Galileo
Galileo is an AI observability and eval engineering platform that turns offline evals into production guardrails for agents and RAG.
If your agents already carry production traffic and your eval scores sit in a dashboard doing nothing about it, Galileo is the platform that connects measurement to enforcement — distilling optimized evals into Luna models that guardrail live traffic, with OWASP ASI-aligned security evals on the side. It is a poor fit if you only need basic tracing; that's a different tool category and you'd be paying for eval engineering you won't use. Watch the tier line: real-time guardrails and dedicated
Verified 13h ago · liveness 80/100 · cite: rightaichoice.com/tools/galileo
- AI agent teams running real production traffic who need evals to gate live behavior
- Enterprise RAG deployments that need evaluation at scale with SME annotations
- Security-conscious teams mapping OWASP agent threats to detection and defense
- Developers who want eval workflows inside Claude and Codex
- Teams that only need basic tracing and monitoring without an eval-to-guardrail workflow
- Buyers who require a fully open-source platform — Galileo is proprietary
- Simple chatbot projects with no agentic or RAG evaluation needs
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Skip Galileo if you only need request tracing and latency dashboards for a non-agentic app — the eval-to-guardrail loop, guardrail policies, and Luna-2 distillation are the product, and you'd pay Enteprise pricing for capability you won't exercise.
Pro lists 50,000 traces per month and states pricing scales based on number of traces, so volume above the cap moves your bill rather than just triggering an alert
Free at $0/month with 5,000 traces and unlimited users is a real entry point for a single developer validating evals. Pro at $100/month billed yearly (33% savings) covering 50,000 traces fits a small platform team running one or two agents. Enterprise with custom billing is priced for organizations that need unlimited traces, VPC or on-prem deployment, SSO/RBAC, and real-time guardrails — closer to a platform contract than a seat license, and comparable in shape to other enterprise
In short
Galileo — Galileo is an AI observability and eval engineering platform that turns offline evals into production guardrails for agents and RAG. Best for AI agent teams running real production traffic who need evals to gate live behavior, Enterprise RAG deployments that need evaluation at scale with SME annotations, Security-conscious teams mapping OWASP agent threats to detection and defense. Free to start; paid plans from $100/mo.
No head-to-head comparison for Galileo yet — the alternatives below are the closest matches we list.
What's new in Galileo
Checked 8 days agoAcross the latest 2 updates: 1 launch and 1 news mention.
The 2026 Caching Playbook for Agents: Bigger Prompts, Smaller Bills
Galileo published a caching playbook for agents covering how to cut inference costs when prompts grow, tied to GCache agent caching.
Introducing Eval Engineer: Bringing Eval Expertise to Claude and Codex
Launch of Eval Engineer, which extends Galileo's eval tooling into Claude and Codex coding-assistant workflows.
What people actually say about Galileo — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
22 mentions across 5 sources (Hacker News, YouTube, Product Hunt, App Store, Lemmy), 57 more we could not attribute · researched Oct 11, 2026.
Weighted by the 79 posts each of 5 sources contributed.
- +Published pricing tiers ($0 / $100-mo / Enterprise) are unusually transparent for this category
- +20+ out-of-box evals span RAG, agents, safety, and security so you start fast
- +OWASP-mapped evals for ASI01 goal hijack and ASI02 tool abuse are concrete, named risks
- +Eval-to-guardrail lifecycle is the core product, not a bolted-on afterthought
- +Luna-2 distillation promises compact guardrails at a fraction of LLM-as-judge cost
- −Essentially no independent community reviews exist to validate the marketing claims
- −Advanced skill level means solo builders and small teams will hit a wall
- −Pro tier caps at 50,000 traces, pushing growing teams into unpriced Enterprise
- −Real-time guardrails and dedicated inference server are Enterprise-only features
- −Keyword-name collision with satellites and a medical app muddies any search-based research
- • Real-time guardrails and dedicated inference server are Enterprise-only, so mid-size teams face an unpriced upgrade
- • Distillation and custom evaluators may require additional ML engineering headcount, an implicit cost
- • Pro caps at 50,000 traces — overage handling isn't published
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: 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-tuned metrics from live feedback targeting 70%+ F1 scores
- Distillation of LLM-as-judge evaluators into compact Luna small language models
- Low-latency guardrails running on 100% of production traffic at 96% lower cost
- Guardrail policies that block harmful responses
- Eval scores that automatically control agent actions, tool access, and escalation paths
- Insights Engine analyzing models, prompts, functions, context, datasets, traces, and MCP servers
- OWASP-based security evaluations for agent threats
- Luna Studio for building and managing eval suites
