Evaluate and monitor generative AI systems
Who should use this?
Teams operating production LLM or agent systems and Shared experiments and release gates.
Who should avoid it?
Small projects served by local tests, Teams without labeled acceptance cases
What problem does it solve?
Galileo combines experiments, datasets, custom and built-in metrics, tracing, production monitoring, and guardrails for LLM and agent applications.
Would I recommend it?
Shortlist Galileo when evaluation is becoming a team operating system rather than a notebook. Do not buy on metric count: require stable agreement with human reviewers, useful root-cause analysis, predictable trace economics, and an approved data path.
Advisor score
Premium review framework
Galileo combines experiments, datasets, custom and built-in metrics, tracing, production monitoring, and guardrails for LLM and agent applications.
Shortlist Galileo when evaluation is becoming a team operating system rather than a notebook. Do not buy on metric count: require stable agreement with human reviewers, useful root-cause analysis, predictable trace economics, and an approved data path.
Build 200 expert-labeled cases covering correct, subtly wrong, unsafe, slow, and expensive outputs. Compare Galileo metrics with blinded human labels, perturb prompts and judge models, inspect false passes and false failures, then measure cost per release and time to diagnose a seeded production regression.
Personal Recommendation
Shortlist Galileo when evaluation is becoming a team operating system rather than a notebook. Do not buy on metric count: require stable agreement with human reviewers, useful root-cause analysis, predictable trace economics, and an approved data path.
Try the recommendation
Free 5,000-trace entry tier
Overall Score
Editorial Review Framework
Who should use this?
Teams operating production LLM or agent systems, Shared experiments and release gates, Organizations needing deployment options.
Who should avoid it?
Small projects served by local tests, Teams without labeled acceptance cases
What problem does it solve?
Galileo combines experiments, datasets, custom and built-in metrics, tracing, production monitoring, and guardrails for LLM and agent applications.
Would I recommend it?
Shortlist Galileo when evaluation is becoming a team operating system rather than a notebook. Do not buy on metric count: require stable agreement with human reviewers, useful root-cause analysis, predictable trace economics, and an approved data path.
Overall Score
8.2
Ease of Use
8.0
AI Quality
8.0
Features
8.4
Speed
8.0
Integrations
8.2
Value for Money
8.2
Customer Support
7.6
Learning Curve
7.6
Recommended Because…
Free 5,000-trace entry tier
Scores use a 0-10 editorial scale. The source data is maintained as 5-point review dimensions, then normalized for reader-friendly comparison.
Reusable trial worksheet
Turn this review’s buyer test into evidence. Your entries autosave only in this browser and are never added to shared shortlist links.
Confirm the tool meets every must-have workflow and stakeholder requirement.
Review starting point: Teams operating production LLM or agent systems; Shared experiments and release gates; Organizations needing deployment options
Run the same representative work you would use in production; do not score a polished demo.
Review starting point: Complete three to five representative tasks with known acceptable outcomes and compare them with your current process.
Calculate the effective cost per accepted result, including usage, review, corrections, and required add-ons.
Review starting point: Free includes 5,000 traces monthly and unlimited users and custom evals. Pro starts at $100 monthly when billed yearly with 50,000 traces; higher volume and Enterprise pricing are custom. Model-judge costs may be separate. Reviewed September 12, 2026.
Define an acceptance threshold, test known answers and edge cases, and record every correction.
Review starting point: Editorial quality signals: features 4.2/5; AI quality 4.0/5. Validate these signals in your own work.
Verify what data enters the product, who can access it, how long it is retained, and whether it trains models.
Review starting point: Use approved low-risk data first. Check roles, consent, deletion, subprocessors, model-training settings, and the contract—not only the marketing page.
Test the real handoffs, permissions, failure states, and export path your team depends on.
Review starting point: OpenAI, Anthropic, LangChain, LlamaIndex, Python, OpenTelemetry
Record training, governance, reliability, accessibility, ownership, and change-management risks before rollout.
Review starting point: Judge calibration remains buyer work; Trace volume can scale quickly; Enterprise controls are quote-based
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Evaluation
Research-based
Price posture
From $0/month
Reviewed
2026-09-12
No authentic product screenshot is published for this review. DiscoverAI does not use generated interface images as product evidence.
Free includes 5,000 traces monthly and unlimited users and custom evals. Pro starts at $100 monthly when billed yearly with 50,000 traces; higher volume and Enterprise pricing are custom. Model-judge costs may be separate. Reviewed September 12, 2026.
Free plan: Yes. The Free plan includes 5,000 traces per month.
Editorial freshness
Pricing and material product claims were checked September 12, 2026.
Community evidence
Structured, editor-moderated experience—not star ratings. This complements our independent review and never changes its score.
No approved community evidence yet. Be the first verified user to contribute.
Galileo combines experiments, datasets, custom and built-in metrics, tracing, production monitoring, and guardrails for LLM and agent applications.
Free includes 5,000 traces monthly and unlimited users and custom evals. Pro starts at $100 monthly when billed yearly with 50,000 traces; higher volume and Enterprise pricing are custom. Model-judge costs may be separate. Reviewed September 12, 2026.
Teams operating production LLM or agent systems, Shared experiments and release gates, Organizations needing deployment options.
Build 200 expert-labeled cases covering correct, subtly wrong, unsafe, slow, and expensive outputs. Compare Galileo metrics with blinded human labels, perturb prompts and judge models, inspect false passes and false failures, then measure cost per release and time to diagnose a seeded production regression.
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