Galileo AI Evals
AI observability and eval engineering platform that turns offline evals into production guardrails.
Galileo delivers where it counts: cutting evaluation costs by 96% through Luna models while maintaining accuracy. The insights engine and guardrail lifecycle are uniquely practical for agent-heavy enterprises. It's overkill for basic monitoring, but essential for teams serious about production AI reliability.
Verified 5d ago · liveness 78/100 · cite: rightaichoice.com/tools/galileo-ai-evals
- Enterprise teams deploying AI agents at scale needing production guardrails
- Developers debugging agent failures with actionable insights
- Teams wanting to cut evaluation costs with Luna models
- Organizations requiring compliance with custom eval-to-guardrail lifecycle
- Small teams needing basic LLM monitoring without sophisticated eval engineering
- Projects where setup cost outweighs evaluation depth
- Teams averse to vendor lock-in for observability
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Skip Galileo if you only need basic LLM latency or token tracking, or if your team lacks the bandwidth to build and tune custom evals—the platform's depth is overkill for simple monitoring.
Going past 50,000 traces per month on the Pro plan adds usage-based fees, which can climb quickly for high-volume production traffic.
Galileo's freemium model suits teams experimenting with AI evals; the Pro tier at 50K traces is competitive with LangSmith's similar volume, but Luna models can cut costs 96% versus LLM judges, making it more cost-effective for high-throughput production.
In short
Galileo AI Evals — AI observability and eval engineering platform that turns offline evals into production guardrails. Best for Enterprise teams deploying AI agents at scale needing production guardrails, Developers debugging agent failures with actionable insights, Teams wanting to cut evaluation costs with Luna models. Free to start; paid plans from $100/mo.
What's new in Galileo AI Evals
Checked 5 days agoAcross the latest 4 updates: 4 feature updates.
Evals You Can Trust Without the Bill: How We Built Luna Studio
Launched Luna Studio for trustworthy evaluations at low cost, reducing reliance on expensive LLM judges.
Introducing Eval Engineer: Bringing Eval Expertise to Claude and Codex
New Eval Engineer tool integrates evaluation expertise directly into Claude and Codex environments.
Your Evals Are Wrong 20% of the Time. Now They Improve Every Time You Look.
Introduced evaluation improvement mechanism that learns from manual reviews to auto-tune eval accuracy.
GCache: Caching Without the Chaos
Launched GCache, a structured caching system for AI agents to reduce unpredictability and improve reliability.
Viability Score
How well maintained and how widely used is Galileo AI Evals? 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 evals from live feedback
- Distill evals into Luna models (96% cost reduction)
- Luna Studio for low-cost, trustworthy evaluations
- Eval Engineer integration with Claude and Codex
- GCache structured caching for AI agents
- Insights engine to identify failure modes and prescribe fixes
- Capture ground truth from synthetic, dev, and production data
- Subject matter expert annotations
- Guardrail policies to block harmful responses
- Eval scores control agent actions, tool access, escalation paths
- Low-latency evaluation on L4 GPUs
- Ingest models, prompts, functions, context, datasets, traces, MCP servers
- Pre-production evals become production guardrails without glue code
About Galileo AI Evals
Galileo is an AI observability and evaluation platform for enterprises shipping AI agents at scale. It bridges pre-production testing and live monitoring: you capture ground truth from synthetic, dev, and production data, then auto-tune evaluation metrics from real feedback so evals stay accurate to your environment. Out of the box you get 20+ evals covering RAG, agents, safety, and security, plus tools to build custom evaluators encoding your team's expertise. The insights engine analyzes millions of signals—models, prompts, functions, context, datasets, traces, and MCP servers—to surface failure modes and prescribe fixes, like adding few-shot examples to stop hallucination-driven tool errors. Galileo's standout is the eval-to-guardrail lifecycle: you distill optimized evals into compact Luna models that run low-latency on L4 GPUs and monitor 100% of traffic at 96% lower cost than LLM-as-judge. Eval scores automatically control agent actions, tool access, and escalation paths. Recent launches add Luna Studio for low-cost trustworthy evaluations, Eval Engineer for Claude and Codex, and GCache for structured caching. Deployment options include SaaS, VPC, or on-premises, with enterprise-grade security and support. Pricing starts at a free tier with 5,000 traces per month, scales to Pro at $100/month for 50,000 traces, and offers custom enterprise plans. Compared to alternatives like LangSmith or Weights & Biases, Galileo focuses on the full eval-to-guardrail lifecycle, making it a sharper fit for teams that need continuous, low-latency guardrails in production.
