Galileo
AI observability and eval engineering platform that turns offline evals into production guardrails.
Galileo's eval-to-guardrail loop is the real differentiator—no other platform turns offline evals into production guardrails this directly. The Luna-2 models cut eval costs by 96%, making 100% traffic monitoring feasible. If you're shipping agentic AI at scale, this is a serious contender; for simple monitoring, cheaper options suffice.
Verified 4d ago · liveness 78/100 · cite: rightaichoice.com/tools/galileo
- AI agent teams needing production-grade guardrails and eval-driven control
- Enterprise RAG deployments requiring low-cost, high-accuracy evals at scale
- Security-conscious teams adopting OWASP agent threat frameworks
- Developers embedding eval workflows into Claude and Codex coding assistants
- Hobby projects or early-stage startups on a tight budget—full value is enterprise-tier
- Teams that only need basic tracing/monitoring without an eval-guardrail loop
- Users requiring a fully open-source solution—Galileo is proprietary
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Skip Galileo if you only need basic monitoring or tracing and don't require the eval-to-guardrail loop; the full value is gated behind the Enterprise tier.
The Pro plan at $100/month covers only 50,000 traces per month; going beyond that requires scaling to Enterprise pricing, which is custom and more expensive.
Galileo's pricing fits enterprise teams that need production-grade guardrails and are willing to invest. The Free and Pro tiers ($0-$100/month) are entry points for developers, but the real value is in the Enterprise tier, which is custom-priced. Compared to rivals like Arize Phoenix (open-source, free) and LangSmith (has a free tier), Galileo's unique eval-to-guardrail feature justifies the price for serious AI teams.
In short
Galileo — AI observability and eval engineering platform that turns offline evals into production guardrails. Best for AI agent teams needing production-grade guardrails and eval-driven control, Enterprise RAG deployments requiring low-cost, high-accuracy evals at scale, Security-conscious teams adopting OWASP agent threat frameworks. Free to start; paid plans from $100/mo.
What's new in Galileo
Checked 4 days agoAcross the latest 4 updates: 4 feature updates.
The 2026 Caching Playbook for Agents: Bigger Prompts, Smaller Bills.
Galileo's guide to caching strategies for AI agents, focusing on reducing costs while handling larger prompts.
Evals You Can Trust Without the Bill: How We Built Luna Studio
Galileo introduces Luna Studio, a suite for building and managing evaluation suites with a focus on cost efficiency.
Introducing Eval Engineer: Bringing Eval Expertise to Claude and Codex
Galileo launches Eval Engineer, a feature that brings evaluation expertise into AI coding assistants like Claude and Codex.
Your Evals Are Wrong 20% of the Time. Now They Improve Every Time You Look.
Galileo announces that its evaluation platform now uses feedback loops to improve accuracy each time evaluations are reviewed.