- Eval Engineer extending eval tooling into Claude and Codex workflows
- GCache agent caching for larger prompts and lower inference cost
- Dataset building from synthetic, development, and live production data
- Subject matter expert annotation capture
- Deployment as SaaS, in a virtual private cloud, or on-premises
About Galileo
Galileo is an AI observability and evaluation platform built on a single premise: the evals you run before launch should keep enforcing quality after launch. You start by building datasets from synthetic, development, and live production data, capture subject matter expert annotations, then build evaluators on top of them. Galileo auto-tunes those metrics from live feedback so scores stay fit to your environment instead of drifting toward generic benchmarks. The eval-to-guardrail lifecycle is the product's center. Eval scores can automatically control agent actions, tool access, and escalation paths without glue code, and optimized evals get distilled into compact Luna models that Galileo says monitor 100% of traffic at 96% lower cost than running LLM-as-judge at scale. The Insights Engine ingests models, prompts, functions, context, datasets, traces, and MCP servers to surface failure modes and prescribe fixes — the canonical example being a hallucination that produced wrong tool inputs, with the recommended remedy of added few-shot examples. Coverage spans 20+ out-of-box evals for RAG, agents, safety, and security, plus custom evaluators that encode your own domain rules. Security work maps OWASP agent threats to detection and defense, and the platform integrates with NVIDIA NeMo and NIM. Deployment is your choice of SaaS, virtual private cloud, or on-premises. Recent releases widen the workflow: Luna Studio for building and managing eval suites, Eval Engineer (2026-05-19) which brings eval tooling into Claude and Codex, and a 2026-05-26 caching playbook for agents with larger prompts tied to GCache. Named reference customers include Writer, Cisco, NVIDIA, HP, MongoDB, CrewAI, and Clearwater Analytics. Against pure tracing tools, Galileo's differentiator is enforcement — it closes the loop from measurement to live control, not just dashboards.
Behind the Verdict
Where Galileo earns its keep is the gap most AI teams hit around month three: offline evals go green, then production drifts and nobody has a mechanism to stop the bad behavior in flight. Galileo's answer is to distill your tuned evaluators into Luna small models and run them on live traffic. That is a genuinely different product from an observability dashboard, and it's why the customer list skews toward companies shipping agents rather than demos. Pick Galileo when you have agentic or RAG workloads in production, when subject matter experts are already annotating outputs, and when you need eval scores to actually gate tool calls, escalation paths, or harmful responses. The Insights Engine is the part teams lean on daily — it watches models, prompts, functions, context, datasets, traces, and MCP servers, then names the failure mode instead of just flagging a red number. Pass on it if you only need tracing and cost monitoring. You can get that cheaper elsewhere, and Galileo's value lives in the eval-to-guardrail loop you wouldn't be using. Same goes for teams that need a fully open-source stack — this is proprietary software, and there's no open-core escape hatch. The practical caveats. Real-time guardrails and dedicated low-latency inference servers are Enterprise-tier, so the loop Galileo markets isn't complete on Free or Pro. Deployment flexibility (SaaS, VPC, on-prem) matters for regulated buyers, but on-prem means you're running the evaluation engine on your own L4 GPUs. And if your prompts are large, read the caching playbook before you budget — prompt growth quietly becomes the biggest line item in agent inference costs. The closest alternative depends on what you're missing. If you want tracing plus evals without guardrail enforcement, the lighter
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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.
Their customer-support agent has been misrouting tool calls in production. They start on Free at $0/month, wire in traces, run Galileo's out-of-box agent evals to see where tool_selection scores drop, then use the Insights Engine to find the failing turns.
Outcome: They identify a hallucination driving incorrect tool inputs, apply the prescribed fix of adding few-shot examples, and confirm the eval score recovers before promoting the change.
LLM-as-judge evaluation costs are scaling with traffic and latency is unacceptable for a live product. They optimize custom evaluators against live feedback, then distill them into Luna-2 small language models via Galileo.
Outcome: Distilled evals run against 100% of traffic at low latency, and eval scores feed guardrail policies that gate responses and escalation paths without custom glue code.
They need to answer how the agent behaves under OWASP ASI01 goal-hijack and ASI02 tool-abuse scenarios before an internal review. They run Galileo's OWASP-based security evals against the agent's tool inventory.
Outcome: They get a mapped detection-and-defense view per attack variant and can document which guardrail policies block which behaviors, instead of arguing from theory.
