Galileo Review (2026)

What Galileo actually is

Galileo is an evaluation-and-observability platform - it calls the category “Evaluation Intelligence.” It captures traces from your GenAI or agent app, scores them with research-backed metrics, and then turns those offline evals into real-time production guardrails. Twenty-plus evaluators ship out of the box for RAG, agents, safety and security, and you can write custom ones.

Before anything else, the disambiguation, because it matters more here than for any other tool we cover.

There are two unrelated companies using the Galileo name. The one reviewed here is the LLM eval platform at galileo.ai, founded in 2021 by Vikram Chatterji, Atindriyo Sanyal and Yash Sheth. A different product - Galileo AI at usegalileo.ai - was a text-to-UI design tool that Google acquired. Half the review aggregators, G2 entries and pricing blogs you’ll find are describing the design tool. If you see a “$39 a month” plan or a note that “your designs are publicly visible,” that’s the wrong company. Verify the domain on every source before you trust a number.

With that cleared up: this is the best-funded platform in the space. Galileo has raised roughly $68M total, including a $45M Series B in October 2024 led by Scale Venture Partners, with strategic money from Databricks Ventures, ServiceNow Ventures and others. Of the platforms we track, it has the lowest bankruptcy risk.

The distinctive part: Luna eval models

The technical bet that sets Galileo apart is its proprietary evaluation foundation models, Luna and Luna-2.

These are small models fine-tuned specifically for eval tasks - hallucination detection, context adherence, prompt-injection, PII - rather than a general-purpose LLM pressed into service as a judge. The argument is economic. If your evaluator is cheap and fast enough, you can run it on every production request instead of sampling, which is what turns offline evals into live guardrails.

Galileo claims Luna is up to 11x faster and 97% cheaper than a GPT-3.5-based judge, with sub-second eval latency. Those are vendor benchmarks, not independent measurements - label them as claims in your own head. The idea is sound and the direction is right; the specific multipliers are marketing until you’ve run them on your own traffic.

Pricing: two self-serve tiers, then a sales call

Tier	Price	Traces/mo	Deployment
Free	$0	5,000	Cloud
Pro	$100/mo billed yearly	50,000	Cloud
Enterprise	Contact sales	Unlimited	Cloud, VPC or on-prem

The free tier is genuinely good - 5,000 traces, unlimited users, unlimited custom evals. That’s enough to evaluate the product properly, and unlimited seats is rare at $0. Galileo reinforced this in July 2025 with a free Agent Reliability Platform launch.

The cliff is above Pro. Pro is $100/mo billed yearly (the page markets a 33% annual saving) for 50,000 traces, and it notes pricing “scales based on number of traces” past that. Everything that makes it an enterprise platform - unlimited traces, VPC or on-prem, SSO, real-time guardrails at scale, a dedicated CSM - is Enterprise contact-sales. The recurring, credible complaint about Galileo is exactly this: you can’t get a real price or size ROI without talking to a rep.

Self-hosting: what you actually get

Enterprise-only, and there’s no open-source version. VPC and on-prem are deployment options you unlock through a contract. If data residency is non-negotiable and you want to keep traces on your own infrastructure without a sales cycle, the open-source platforms in this category - Langfuse, Laminar - are a better starting point. Galileo’s self-host is a premium, closed offering, not a free one.

Galileo versus Maxim

Both are closed, cloud-first agent-eval platforms, so the comparison is about depth versus workflow. Galileo leads on funding, on research-grade eval metrics, and on turning evals into guardrails. Maxim leads on pre-release agent simulation. If your pain is “our agent hallucinates in production and we need to measure and block it,” Galileo’s Luna-plus-guardrails loop is the stronger story. If your pain is “we need to stress-test the agent before it ships,” Maxim’s simulation is more directly aimed at that. Both gate self-host behind Enterprise, so neither wins the data-residency question cheaply.

Should you use it?

Use Galileo if you’re an enterprise GenAI team that’s been burned by production hallucinations, you want research-grade eval metrics and real-time guardrails, and you can run a sales process to get there.

Don’t use Galileo if you need free or open-source self-hosting, you want transparent pricing you can commit to on a card past $100/mo, or you’re a small team that will never justify the enterprise motion.

Bottom line: the strongest-funded and most research-forward platform here, and the Luna bet on cheap continuous evaluation is a real one. Just confirm you’re looking at the right Galileo, and go in knowing that everything serious lives behind a sales call.

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Pricing and features verified against galileo.ai on 23 July 2026. This category ships breaking changes monthly - we re-verify every 30 days.