Viability Score
How well maintained and how widely used is Galileo? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- 20+ out-of-box evals for RAG, agents, safety, and security
- Custom evaluators to encode domain expertise
- Auto-tune metrics from live feedback to hit 70%+ F1 scores
- Distill evals into Luna-2 small language models
- Run low-latency guardrails on 100% of traffic with L4 GPU support
- Guardrail policies to block harmful responses and control agent actions
- Insights Engine analyzes agent behavior to surface failure modes
- OWASP-based security evals (ASI01, ASI02)
- Luna Studio for cost-efficient eval suite management
- Eval Engineer integrates eval expertise into Claude and Codex workflows
- GCache for agent caching to reduce costs with larger prompts
- Feedback loops that improve eval accuracy every time you review
- End-to-end visibility into agent completions
- Deploy on SaaS, VPC, or on-premises
- Integrates with NVIDIA NeMo and NIM, CrewAI, MongoDB, and HP AI Studio
About Galileo
Galileo is an AI observability and eval engineering platform that unifies offline testing and production governance. Instead of separating pre-production evals from online safety, Galileo distills optimized evals into compact Luna-2 small language models that run low-latency, low-cost guardrails on 100% of traffic—at 96% lower cost than LLM-as-judge approaches. This eval-to-guardrail lifecycle means that eval scores automatically control agent actions, tool access, and escalation paths, with no glue code required. Built for teams shipping production-grade AI agents, RAG systems, and LLM applications, Galileo starts with 20+ out-of-box evals for RAG, agents, safety, and security, plus custom evaluators. The Insights Engine analyzes millions of signals—models, prompts, functions, context, datasets, traces—to surface failure modes and prescribe fixes, cutting debugging time from days to minutes. Recent additions extend the platform's reach: Luna Studio for cost-efficient eval suite management, Eval Engineer for Claude and Codex workflows, and GCache for agent caching. Security is addressed with OWASP-based evaluations (ASI01, ASI02) and integration with NVIDIA NeMo and NIM, plus partnerships with CrewAI, MongoDB, and HP AI Studio. Deployment flexibility is strong: SaaS, VPC, or on-premises, accommodating strict compliance needs. Trusted by enterprises like Writer, Cisco, NVIDIA, and HP, Galileo is designed for teams that need active protection, not just dashboards. Compared to alternatives that offer only monitoring or separate offline eval tools, Galileo's unique edge is closing the loop: making pre-production evals become production governance. That said, the full value—especially real-time guardrails and low-latency inference—is gated behind the Enterprise tier, making it a serious investment for serious teams.
Behind the Verdict
Galileo is a platform that closes the loop between evaluation and production guardrails. This is a unique approach: rather than treating evals as a separate step, it lets you distill your evals into Luna-2 models that run on your traffic, actively blocking bad behavior. The cost savings are real: 96% lower cost than LLM-as-judge, which makes it practical to evaluate 100% of your traffic. Strengths: The Insights Engine is impressive, analyzing millions of signals to surface failure modes and prescribe fixes, cutting debugging time dramatically. The out-of-box evals cover RAG, agents, safety, and security, and you can build custom evals. The auto-tune feature that improves eval accuracy from feedback is a nice touch. Security is a focus, with OWASP ASI01/ASI02 evals and NVIDIA NeMo integration. Weaknesses: The full value is gated behind the Enterprise tier. The Free and Pro tiers don't include real-time guardrails or low-latency inference, so if you're a smaller team, you might not get the core benefit without paying enterprise prices. The Pro tier includes support for up to 50k traces, but you'll need to scale up for larger volumes. Also, deployment options like VPC and on-prem are Enterprise-only. Where it fits: Enterprise teams shipping AI agents that need active protection, RAG systems, and security-conscious organizations. Where it doesn't: hobby projects, simple chatbots, or teams that just need basic monitoring.
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Real-world workflow fit
Concrete scenarios for the personas Galileo actually fits — and what changes day-one when you adopt it.
You're shipping a customer support agent and need to ensure it doesn't hallucinate. You start with Galileo's free tier, create custom evals for accuracy and safety, and run them on your test data. After iterating, you distill your best evals into a Luna model to run against production traffic.
Outcome: You catch hallucinations early and deploy guardrails that block bad responses, reducing risk and improving customer trust.
Your team is building a RAG system and needs to evaluate retrieval quality. With Galileo, you use the 20+ out-of-box RAG evals to measure context precision and answer relevance. The Insights Engine identifies specific failure modes, and you use auto-tune to improve your pipeline.
Outcome: You achieve >90% F1 on your RAG metrics, and the insights help you fix issues in days instead of weeks.
You need to ensure your AI agents are secure. You use Galileo's OWASP-based evals (ASI01, ASI02) to test for goal hijacking and tool misuse. With GCache, you reduce costs while keeping the system responsive.
Outcome: You identify and mitigate security vulnerabilities before they become incidents, and you have a dashboard for compliance.
Use Cases
- Monitor and debug LLM agent behaviors in production to catch hallucinations and tool misuse.
- Auto-tune custom evaluators from live feedback to achieve >90% F1 scores on domain-specific metrics.
