Galileo AI Evals
Galileo AI Evals turns offline evaluations into production guardrails that gate your AI agents in real time.
Pick Galileo when eval results need to actually stop a bad agent action at runtime, not just appear in a chart the day after. The eval-to-guardrail loop plus Luna distillation is a real architectural difference from trace-first tools, and the free tier at 5,000 traces/month is enough to test that claim. Teams that only want latency and token counts will pay for depth they never use.
Verified 1d ago · liveness 78/100 · cite: rightaichoice.com/tools/galileo-ai-evals
- Enterprise teams running AI agents on live traffic that need runtime guardrails, not post-hoc dashboards
- Platform teams standardizing an eval-engineering practice across multiple AI products
- Developers debugging agent failures who want a named failure mode plus a suggested fix
- Teams trying to cut LLM-as-judge evaluation costs by distilling evals into small models
- Small teams that only need basic LLM monitoring or request logs
- Projects where the setup effort of dataset and eval design outweighs the depth it returns
- Teams whose only requirement is latency, token, and cost tracking
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Skip Galileo if you only need latency and token dashboards, or if you can't commit to yearly billing to get the $100/month Pro rate for 50,000 traces.
Pro is advertised at $100/month billed yearly for a 33% saving, so monthly billing costs more than the headline number suggests.
The free tier (5,000 traces/month, unlimited users, unlimited custom evals) is genuinely usable for a small team experimenting. Pro at $100/month billed yearly for 50,000 traces fits a funded product team running agents in production. Compared with LangSmith or Weights & Biases, Galileo bundles eval-to-guardrail enforcement rather than monitoring alone, which is why the Pro and Enterprise steps exist — but only Enterprise gets unlimited traces, VPC/on-prem, SSO, and real-time guardrails.
In short
Galileo AI Evals — Galileo AI Evals turns offline evaluations into production guardrails that gate your AI agents in real time. Best for Enterprise teams running AI agents on live traffic that need runtime guardrails, not post-hoc dashboards, Platform teams standardizing an eval-engineering practice across multiple AI products, Developers debugging agent failures who want a named failure mode plus a suggested fix. Free to start; paid plans from $100/mo.
No head-to-head comparison for Galileo AI Evals yet — the alternatives below are the closest matches we list.
What's new in Galileo AI Evals
Checked yesterdayAcross the latest 2 updates: 2 launches.
Introducing Eval Engineer: Bringing Eval Expertise to Claude and Codex
A new Eval Engineer tool puts evaluation expertise directly inside Claude and Codex environments.
GCache: Caching Without the Chaos
GCache is a structured caching system for AI agents aimed at reducing unpredictability and improving 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: October 2026
How we score →Key Features
- 20+ out-of-box evals for RAG, agents, safety, and security
- Custom evaluators that encode domain expertise
- Auto-tune evals from live production feedback
- Distill LLM-as-judge evals into compact Luna models
- Run Luna models on L4 GPUs for low-latency scoring
- Monitor 100% of traffic at a claimed 96% lower cost than LLM-as-judge
- Guardrail policies that block harmful responses
- Eval scores control agent actions, tool access, and escalation paths
- Insights engine diagnoses failure modes and prescribes fixes
- Ingest models, prompts, functions, context, datasets, traces, and MCP servers
- Build datasets from synthetic, development, and production data
- Capture subject-matter-expert annotations as ground truth
- Eval Engineer for Claude and Codex (May 2026)
- GCache structured caching for AI agents (March 2026)
- Deploy as SaaS, virtual private cloud, or on-premises
About Galileo AI Evals
