generative AI eBook](https://galileo.ai/building-trust-in-generative-ai-ebook): Essential evaluation techniques for trustworthy generative AI applications
- [Galileo and HP Partnership](https://galileo.ai/blog/hp-partner): Partnership announcement for trustworthy AI with Z by HP integration
- [Galileo Joins MongoDB MAAP](https://galileo.ai/blog/galileo-joins-mongodb-s-ai-applications-program-as-their-first-agentic-evaluation-platform): Galileo as MongoDB's first agentic evaluation platform partner
- [Galileo Joins AWS Marketplace](https://galileo.ai/blog/galileo-joins-aws-marketplace): Galileo's AI agent reliability capabilities available on AWS Marketplace
- [Galileo Optimizes with NVIDIA](https://galileo.ai/blog/galileo-optimizes-enterprise-scale-agentic-ai-stack-with-nvidia): Galileo and NVIDIA collaboration for enterprise-scale agentic AI reliability
- [Cisco AI Defense Integration](https://galileo.ai/blog/securing-the-agentic-future-cisco-ai-defense-integrates-with-agent-control): Cisco AI Defense integration with Galileo's Agent Control for security
- [Galileo and Databricks Integration](https://galileo.ai/blog/confidently-ship-ai-applications-with-databricks-and-galileo): AI application deployment through Galileo's Databricks integration
- [Galileo and Google Cloud Integration](https://galileo.ai/blog/galileo-and-google-cloud-evaluate-observe-generative-ai-apps): Using Galileo tools on Google Cloud for AI reliability and observability
- [On-Premise AI Observability](https://galileo.ai/blog/bringing-ai-observability-behind-the-firewall-deploying-on-premise-ai): On-premise AI observability deployment for regulated industries
- [AI Safety Overview](https://galileo.ai/blog/introduction-to-ai-safety): Essential AI safety metrics and implementation methods overview
- [Hidden Costs of Agentic AI](https://galileo.ai/blog/hidden-cost-of-agentic-ai): Unseen expenses and challenges hindering agentic AI project success
- [LLM Red Teaming Strategies](https://galileo.ai/blog/llm-red-teaming-strategies): Seven strategies for proactive LLM security testing and vulnerability detection
- [MLOps KPIs to Prove ROI](https://galileo.ai/blog/mlops-kpis-measure-prove-roi): Fourteen MLOps KPIs connecting technical performance gains to business ROI
- [Galileo AI Events](https://galileo.ai/events): Upcoming AI-focused webinars and in-person events from Galileo
- [Galileo AI Webinars](https://galileo.ai/webinar): Archive of AI evaluation, agent governance, and observability webinars