What Galileo does
Galileo is an AI reliability platform for traces, datasets, experiments, custom and Luna evaluators, analytics, monitoring, drift alerts, guardrails, and human feedback.
Galileo brings evaluation, trace debugging, production monitoring, and protection into one reliability workflow. A team can establish a golden dataset, compare prompt or model versions, locate a failing retrieval step, deploy selected metrics to production, watch for drift, and route incidents back into tests. Compact Luna evaluators aim to reduce the expense and latency of judging at scale. Efficiency does not make a metric ground truth: a smaller evaluator can inherit benchmark limitations, miss domain nuance, or behave differently after the application population shifts, and a guardrail can block legitimate users while still missing novel attacks.
Free is $0 with 5,000 traces monthly and unlimited users and custom evals. Pro starts at $100 per month when billed yearly, includes 50,000 traces, standard RBAC, analytics, and support, and explicitly scales with trace volume. Enterprise pricing is custom with deployment choices, scale, SSO, advanced security, guardrails, dedicated inference, and support. Model-provider usage, custom hosting, data transfer, retention, and professional services may add cost. Verify what constitutes a trace, included and excess volume, evaluator calls, guardrail requests, annual commitment, regions, retention, taxes, cancellation, and current deployment terms in the quote.
Evaluation telemetry can contain complete conversations, retrieved proprietary sources, tool payloads, user feedback, identifiers, and adversarial material. Minimize attributes, redact before transport, isolate tenants and environments, restrict roles, define retention and deletion, and never log keys or unnecessary regulated data. Golden sets become stale as products, policies, users, attacks, languages, and models change. Validate Luna and other judges against blinded experts for the actual domain; measure false positives, false negatives, subgroup performance, and uncertainty; version thresholds and models; protect evaluators from prompt injection; monitor missing traces and distribution drift; provide escalation and override; and keep qualified humans responsible for consequential releases and interventions.
How Galileo works
Teams integrate Galileo by SDK, supported framework, API, or playground and organize prompts, traces, datasets, runs, metrics, and feedback. Offline evaluation executes a controlled test set against an application version and scores results with built-in Luna models, other model judges, deterministic checks, or custom metrics. Trace views expose steps, cost, latency, retrieval, and failure context. Production observability continuously evaluates selected traffic, aggregates analytics, detects configured changes, and triggers alerts; enterprise protection can apply low-latency guardrails. Human and subject-matter feedback can refine criteria. Every metric and guardrail has coverage, threshold, calibration, latency, and false-positive tradeoffs.
Define reliability, privacy, and action criteria
Map user tasks, failure severity, release and guardrail actions, necessary telemetry, consent, redaction, tenant and regional boundaries, retention, deletion, escalation, and accountable reviewers.
Instrument representative AI workflows
SDK, framework, API, or playground integration records model, retrieval, agent, tool, latency, cost, and error context. Pilot with safe data and verify streaming, retries, failures, missing spans, and secrets.
Test Luna, custom metrics, and guardrails
Run a living golden set through deterministic, compact-model, judge, and human evaluations. Measure bias, uncertainty, injection, threshold sensitivity, false blocks, misses, latency, and domain-expert agreement.
Release gradually and watch for drift
Require human approval, sample lawful production traffic, alert on quality, distribution, and telemetry changes, inspect cases behind aggregates, preserve override and escalation, and refresh datasets and thresholds.
How to set up Galileo
Define reliability and harm criteria
Map user tasks, failure severity, release gates, runtime actions, telemetry minimization, consent, redaction, tenant isolation, regions, retention, deletion, and accountable reviewers.
Instrument a safe pilot
Use synthetic and authorized data, separate environments, scoped keys, and representative RAG, agent, tool, streaming, retry, and error flows; audit trace completeness and cost.
Build a living golden set
Include normal, rare, adversarial, multilingual, accessibility, subgroup, policy, retrieval, and tool-use cases with provenance, versioned expectations, and documented exclusions.
Calibrate metrics and guardrails
Compare Luna, judge, deterministic, and custom scores with blinded experts; test injection, order effects, thresholds, latency, false blocks, misses, and escalation behavior.
Release with drift supervision
Require human approval, roll out sampling and protection gradually, alert on quality and telemetry changes, inspect cases behind aggregates, track spend, and refresh data and thresholds.
Galileo FAQs
How much does Galileo cost?
Free includes 5,000 traces monthly. Pro starts at $100 monthly billed yearly with 50,000 and scales by trace volume; Enterprise is custom.
What are Galileo Luna evaluators?
They are compact evaluation models intended to score generative AI behavior with lower cost and latency. Validate each metric against domain experts and current traffic.
Can Galileo detect drift?
It can monitor configured production metrics and distribution changes, but usefulness depends on instrumentation, sampling, baselines, evaluator stability, and alert thresholds.
Do runtime guardrails guarantee safe output?
No. Guardrails have false positives, false negatives, latency, scope, and novel-attack limits. Layer controls, test adversarially, monitor, and provide human escalation.
How often should golden datasets be updated?
Review them after product, policy, model, prompt, retrieval, user, language, or threat changes and whenever production failures expose missing or stale cases.
Listing reviewed 2026-07-15. Product details and pricing can change; verify important terms on the provider's website.
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