Galileo
Also known as: Galileo AI
AI reliability platform that evaluates, observes, and guardrails GenAI apps and agents using its own low latency Luna evaluation models.
Galileo is an AI reliability platform that combines evaluation, observability, and guardrails for generative AI applications and agents. The pitch is a single trust layer that follows a system across its lifecycle: teams test outputs before they ship, watch them in production, and block unsafe or low quality responses at runtime, rather than stitching those concerns together from separate tools. Galileo was founded in 2021 by Vikram Chatterji, Atindriyo Sanyal, and Yash Sheth, engineers who came from Google AI, Google Brain, Apple Siri, and Uber, and it is based in San Francisco. Note that this is the evaluation company at galileo.ai, not the similarly named text to UI design tool.
What sets Galileo apart technically is Luna and Luna-2, its own family of small language models built for evaluation. Because they score at sub 200 millisecond latency and very low cost per token, teams can evaluate 100 percent of production traffic rather than sampling, which is usually too expensive with general purpose models. On top of these run more than twenty research backed evaluators covering hallucination, correctness, completeness, context adherence, RAG quality, and agent reliability, and a continuous learning workflow improves those evaluators over time using human feedback.
In 2025 Galileo shipped an agent reliability platform aimed at multi agent systems, where a single bad action can leak data or cost real money. It gives end to end visibility into every step an agent takes, with agent specific metrics like tool call tracking and instruction following, and an insights engine that clusters similar failures across traces so the root cause surfaces without manual detective work. Galileo Protect turns the same evaluators into runtime guardrails that scan prompts and responses and block violations, and integrates with NVIDIA NeMo Guardrails for added control.
Galileo runs on a usage based subscription. A free tier includes 5,000 traces a month with unlimited users and unlimited custom evaluations, the Pro tier starts at $100 a month billed yearly for 50,000 traces with role based access and advanced analytics, and Enterprise is custom with unlimited traces, single sign on, runtime guardrails, and dedicated inference servers. It deploys as SaaS, in a private cloud, or on premises. Galileo has raised about $68M, led by a Series B from Scale Venture Partners, and counts HP, Twilio, Reddit, and Comcast among its customers.
Vendor details
Canonical URL
https://galileo.ai
Category
Agent infrastructure
Subcategory
Evaluation and observability
Funding status
Founded in 2021 by Vikram Chatterji, Atindriyo Sanyal, and Yash Sheth, engineers from Google AI, Google Brain, Apple Siri, and Uber. Headquartered in San Francisco. Has raised about $68M, headlined by a $45M Series B in October 2024 led by Scale Venture Partners, with Premji Invest, Databricks Ventures, ServiceNow Ventures, Citi Ventures, and Battery Ventures participating. Customers include HP, Twilio, Reddit, and Comcast.
Company status
independent
Use cases & customers
Primary use cases
Target customers
Deployment options
Integrations
Native integrations with agent frameworks like CrewAI, NVIDIA NeMo and NIM for guardrails, and the MongoDB MAAP ecosystem, plus CI/CD pipelines for unit testing AI before production. Evaluations run on Galileo's own Luna and Luna-2 small language models for low latency scoring.
In practice
Your agent fails intermittently in production and debugging means digging through thousands of traces by hand. Galileo's insights engine clusters similar failures and surfaces the root cause, so you fix the pattern, not single runs.
You want to evaluate every production response, not a sample, but general purpose judge models make that too expensive. Galileo's Luna models score at sub 200 millisecond latency and low cost, making 100 percent traffic evaluation affordable.
Your regulated application cannot risk shipping an unsafe response. Galileo Protect turns your evaluators into runtime guardrails that scan prompts and responses and block violations before they reach a customer.