Use Cases
- Debug LLM agent behavior in production to catch hallucinations and incorrect tool calls
- Auto-tune custom evaluators from live feedback to reach domain-specific accuracy targets
- Distill expensive LLM-as-judge evaluators into Luna-2 models for low-latency guardrailing at lower cost
- Enforce guardrail policies that block harmful responses and control agent actions without glue code
- Wire offline evals into CI/CD so deployments ship with evaluation gates
- Evaluate agent security against OWASP ASI01 goal-hijack and ASI02 tool-abuse threats
- Centralize eval suites in Luna Studio to control cost while scaling coverage
Models Under the Hood
as of 2026-10-16
Limitations
- Galileo's value depends on you bringing your own data: you integrate your AI systems and production traffic to capture groundtruth, so a team with no traffic and no eval habits gets little from it.
- The platform is proprietary — no open-source path.
- Galileo also publishes its own claim that evals can be wrong roughly 20% of the time before the review-driven self-improvement loop corrects them, so plan to review.
as of 2026-09-30
Verification history
We have re-verified Galileo 19 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
- — 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
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 19 verification passes.
Free to cite with attribution — this page re-verifies continuously.
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/mo
Ideal for
A solo developer or small team validating evaluation on a single agent before spending anything — Galileo's published free entry point at $0/month.
What this tier adds
Starting tier: 5,000 traces per month, unlimited users, unlimited custom evals, and access to the 20+ out-of-box RAG, agent, safety, and security evals.
Pro
$100/mo
Ideal for
A small platform team running one or two agents in production that needs standard RBAC and analytics, and can commit to an annual term.
What this tier adds
Raises the trace ceiling to 50,000 per month and adds standard RBAC, advanced analytics and insights, and dedicated Slack support; billed yearly with 33% savings at $100/month, scaling with trace volume.
Enterprise
Custom
Ideal for
Organizations whose agents touch money, compliance, or customer-facing tool calls and that need deployment control plus real-time enforcement on live traffic.
What this tier adds
Custom billing adds unlimited traces, custom rate limits, hosted/VPC/on-prem deployment, SSO and RBAC, real-time guardrails, low-latency dedicated inference servers, a dedicated CSM, forward deployed engineering, 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.
Free at $0/month with 5,000 traces and unlimited users is a real entry point for a single developer validating evals. Pro at $100/month billed yearly (33% savings) covering 50,000 traces fits a small platform team running one or two agents. Enterprise with custom billing is priced for organizations that need unlimited traces, VPC or on-prem deployment, SSO/RBAC, and real-time guardrails — closer to a platform contract than a seat license, and comparable in shape to other enterprise
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.
A solo developer on Free can connect an app and see first traces within an afternoon. A platform team wiring production traffic, building custom evaluators, and tuning F1 against their own data should budget one to two weeks before eval scores are trustworthy enough to gate a release. The Enterprise path — VPC or on-prem deployment, SSO/RBAC, real-time guardrails on dedicated inference — is a
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 manual prompt review spreadsheets: import synthetic and production traces as datasets, then rebuild your review criteria as custom evaluators
- →From LLM-as-judge scripts: port the judge prompt into a Galileo evaluator, optimize it against live feedback, then distill it into a Luna-2 model for lower-latency scoring
- →From a tracing-only observability tool: keep the trace pipeline, and layer 20+ out-of-box RAG, agent, safety, and security evals on top of the data you already capture
- ↗To an open-source eval framework: export your datasets and evaluator definitions, then reimplement the judge prompts yourself — expect to lose the auto-tune and Luna-2 distillation path
- ↗To a tracing-only monitoring tool: keep Galileo's datasets as regression fixtures, but you give up guardrail policies and eval-driven control of agent actions
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
YouTube returned 6 videos for “Galileo”, and we withheld 6: 6 could not be judged, because “Galileo” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Galileo.
Official links
Tools that pair well with Galileo
Common stack mates teams adopt alongside Galileo, with the specific reason each pairing earns its keep.
Galileo AI Evals
Galileo AI Evals turns offline evaluations into production guardrails that gate your AI agents in real time.
Langfuse
Open-source LLM observability and agent evals: trace, evaluate, and improve production AI agents with MIT-licensed tooling.
Arize Phoenix
Arize Phoenix is open-source LLM observability: trace agent steps, run evals, and self-host your traces.
Alternatives to Galileo
View all
Galileo AI Evals
Galileo AI Evals turns offline evaluations into production guardrails that gate your AI agents in real time.
Langfuse
Open-source LLM observability and agent evals: trace, evaluate, and improve production AI agents with MIT-licensed tooling.
Arize Phoenix
Arize Phoenix is open-source LLM observability: trace agent steps, run evals, and self-host your traces.
Frequently Asked Questions
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