Galileo AI vs Respan: Observability, Prompts & Evals Comparison
Compare Galileo AI and Respan side by side. Both are tools in the Observability, Prompts & Evals category.
Updated February 28, 2026
The short answer
Choose Galileo AI if generous free tier with 5,000 traces/month including Agent Reliability Platform.
Choose Respan if unified observability across all LLM providers in one dashboard.
Want to compare Galileo AI and Respan on your own traffic?
Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 250+ models through one gateway. Free tier covers 10K traces per month. Setup in 5 minutes, no credit card.
Quick Comparison
| Category | Observability, Prompts & Evals | Observability, Prompts & Evals |
| Pricing | Freemium | — |
| Best For | AI teams who need to measure and improve the quality of their LLM outputs | — |
| Website | rungalileo.io | respan.ai |
| Key Features |
| — |
| Use Cases |
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Pros & Cons: Galileo AI vs Respan
- +Generous free tier with 5,000 traces/month including Agent Reliability Platform
- +Recognized as Gartner Cool Vendor demonstrating industry validation
- +Research-backed metrics providing scientific rigor to evaluations
- +Real-time observability with automated drift detection and alerting
- +Established team of 142 employees providing solid support capacity
- −Limited public information about advanced features and capabilities
- −Pro plan pricing jumps from free to $100/month with potential gaps for small teams
- −Less community-driven compared to open-source alternatives like Langfuse
- +Unified observability across all LLM providers in one dashboard
- +Real-time alerting for cost spikes, latency, and quality issues
- +Built-in evaluation tools for automated quality scoring
- +Prompt management with versioning and A/B testing
- +Easy integration with popular AI frameworks
- −Newer platform building out integrations with more frameworks
- −Advanced evaluation features require configuration effort
- −Smaller community compared to established observability tools
Pricing: Galileo AI vs Respan
- · 10,000 traces/month
- · 7-day retention
- · Basic dashboards
- · Community support
- · 100,000 traces/month
- · 30-day retention
- · Advanced analytics
- · Priority support
- · Unlimited traces
- · Custom retention
- · SSO and RBAC
- · Dedicated support
When to Choose Galileo AI vs Respan
- Monitoring LLM output quality
- Detecting and preventing hallucinations
- Evaluating RAG pipeline accuracy
About Galileo AI
Galileo is an AI observability and evaluation platform designed to provide AI reliability for teams across the entire development lifecycle. The platform offers real-time observability that continuously evaluates systems in production, sending alerts if something goes wrong or if interactions drift from training data. Galileo provides powerful, research-backed metrics and evaluation-powered development workflows to help teams build, scale, monitor, and protect AI applications in real-time. The platform is recognized as a Gartner Cool Vendor and serves as a comprehensive solution for AI teams looking to ensure reliability and performance of their LLM applications. With the Agent Reliability Platform available as part of their free tier, Galileo makes advanced AI observability accessible to teams of all sizes. The platform emphasizes scalability, security, and premium support for enterprise customers while maintaining an approachable entry point through their generous free tier.
About Respan
Respan Observability provides comprehensive LLM monitoring and debugging for AI applications in production. The platform tracks every prompt, completion, latency metric, cost, and quality signal across all LLM providers from a single dashboard, giving engineering teams full visibility into their AI stack.
The observability suite includes real-time tracing of LLM calls with detailed breakdowns of token usage, response times, and error rates. Teams can set up alerts for cost spikes, latency degradation, or quality drops, and drill into individual traces to debug issues. Built-in evaluation tools enable automated quality scoring of LLM outputs using custom rubrics or reference-based evaluation.
Prompt management features allow teams to version, test, and deploy prompts without code changes. A/B testing capabilities enable comparing model performance across different configurations, and semantic caching identifies repeated queries to reduce costs. The platform integrates with popular frameworks like LangChain, LlamaIndex, and the Vercel AI SDK.
What is Observability, Prompts & Evals?
Tools for monitoring LLM applications in production, managing and versioning prompts, and evaluating model outputs. Includes tracing, logging, cost tracking, prompt engineering platforms, automated evaluation frameworks, and human annotation workflows.
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