AI visibility report
AI visibility report for Galileo in LLM Observability Evals & Gateways.
Outside the top three on 13 of the 25 prompts buyers actually ask.
Braintrust is cited on 4 of those losses.
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Track Galileo across these prompts daily.
Start free trialStill absent from 88% of tracked prompt responses
Top-3 citations across 75 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
Gemini Search
ChatGPT
PerplexityHow to read this. Galileo appears in 12% of tracked prompt responses. Presence is absolute coverage; share of voice is relative citation share; sentiment measures tone only when the brand appears.
Where Galileo is losing
Prompts where competitors are visible and Galileo is not.
These prompt-level losses are the first prompts to track and repair.
Where Galileo is winning2
What are the best tools for detecting hallucinations and faithfulness issues in RAG pipelines?
Avg # 2.0 · 2 platforms
Which AI guardrail platforms provide pre-execution intervention to block unsafe agent actions before they run?
Avg # 3.5 · 2 platforms
Where Galileo is losing5
Which LLM observability tools work with OpenTelemetry so I don't have to add yet another vendor SDK?
Competitors on 3 platforms
Track this promptWhich LLM eval platforms support running automated evaluations on production traces with custom metrics?
Competitors on 3 platforms
Track this promptWhich AI observability platforms can be self-hosted with one command using Docker Compose?
Competitors on 2 platforms
Track this promptWhat AI eval platforms support on-premise or VPC deployment for regulated industries?
Competitors on 2 platforms
Track this promptWhich observability tools include real-time alerting on quality drops, not just latency?
Competitors on 2 platforms
Track this prompt
Track Galileo daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
Overview
Galileo (galileo.ai) is a San Francisco-based AI observability, evaluation, and guardrail platform founded in 2021 by AI veterans from Google AI, Google Brain, Apple Siri, and Uber AI. The platform is purpose-built for enterprise teams building GenAI applications and AI agents, addressing hallucinations, safety risks, and performance degradation across the full development lifecycle. Its flagship innovation, the Luna-2 family of small language models, powers 20+ evaluation metrics running at sub-200ms latency for a fraction of the cost of LLM-as-judge approaches. Galileo's eval-to-guardrail lifecycle enables offline evaluations to become production-grade guardrails without custom glue-code. Trusted by Fortune 50 companies including Comcast and Twilio, the company has raised $68M and reported 834% revenue growth in 2024.
Galileo is an AI observability and eval engineering platform that transforms offline evaluations into production guardrails for GenAI applications and multi-step AI agents. Built around its proprietary Luna-2 small language models, the platform delivers 20+ research-backed evaluation metrics at low latency and cost, an autotune system that calibrates metrics from live feedback, a real-time Protect layer that blocks policy violations before they reach users, and an Insights Engine that automatically surfaces agent failure modes and prescribes fixes. It supports the full eval engineering lifecycle—from experiment management and CI/CD integration to production monitoring and runtime protection—across SaaS, VPC, and on-premises deployments.
Key Facts
- Founded
- 2021
- HQ
- San Francisco, CA
- Founders
- Vikram Chatterji, Atindriyo Sanyal, Yash Sheth
- Employees
- 101-250
- Funding
- $68M
- Status
- Private
Target users
Key Capabilities9
- Luna-2 small language models for sub-200ms, low-cost production evaluations (~$0.02/1M tokens)
- 20+ out-of-box eval metrics covering RAG, agents, safety, and security
- Autotune: auto-calibrates LLM-as-judge metrics from live user feedback to domain-specific accuracy
- Eval-to-guardrail lifecycle: promotes offline evals directly into real-time production guardrails
- Galileo Protect: real-time runtime protection blocking hallucinations and policy violations pre-response
- Agentic Evaluations: multi-step agent tracing with tool-selection, task-completion, and session-level metrics
- Insights Engine: automatic failure mode detection, root-cause analysis, and prescriptive fixes
- Experiment management: prompt versioning, dataset management, and CI/CD-integrated evaluation pipelines
- Flexible deployment: SaaS, VPC, and on-premises with enterprise SSO and RBAC
Key Use Cases8
- Evaluating and guardrailing production RAG pipelines for hallucination and context adherence
- Monitoring and debugging multi-step AI agents and agentic workflows
- Building eval-to-guardrail pipelines that block harmful or off-policy responses in real time
- Running systematic offline experiments for prompt optimization and model version comparison
- Enabling CI/CD-integrated AI quality gates for every model or prompt deployment
- Enterprise AI safety and compliance monitoring for Fortune 500 GenAI deployments
- Reducing mean time to detect AI failures from days to minutes in production
- Scaling evaluation to 100% of production traffic at low cost using Luna-2 distillation
Galileo customer outcomes
Accuracy improved from ~70% toward 100%
Satisfi Labs used Galileo to improve conversational AI response accuracy and scale services efficiently. Their CPO and co-founder noted the platform enabled moving from a significant accuracy ceiling to full resolution.
