Galileo
Contact SalesAI Observability and Evaluation Platform for Reliable AI
By Tanmay Verma, Founder · Last verified 30 May 2026
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Added 7d ago
80/100Safe BetAI Observability and Evaluation Platform for Reliable AI
By Tanmay Verma, Founder · Last verified 30 May 2026
Affiliate disclosure: We earn a commission when you use our links. Editorial picks are independent. How we choose.
Galileo stands out for turning evals into production guardrails, a unique lifecycle that saves teams from maintaining separate offline and online systems. The Luna model distillation for low-cost, low-latency monitoring is a game-changer, but smaller teams may find it complex.
Last verified: May 2026
Pick Galileo if you are an AI team deploying LLM agents or RAG systems at scale and need to move beyond basic monitoring to proactive guardrails that prevent failures. The platform excels in high-stakes environments where accuracy and reliability are critical, such as enterprise customer-facing agents or regulated industries. Its ability to distill LLM judges into Luna models for cost-effective production monitoring is a standout feature, potentially saving 97% on eval costs. However, pass if you need a simple monitoring dashboard or are early-stage with minimal traffic, as Galileo’s depth may be overkill. The closest alternative is probably Arize AI, but Galileo's eval-to-guardrail lifecycle and Luna distillation give it an edge for teams wanting a unified offline-to-online workflow. Real-world usage caveat: it requires investment in setting up groundtruth and custom evals to fully benefit, and the pricing (not publicly listed) may be enterprise-focused.
Skip Galileo if Skip Galileo if you need a no-code AI playground with high free-tier trace limits or if you only use a single model and don't require observability across models.
Galileo published a caching playbook for AI agents to reduce prompt costs.
Galileo introduced Luna Studio, a tool for trustworthy evaluations without high costs.
How likely is Galileo to still be operational in 12 months? Based on 6 signals including funding, development activity, and platform risk.
Galileo is an AI observability and evaluation engineering platform that enables teams to move from offline evals to production guardrails. It helps developers and enterprises solve the AI measurement problem by providing 20+ out-of-box evals for RAG, agents, safety, and security, plus custom evaluators. The platform captures groundtruth from synthetic, dev, and live production data, and auto-tunes metrics from live feedback to create accurate evals with high F1 scores. A key differentiator is the ability to distill expensive LLM-as-judge evaluators into compact Luna models that run at low latency and 97% lower cost, monitoring 100% of production traffic. Galileo's insights engine analyzes agent behavior to detect failure modes and prescribe fixes, and its eval-to-guardrail lifecycle allows pre-production evals to become production governance, controlling agent actions and tool access without glue code. Trusted by enterprises like Writer, Cisco, and NVIDIA, Galileo is designed for teams that need to ship AI with confidence.
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Concrete scenarios for the personas Galileo actually fits — and what changes day-one when you adopt it.
A developer deploys a customer support agent using LangChain and OpenAI. They use Galileo to capture traces in production, identify a hallucination issue causing incorrect tool inputs, and auto-tune a custom evaluator to detect similar failures.
Outcome: The engineer implements a guardrail policy that blocks the hallucination in real-time, reducing error rates by 80% within the first day.
A team uses Galileo's Insights engine to analyze agent behavior across 10,000 traces. The engine detects that the agent frequently mis-selects tools due to ambiguous prompts.
Outcome: The team refines prompt templates and adds few-shot examples, improving tool selection accuracy from 67% to 95%.
An enterprise deploys an AI agent for financial services. They use Galileo's real-time guardrails to enforce security policies and prevent the agent from accessing unauthorized data.
Outcome: The guardrail policies block 100% of policy violations without adding latency, satisfying compliance requirements.
The free tier caps at 5,000 traces per month, which may be insufficient for production workloads. Pro pricing scales with trace volume, potentially increasing costs for high-traffic systems. On-prem and VPC deployments are limited to Enterprise tier.
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
For each published Galileo tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/month
Ideal for
Solo developers or small teams experimenting with AI observability and evaluation.
What this tier adds
Entry-level tier with 5,000 traces per month and unlimited users.
Pro
$100/month (billed yearly, save 33%)
Ideal for
Growing teams launching production AI applications that need more traces and advanced analytics.
What this tier adds
Adds 50,000 traces per month, RBAC, advanced analytics, and dedicated Slack support.
Enterprise
Contact us
Ideal for
Large organizations needing unlimited traces, security controls, and custom deployment options.
What this tier adds
Unlimited traces, on-prem/VPC deployment, SSO, dedicated CSM, and real-time guardrails.
The company stage and team size where Galileo's pricing actually pencils out — and where peers do it cheaper.
Galileo's free tier is generous for small teams experimenting, but the Pro tier at $100/month (billed yearly) for 50,000 traces may be costlier than alternatives like LangSmith for similar trace volumes. Enterprise pricing is custom. The Luna model distillation can reduce overall evaluation costs by 97% compared to LLM-as-judge, making it attractive for high-volume production use.
How long it actually takes to get something useful out of Galileo — broken out by persona, not the marketing-page minute.
For an AI engineer: basic setup (SDK integration, trace ingestion) can be done in under 30 minutes. Auto-tuning evaluators may take a few hours to gather enough traces. Enterprise on-prem deployment may take several days to configure.
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
Full product docs from galileo.ai
Learn to create a production-ready Stripe AI Agent using LangChain, OpenAI, and the Stripe Agent Toolkit—fully instrumented with Galileo for agent reliability. Monitor every tool call, trace LLM reasoning, and catch failures in real-time. From CLI to web interface, build with con
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Galileo launched Eval Engineer, integrating evaluation expertise into Claude and Codex.
Last calculated: May 2026
Durable execution platform for crash-safe AI agents and workflows.