Galileo AI Evals is an AI observability and evaluation platform where the evals you run before launch become the guardrails you enforce after it. You build datasets from synthetic, development, and live production data, capture subject-matter-expert annotations, and start from 20+ out-of-box evals covering RAG, agents, safety, and security — then add custom evaluators to encode your own domain expertise. It's aimed at enterprise teams running AI agents on real traffic, not at hobby projects that just need a request log. The core mechanism is a lifecycle rather than a dashboard. Galileo auto-tunes metrics from live feedback so evals match your environment, then distills the expensive LLM-as-judge evaluators into compact Luna models that run on L4 GPUs and monitor 100% of traffic at what the vendor claims is 96% lower cost than LLM-as-judge. Eval scores can then control agent actions, tool access, and escalation paths, so pre-production tests become production governance without glue code. Alongside that, an insights engine ingests models, prompts, functions, context, datasets, traces, and MCP servers to identify failure modes and prescribe fixes — for example, suggesting few-shot examples when a hallucination is producing incorrect tool inputs. Recent releases include Eval Engineer for Claude and Codex (May 2026) and GCache structured caching for agents (March 2026). Deployment is SaaS, VPC, or on-premises, and there's a free tier at 5,000 traces/month with Pro at $100/month billed yearly for 50,000 traces. Compared with observability-first tools that bolted evals onto tracing later, Galileo leads with evaluation engineering and treats monitoring as the enforcement layer. That makes it heavier to adopt than a latency-and-token dashboard, but considerably more useful once agent failures start costing you money.
Behind the Verdict
The decision here is mostly about what you expect an eval platform to do after the test passes. Galileo's answer is that pre-production evals become production governance: an eval score can block a harmful response, restrict a tool, or trigger an escalation path. If that sentence describes a gap in your stack, Galileo is built for you. Where it earns its keep is cost. LLM-as-judge evaluation gets expensive fast at full traffic volume, and distilling optimized evals into compact Luna models that run on L4 GPUs is the vendor's answer — advertised at 96% lower cost than LLM-as-judge while still covering 100% of traffic. We'd validate that number against your own trace mix before budgeting on it, but the architecture is the right shape for continuous evaluation. The insights engine is the other half. It pulls in models, prompts, functions, context, datasets, traces, and MCP servers, then names the failure mode and suggests a fix. In practice that turns debugging from a dashboard-reading exercise into something closer to a code review comment. The May 2026 Eval Engineer for Claude and Codex pushes that further by putting eval expertise inside the editors developers already use. When to pass: if your team only tracks latency, token spend, and error rates, this is a lot of platform for the job, and a lighter tracing tool will cost less and onboard faster. Small projects where setup effort outweighs evaluation depth should also stay away. Budgeting caveat worth flagging: the $100/month Pro rate is a yearly-billing price, so month-to-month buyers should expect to pay more or commit for the discount. Above 50,000 traces you're in enterprise territory, with VPC and on-premises options if you can't send traces to a SaaS endpoint. The closest comparison is trace-first
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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.
You connect your agent traces and MCP servers to Galileo, start from the out-of-box agent and RAG evals, then use the insights engine on a failing turn — a hallucination produced incorrect tool inputs — and apply the prescribed fix of adding few-shot examples for correct tool input.
Outcome: The failure mode is diagnosed and fixed in a single loop instead of a multi-day log hunt, and the corrected eval is kept for regression testing.
You capture subject-matter-expert annotations, let Galileo auto-tune the metrics from live feedback using the April 2026 evaluation-improvement mechanism, then distill the tuned evals into Luna models and roll them out as guardrail policies that block harmful responses.
Outcome: Pre-production evals become production guardrails that gate agent actions and escalation paths without glue code, at low latency on L4 GPUs.
You install Eval Engineer into your Claude or Codex environment and use it to write and refine evaluations alongside the code you're already writing.
Outcome: Eval expertise is available at the point of development, so agents ship with measurement instead of retrofitted tests.