- Distill expensive LLM-as-judge evaluators into Luna models for real-time guardrailing at 97% lower cost.
- Enforce guardrail policies that block harmful responses and control agent actions without glue code.
- Accelerate deployment cycles by integrating offline evals with CI/CD pipelines and shipping with confidence.
- Evaluate agent security against OWASP ASI01 and ASI02 vulnerabilities.
Models Under the Hood
as of 2026-08-31
Limitations
- Galileo is an AI observability and evaluation platform that requires integration with external LLMs for generation, focusing on evaluation and guardrails.
- The free tier is limited to 5,000 traces per month, and Pro scales with traces.
- Enterprise plans offer unlimited traces, custom rate limits, and deployment options including hosted, VPC, or on-prem.
as of 2026-08-30
Verification history
We have re-verified Galileo 16 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
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- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
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12-month cost
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.
Plans compared
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
Developers and small teams who want to experiment, iterate, and build without cost commitment, exploring Galileo's evals and observability features.
What this tier adds
This is the starting tier, offering 5,000 traces per month, unlimited users, and unlimited custom evals, but no analytics or dedicated support.
Pro
$100/month
Ideal for
Growing teams ready to launch an app with confidence, needing more traces and advanced analytics, but not yet requiring enterprise security or guardrails.
What this tier adds
Adds 50,000 traces per month, standard RBAC, advanced analytics & insights, and dedicated Slack support compared to Free.
Enterprise
Contact us
Ideal for
Large enterprises shipping AI agents at scale that need unlimited traces, real-time guardrails, and strict security and compliance (VPC/on-prem).
What this tier adds
Unlocks everything: unlimited traces, custom rate limits, hosted/VPC/on-prem deployment, enterprise-grade security (RBAC, SSO), dedicated CSM, real-time guardrails, and 24/7 support.
Where the pricing makes sense
The company stage and team size where Galileo's pricing actually pencils out — and where peers do it cheaper.
Galileo's pricing fits enterprise teams that need production-grade guardrails and are willing to invest. The Free and Pro tiers ($0-$100/month) are entry points for developers, but the real value is in the Enterprise tier, which is custom-priced. Compared to rivals like Arize Phoenix (open-source, free) and LangSmith (has a free tier), Galileo's unique eval-to-guardrail feature justifies the price for serious AI teams.
Setup time & first value
How long it actually takes to get something useful out of Galileo — broken out by persona, not the marketing-page minute.
For a developer on the Free plan: you can set up an account, create your first eval, and see results within a few hours. For a team planning production guardrails: expect a few days to integrate with your tech stack, create custom evals, and deploy Luna models. Enterprise setups with VPC/on-prem may take weeks.
Switching to or from Galileo
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Arize Phoenix or LangSmith: you can export your traces and evaluation results, then import them into Galileo via its API or dashboard to start your eval-to-guardrail journey.
- →From custom LLM-as-judge scripts: you can replace them with Galileo's out-of-box evals and use Luna to reduce costs.
- ↗To Arize Phoenix or LangSmith: if you need a more open-source or lighter-weight solution, you can export your traces and eval results via the API.
Integrations
Resources & Guides
- Resourcegalileo.ai
How to Build a Reliable Stripe AI Agent with LangChain, OpenAI, and Galileo
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
- Resourcegalileo.ai
Bringing AI Observability Behind the Firewall: Deploying On-Premise AI
AI observability is no longer optional. Learn why enterprises deploying agents at scale need on-prem evaluation infrastructure to ensure visibility, control, and compliance.
- Resourcegalileo.ai
Architectures for Multi-Agent Systems
Choosing the right design is critical for success
Tutorials & Learning
Official links
Tools that pair well with Galileo
Common stack mates teams adopt alongside Galileo, with the specific reason each pairing earns its keep.
Alternatives to Galileo
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Galileo AI Evals
AI observability and eval engineering platform that turns offline evals into production guardrails.
Arize Phoenix
Open-source LLM agent observability with tracing, evals, and experiments
Frequently Asked Questions
Best-of guides
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