Sources & related URLs
Related / legacy domains
Capability coverage
6.5 / 14 capabilities · 46%
| Integrations & Tool CallingIntegrates with agent frameworks such as CrewAI, NVIDIA NeMo and NIM, the MongoDB MAAP ecosystem, CI/CD pipelines, and third party data and API providers, but it is an evaluation and observability layer rather than a tool calling hub. | Partial |
|---|---|
| Workflow OrchestrationObserves and evaluates agent workflows but does not orchestrate agent execution, sequencing, or branching at runtime. | Unable to verify |
| Knowledge Grounding & RAGProvides RAG quality and context adherence evaluators to measure grounding, but does not itself provide retrieval or knowledge grounding to agents. | Unable to verify |
| Human Oversight & GuardrailsGalileo Protect provides runtime guardrails that scan prompts and responses and block safety or quality violations, integrating with NVIDIA NeMo Guardrails, and a continuous learning from human feedback workflow refines evaluators. Runtime guardrails are an Enterprise capability. | Full |
| Security, Identity & GovernanceRole based access control on Pro, single sign on on Enterprise, and VPC or on premises deployment for strict security needs, a strong enterprise posture, though specific certifications are not detailed here. | Partial |
| Observability & AuditabilityCore product. Agentic observability with end to end trace capture across every agent step, real time monitoring and alerts, and an insights engine that clusters similar failures to surface root causes. | Full |
| Memory & State PersistencePersists traces and evaluation datasets, but does not provide an agent memory or state persistence layer. | Unable to verify |
| Deployment & Data ResidencyDeploys as managed SaaS, in a customer's virtual private cloud, or fully on premises, giving real deployment and data residency choices for regulated and security sensitive teams. | Full |
| Prebuilt Agents, Templates & PacksShips a library of more than twenty out of the box evaluators, but these are evaluation components rather than prebuilt agents or starter application templates. | Unable to verify |
| Triggers & Channel CoverageReal time monitoring with alerts on failures and drift, plus CI/CD integration to run evaluations before deployment, but no conversational channels or agent invocation. | Partial |
| Model Flexibility & RoutingEvaluates outputs from any model and framework and runs its own Luna evaluation models, and publishes an agent leaderboard across models, but it is not a routing gateway for the user's production models. | Partial |
| APIs, SDKs & MCP ExtensibilityProvides APIs and SDKs to instrument applications and capture traces, with CI/CD integration for pipeline testing, though no MCP server is documented. | Partial |
| Testing, Debugging & OptimizationCore product. More than twenty research backed evaluators for hallucination, correctness, and agent reliability, offline and online evaluation, CI/CD unit testing of AI pipelines, and continuous improvement from human feedback. | Full |
| Browser & Computer UseNot applicable. Galileo is an evaluation, observability, and guardrails platform with no browser automation or computer use. | Unable to verify |
Pricing
From $100/mo billed yearly · free tier
Monthly traces within subscription tiers; Pro pricing scales with trace volume
Included quota
Free 5,000 traces/mo (unlimited users, unlimited custom evals). Pro 50,000 traces/mo. Enterprise unlimited traces.
What is public
Galileo publishes Free and Pro pricing with trace limits and feature differences. Enterprise pricing and exact trace tier steps above Pro are custom.
Billing mechanics
A usage based subscription metered on monthly traces. Free includes 5,000 traces; Pro starts at $100 a month billed yearly for 50,000 traces and scales with volume; Enterprise is a custom contract with unlimited traces. Runtime guardrails and dedicated inference are Enterprise only.
Cost watchouts
Trace volume is the main cost driver and grows with production traffic. Capabilities many teams consider essential, such as runtime guardrails and private deployment, require the Enterprise tier.
Variable cost rationale
Tiers are predictable monthly subscriptions, but Pro pricing scales with trace volume, so a busy production system that logs 100 percent of traffic can move up the trace tiers quickly. Galileo's low cost Luna evaluators are designed to keep that scaling affordable.
Additional watchouts
Pro pricing scales with trace volume, so 100 percent traffic evaluation on a high volume app can climb tiers. Runtime guardrails (Galileo Protect), SSO, and VPC or on premises deployment are gated to Enterprise.
Overage / add-ons
Pro pricing scales with trace volume above the base allotment. Enterprise offers unlimited traces under a custom contract.
Sales call required
No — self-serve available
Free / trial
Free tier: 5,000 traces/month, unlimited users, unlimited custom evals, no card
Lowest paid plan
Pro $100/mo billed yearly (50,000 traces, RBAC, advanced analytics)
Commercial notes
Sold both bottom up through a generous free and Pro self serve tier and top down to large enterprises that need VPC or on premises deployment, runtime guardrails, and SSO. Customers skew to large companies including HP, Twilio, Reddit, and Comcast.
Key ambiguities
How Pro pricing steps up with trace volume above the 50,000 base, and where Enterprise pricing lands, are not disclosed.
Cancellation / refund
Free and Pro are self serve subscriptions, cheaper billed yearly; standard cancellation. Enterprise terms are contractual.
Support SLA / resale
Community support on Free, dedicated Slack support on Pro, and a dedicated customer success manager and SLA on Enterprise.
Missing data
Enterprise pricing, the exact trace tier steps and per trace overage above Pro, and the precise cost of dedicated inference servers are not public.
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