Use Cases
- Evaluate and monitor RAG pipelines for accuracy and hallucination prevention
- Build custom evaluators that 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 cheaper production monitoring
- Analyze agent trace data to identify failure modes and prescribe fixes
- Run CI/CD evals for agent systems before shipping to production
- Use Luna Studio for low-cost, trustworthy evaluations without large LLM bills
Models Under the Hood
as of 2026-10-16
Limitations
- Galileo positions itself as an AI observability and eval-engineering platform spanning evaluation, monitoring, observability, and real-time guardrails.
- Its headline evals cover RAG, agents, safety, and security, with 20+ out-of-box metrics plus custom evaluators.
- It advertises distilling evaluators into compact Luna models that monitor 100% of traffic at 96% lower cost, and capturing ground truth through synthetic, development, and production data plus subject-matter-expert annotations — implying substantial eval data and expert tuning are prerequisites for the deepest capabilities.
as of 2026-09-29
Verification history
We have re-verified Galileo AI Evals 17 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
- — 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-checked, vendor evidence unchanged
- — 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
A solo developer or small team instrumenting a first agent and iterating on custom evals before committing budget.
What this tier adds
Starting tier: 5,000 traces per month, unlimited users, unlimited custom evals at $0/month.
Pro
$100/mo billed yearly
Ideal for
A funded product team running agents in production that has outgrown the 5,000-trace free ceiling.
What this tier adds
Adds 50,000 traces per month, standard RBAC, advanced analytics and insights, and dedicated Slack support at $100/month billed yearly.
Enterprise
Custom
Ideal for
Regulated or high-scale organizations that need unlimited traces, VPC or on-prem deployment, SSO, and 24/7 support.
What this tier adds
Adds unlimited traces, custom rate limits, hosted/VPC/on-prem deployment, enterprise-grade security with RBAC and SSO, dedicated CSM, real-time guardrails, and forward deployed engineering support; custom contact-us pricing.
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.
The free tier (5,000 traces/month, unlimited users, unlimited custom evals) is genuinely usable for a small team experimenting. Pro at $100/month billed yearly for 50,000 traces fits a funded product team running agents in production. Compared with LangSmith or Weights & Biases, Galileo bundles eval-to-guardrail enforcement rather than monitoring alone, which is why the Pro and Enterprise steps exist — but only Enterprise gets unlimited traces, VPC/on-prem, SSO, and real-time guardrails.
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.
A solo developer can instrument a first agent and see the free-tier evals within an hour or two. A product team wiring traces, MCP servers, and custom evaluators should plan a sprint. Enterprises deploying guardrails on VPC or on-prem will spend longer on security review and a sales cycle before real-time guardrails are live.
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 LLM-as-judge pipelines: distill your judge prompts into Luna models so the same scoring runs at lower latency and cost.
- →From LangSmith: re-point trace ingestion to Galileo, then rebuild generic evals as custom evaluators tuned on your own data.
- →From manual QA review: capture the reviewer's annotations as subject-matter-expert ground truth and let Galileo auto-tune evals from that feedback.
- →From Weights & Biases: keep W&B for experiment tracking and add Galileo as the production eval-and-guardrail layer.
- ↗To LangSmith: export your traces and rebuild your out-of-box evals from their metric library if you only need monitoring.
- ↗To Weights & Biases Weave: move traces across if your priority shifts to experiment tracking over guardrails.
- ↗To a self-built eval harness: reimplement your custom evaluators against your own judge prompts, accepting the LLM-as-judge cost Galileo was absorbing.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Galileo AI Evals”, and we withheld 6: 6 did not mention Galileo AI Evals. We are showing none, because we could not prove any of them are about Galileo AI Evals.
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.
Galileo
Galileo is an AI observability and eval engineering platform that turns offline evals into production guardrails for agents and RAG.
Langfuse
Open-source LLM observability and agent evals: trace, evaluate, and improve production AI agents with MIT-licensed tooling.
Maxim AI
Maxim AI evaluates, simulates and observes AI agents — with Bifrost, its open-source LLM gateway, handling routing, budgets and guardrails.
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