Galileo

Galileo is an AI evaluation and observability platform for teams shipping LLM apps, RAG systems, and agents into production. The fetched pricing page describes “AI observability and evaluations to help you ship faster,” and the plan details are specific: Free is $0/month with 5,000 traces per month, unlimited users, and unlimited custom evals. Pro is $100/month when billed yearly, includes 50,000 traces per month, standard RBAC, advanced analytics and insights, and dedicated Slack support; the page notes that pricing scales based on trace volume. Enterprise is contact-sales and adds unlimited traces, custom rate limits, hosted/VPC/on-prem deployment, enterprise security, RBAC, SSO, a dedicated CSM, real-time guardrails, 24/7 support, dedicated inference servers, and forward-deployed engineering support.

Galileo is different from general analytics tools because it is built around AI reliability problems: hallucination detection, retrieval quality, prompt and model comparisons, production monitoring, and guardrails. A normal logging dashboard can tell you that an endpoint was slow. Galileo is meant to help answer whether the model retrieved the right evidence, followed instructions, passed an eval, or drifted after a prompt change.

The strongest use cases are enterprise LLM monitoring, RAG evaluation, AI governance, regression testing before releases, and executive confidence around production AI behavior. The Free tier is generous enough for developers and small teams to experiment with traces and custom evals, while Pro gives a clearer path for production teams that need Slack support and more trace volume. Enterprise features matter for regulated deployments where VPC/on-prem options, SSO, and dedicated support are not optional.

The cons are mostly about fit and maturity. Galileo is not the first tool a team needs if it has not shipped an AI workflow yet. You need developers to instrument traces, define evals, and decide what quality metrics matter. Pricing can also scale with trace volume, so busy apps should model costs before sending every interaction. Native MCP support was not verified from the fetched pages. Compare Galileo with /tools/langfuse if open source matters, /tools/braintrust for eval-heavy product workflows, /tools/promptfoo for code-centric testing, and /tools/arize-phoenix for open-source observability.

A strong Galileo implementation usually has three layers: offline evals for prompt and model changes, online observability for real user traffic, and guardrails for high-risk failures. Teams should label examples of good and bad behavior, track retrieval quality separately from generation quality, and review regressions before deploying new prompts. That work takes discipline, but it gives engineering, product, and compliance teams a shared language for AI reliability instead of arguing from isolated screenshots.