Behind the Verdict
Galileo is built for teams that have moved past the honeymoon phase of AI and are now wrestling with production reliability. The core pitch—turn offline evals into always-on guardrails—is a genuine differentiator. Most observability tools stop at dashboards and alerts; Galileo goes further, letting eval scores directly control agent behavior. That's powerful for regulated industries or any deployment where a silent failure is unacceptable. Where Galileo really shines is the cost story. Distilling expensive LLM-as-judge evaluators into compact Luna models that monitor 100% of traffic at 96% lower cost is a concrete, measurable win. The recent Luna Studio launch (May 2026) doubles down on this, making low-cost trustworthy evaluations more accessible. The Eval Engineer integration with Claude and Codex (May 2026) is also notable—it brings eval expertise into the coding environments where agents are actually built. Strengths: deep eval coverage (RAG, agents, safety, security), auto-tuning from live feedback, actionable insights with prescriptive fixes, flexible deployment (SaaS/VPC/on-prem), and a free tier that's genuinely useful. Weaknesses: the platform's sophistication means a real learning curve. It's not a set-and-forget monitoring tool; you need to invest in building and tuning evals. The Pro tier caps at 50,000 traces/month, which may feel tight for high-volume production. Enterprise features like VPC/on-prem and advanced RBAC are locked behind custom pricing, so mid-size teams might feel the squeeze. Also, eval-to-guardrail is a strong workflow, but teams with simple monitoring needs will find it over-engineered. Where it fits: enterprises deploying AI agents at scale, especially those with compliance requirements or high stakes for failures. Data science teams building custom evals from synthetic and production data will get the most value. Where it doesn't: small teams needing basic latency/token tracking. If you just want to see if your LLM is slow or costing too much, Galileo's depth is wasted. Teams already locked into a specific observability stack might find the physics of migration uncomfortable. Overall, Galileo is a serious tool for a serious problem. If production AI reliability is your top concern, it's worth a deep look. If you're just starting out with LLMs, the free tier is a great sandbox, but don't expect to need (or use) the full power immediately.
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Real-world workflow fit
Concrete scenarios for the personas Galileo AI Evals actually fits — and what changes day-one when you adopt it.
Build a custom evaluator for tool-selection accuracy, capture production traces, auto-tune from feedback, distill to a Luna model, and deploy as a real-time guardrail.
Outcome: Reduced hallucination-driven tool errors by detecting the pattern and adding few-shot examples recommended by the insights engine.
Use the free tier to evaluate RAG responses for hallucination, then auto-tune eval criteria using feedback from subject-matter experts.
Outcome: Achieved sub-70% F1 improvements and shipped with confidence, knowing evals are grounded in production data.
Deploy Galileo in VPC to monitor 100% of agent traffic, setting guardrail policies that block harmful responses and control escalation paths.
Outcome: Caught failures in minutes instead of days, with actionable insights that improved agent reliability and reduced operational risk.
Use Cases
- Evaluate and monitor RAG pipelines for accuracy and hallucination prevention
- Build custom evaluators to encode domain-specific success criteria for AI agents
- Deploy low-latency guardrails that block harmful responses in real-time
- Distill expensive LLM judges into lightweight Luna models for cost-effective production monitoring
- Analyze agent behavior trace data to identify failure modes and prescribe fixes
- Run CI/CD evaluations for agent systems before shipping to production
- Use Luna Studio for low-cost, trustworthy evaluations without massive LLM bills
Models Under the Hood
as of 2026-08-30
Limitations
- The free tier is limited to 5,000 traces per month, while the Pro tier at $100/month scales to 50,000 traces.
- Enterprise plans offer unlimited traces and custom rate limits, with deployment options including hosted, VPC, or on-prem.
- Advanced deployment and security features like VPC/on-prem and dedicated support are restricted to the Enterprise tier.
- The platform aims to distill expensive LLM-as-judge evaluators into compact Luna models for low-latency and low-cost monitoring.
as of 2026-08-28
Verification history
We have re-verified Galileo AI Evals 14 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-checked, vendor evidence unchanged
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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
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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 AI Evals 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/mo
Ideal for
Solo developers and small teams evaluating RAG or agent prototypes with up to 5,000 traces per month, exploring eval features without cost.
What this tier adds
Free entry point with 5,000 traces, unlimited users, and unlimited custom evals—enough to run initial experiments.
Pro
$100/mo*
Ideal for
Growing teams launching AI features with confidence, needing 50,000 traces, advanced analytics, and Slack support.
What this tier adds
Adds 50,000 traces (10x free), Standard RBAC, advanced analytics & insights, and dedicated Slack support.
Enterprise
Contact us
Ideal for
Large enterprises with compliance needs, requiring unlimited traces, VPC/on-prem deployment, and premium support.
What this tier adds
Unlimited traces, custom rate limits, hosted/VPC/on-prem deployment, enterprise security, RBAC/SSO, and real-time guardrails.
Where the pricing makes sense
The company stage and team size where Galileo AI Evals's pricing actually pencils out — and where peers do it cheaper.
Galileo's freemium model suits teams experimenting with AI evals; the Pro tier at 50K traces is competitive with LangSmith's similar volume, but Luna models can cut costs 96% versus LLM judges, making it more cost-effective for high-throughput production.
Setup time & first value
How long it actually takes to get something useful out of Galileo AI Evals — broken out by persona, not the marketing-page minute.
You can get value in under an hour: create an account, ingest a few traces, and see the default evals run. For custom evals and auto-tuning, budget a few hours to a day. Deploying guardrails in production may take a couple of days to integrate and tune.
Switching to or from Galileo AI Evals
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangSmith: Export your traces and yaml configs, then recreate chains in Galileo; use the free tier to compare evals before switching.
- →From custom scripts: Use the SDK to send traces and quick-start templates to replicate simple heuristics, then enrich with Galileo's signal ingestion.
- →From Weights & Biases Prompts: Export run data and import into Galileo to leverage its auto-tuning and guardrail features.
- ↗To LangSmith: Export traces and eval results; recreate workflows manually, noting Galileo's guardrail lifecycle is not directly transferable.
- ↗To an open-source stack like Langfuse: Export traces and metrics, then rebuild custom evals and guardrails using local infrastructure.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Galileo AI Evals
Common stack mates teams adopt alongside Galileo AI Evals, with the specific reason each pairing earns its keep.
Alternatives to Galileo AI Evals
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