Galileo
AI observability and evaluation platform that turns offline evals into production guardrails.
Galileo is the most complete platform for turning AI agent evaluations into production guardrails, with unique features like Luna Studio, Eval Engineer, and auto-tune feedback loops. If you ship agentic systems at scale, it's worth the investment. For basic LLM monitoring, lighter tools like Langfuse may suffice.
- AI agent teams needing production-grade guardrails and evaluation
- Enterprise RAG deployments requiring low-cost, high-accuracy evals
- Security-conscious teams adopting OWASP agent threat frameworks
- Developers wanting to integrate evaluation into Claude and Codex workflows
- Small projects or hobbyists on a tight budget (pricing is enterprise-level)
- Teams needing only basic monitoring without eval/guardrail loop
- Users seeking a fully open-source solution (Galileo is proprietary)
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Skip Galileo if you only need basic LLM monitoring without evaluation and guardrailing.
Pro pricing scales with trace volume beyond 50,000 traces per month, so costs can increase for high-traffic systems.
Galileo's pricing fits mid-to-large enterprise AI teams that need comprehensive eval-to-guardrail capabilities. At $100/mo Pro, it's more expensive than basic monitoring tools like Langfuse (free tier up to 50k traces) but offers unique features like Luna distillation and auto-tune. For startups, the free tier (5k traces) is a good starting point. Enterprise pricing is custom and can be costly, but includes dedicated support and on-prem deployment.
In short
Galileo — AI observability and evaluation platform that turns offline evals into production guardrails. Best for AI agent teams needing production-grade guardrails and evaluation, Enterprise RAG deployments requiring low-cost, high-accuracy evals, Security-conscious teams adopting OWASP agent threat frameworks. Free to start; paid plans from $100/mo.
Viability Score
How likely is Galileo to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- 20+ out-of-box evals for RAG, agents, safety, security
- Custom evaluators for domain-specific metrics
- Luna Studio for low-cost, high-trust evaluations
- Eval Engineer integration with Claude and Codex
- Auto-tune metrics from live feedback (70%+ F1 scores)
- Insights engine for failure mode detection and root cause analysis
- Guardrail policies: block harmful responses, control agent actions
- Synthetic, development, and production data ingestion
- Subject matter expert annotations
- GCache: caching solution for AI agents
- Pre-production evals become production governance
- OWASP-based security evaluation (ASI01, ASI02)
- Low-latency evaluation on L4 GPUs
- Agent Control open-source control plane
- Real-time guardrails with Luna models
About Galileo
Galileo is an AI observability and evaluation platform for teams building production-grade AI agents, RAG systems, and LLM applications. It bridges offline testing and online safety by converting evaluators into guardrails. The platform includes Luna Studio for low-cost, high-trust evaluations; Eval Engineer for eval integration into Claude and Codex; auto-tune metrics from live feedback (70%+ F1 scores); Luna models for low-latency guardrailing at 96% lower cost; insights engine for failure mode detection; guardrail policies; synthetic data generation; and GCache for agent caching. Adopted by Writer, Cisco, NVIDIA, HP, and others. Recent updates include Agent Control open-source, Cisco AI Defense integration, and OWASP ASI01/ASI02 security evaluations. Unlike generic LLM monitoring, Galileo provides end-to-end visibility into agent completions and a seamless path from evals to guardrails.
Behind the Verdict
Galileo isn't for everyone. If you're a solo dev building a simple chatbot, skip it—the complexity and cost don't make sense. But if you're shipping production AI agents that need to be reliable, safe, and continuously evaluated, Galileo is currently the most complete platform we've seen. Its standout feature is bridging offline evaluation and online guardrailing without custom glue code. The auto-tune feedback loop that hits 70%+ F1 scores is a genuine differentiator. Eval Engineer integration with Claude and Codex is also a smart move for teams already in those ecosystems. On the downside, the free tier caps at 5,000 traces—enough for experimentation but not serious testing. The Pro tier at $100/month for 50,000 traces scales costs quickly. Compared to Langfuse, which is more straightforward for basic LLM monitoring, Galileo is opinionated about the eval-to-guardrail lifecycle. We'd reach for Galileo when we need end-to-end visibility into agent completions, failure mode detection, and production guardrails—all in one platform. It's especially strong for enterprise RAG and agent deployments where security (OWASP, VPC/on-prem) matters. But if your team just needs simple prompt logging and latency tracking, lighter tools will serve you better without the overhead.
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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 build a custom evaluator for agent response accuracy, auto-tune it from live feedback, and distill it into a Luna model. The Luna model runs in production as a guardrail, blocking hallucinated responses in real-time.
Outcome: Achieve >70% F1 accuracy on agent responses, reduce false positives, and deploy with confidence.
You use Galileo's OWASP ASI01 evaluations to detect goal hijack attempts in your agent. You set up guardrail policies to block suspicious tool calls and escalation paths.
Outcome: Mitigate agent security threats proactively, with continuous monitoring and automated response.
Using the Eval Engineer feature, you add Galileo evaluations directly into your Claude and Codex workflows, testing agent behavior during development before shipping.
Outcome: Catch bugs earlier, reduce iteration cycles, and ship more reliable agents.
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 2026-07-06
Limitations
- Focuses on eval and guardrails; may require integration with proprietary LLMs for actual generation.
- Low-latency eval on L4 GPUs suggests hardware limits under heavy load.
- Free tier caps may apply.
as of 2026-06-30
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
Individual developers and small teams experimenting with AI evaluation and needing up to 5,000 traces per month.
What this tier adds
Starting tier with 5,000 traces per month, unlimited users, and unlimited custom evals.
Pro
$100/mo (billed yearly)
Ideal for
Teams launching AI applications in production who need higher trace volume (50,000/mo), standard RBAC, and advanced analytics.
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 organizations requiring unlimited traces, custom rate limits, VPC/on-prem deployment, enterprise-grade security, and dedicated support.
What this tier adds
Unlimited traces, custom rate limits, hosted/VPC/on-prem deployment, enterprise RBAC/SSO, dedicated CSM, real-time guardrails, and 24/7 support compared to Pro.
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 mid-to-large enterprise AI teams that need comprehensive eval-to-guardrail capabilities. At $100/mo Pro, it's more expensive than basic monitoring tools like Langfuse (free tier up to 50k traces) but offers unique features like Luna distillation and auto-tune. For startups, the free tier (5k traces) is a good starting point. Enterprise pricing is custom and can be costly, but includes dedicated support and on-prem deployment.
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 developers: get started in minutes by ingesting traces via SDK or API. The free tier provides instant access to 20+ out-of-box evals. For complex auto-tune pipelines and guardrail policies, expect a few hours to configure and distill Luna models. Enterprise teams may need a few days for custom deployment (VPC/on-prem) and RBAC setup.
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 Langfuse: export your traces and evals as JSON, then import via Galileo's API or upload in the UI.
- →From Weights & Biases: use Galileo's integration to sync datasets and evaluation results.
- →From custom eval scripts: replace your LLM-as-judge calls with Galileo's SDK for auto-tune and distillation.
- ↗To Langfuse: export your traces and evaluation results from Galileo via API and import into Langfuse's trace format.
- ↗To Arize AI: use the open telemetry exporter to send Galileo traces to Arize.
- ↗To Datadog: configure Galileo's monitoring export to Datadog via webhook or custom integration.
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
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
Eval engineering platform that turns evals into production guardrails at 96% lower cost.
Arize Phoenix
Open-source AI observability for LLM agent tracing and evaluation.
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