Mean time to detect reduced from ~3 days to minutes
A Distinguished Engineer at Clearwater Analytics reported that Galileo reduced their time to detect AI failures in production from multiple days to minutes, filling gaps in instrumentation and observability.
Recent Trend
How AI describes Galileo3
* Galileo AI ---------- * Deployment: Enterprise VPC / private cloud deployments * Strengths: * Dataset-based LLM evaluation * Hallucination detection * Structured eval pipelines (RAG + summa...
What AI eval platforms support on-premise or VPC deployment for regulated industries?
...forms | Platform | Evals | Runtime Guardrails | Automatic eval→guardrail path | Notes | | --- | --- | --- | --- | --- | | Galileo | Yes | Yes | Yes (core product concept) | Probably the clearest "offline...
Which evaluation platforms let me convert development-time evals into production guardrails automatically?
Galileo AI Guardrails: real-time runtime protection that scans prompts and responses, blocking harmful actions before they reach users while maintaining audit logs.
Which AI guardrail platforms provide pre-execution intervention to block unsafe agent actions before they run?
Most cited sources8
- 7
5 Best AI Guardrails Platforms Compared in 2026 | Galileo
galileo.ai·Blog Post
- 7
8 Best AI Agent Guardrails Solutions in 2026 | Galileo
galileo.ai·Blog Post
- 7
8 Best AI and LLM Observability Tools in 2026 | Galileo
galileo.ai·Blog Post
- 5
8 Best AI Agent Debugging & Root Cause Analysis Tools | Galileo
galileo.ai·Blog
- 3
7 Top Rag Evaluation Tools | Galileo
galileo.ai·Blog Post
- 3
5 Best RAG Observability Tools Compared in 2026 | Galileo
galileo.ai·Blog Post
Alternatives in LLM Observability Evals & Gateways6
Galileo positions itself as the enterprise-grade, proprietary, all-in-one eval engineering platform where offline evaluations become production guardrails.
- Its core differentiation is the Luna-2 family of small language models that run 20+ sophisticated metrics simultaneously at sub-200ms latency and ~$0.02 per 1M tokens — making 100%-traffic guardrailing economically viable at scale.
- Unlike open-source-first competitors (Langfuse, Arize Phoenix) that prioritize flexibility and data control, Galileo offers an opinionated, managed workflow with autotune feedback loops, pre-packaged eval metrics, and a direct eval-to-guardrail lifecycle requiring no glue-code.
- Compared to gateway-focused tools (Helicone, Portkey, LiteLLM), Galileo goes deeper into evaluation intelligence, agent-level failure detection, and root-cause analysis rather than pure routing and cost observability.
Reviews
Praised
- Precise and reliable evaluation metrics
- Intuitive interface for onboarding and basic use
- Real-time observability and fast failure detection
- Comprehensive platform covering evals, monitoring, and guardrails
- Responsive and professional customer support
- Cost-effective evaluation at production scale
- Easy integration with existing tools and frameworks
Criticized
- Steep learning curve for advanced features
- Difficulty discovering full feature set without vendor guidance
- Limited compatibility with arbitrary pre-trained models
- Sparse public documentation on edge-case configurations
- Low total review volume relative to enterprise positioning
Galileo maintains limited but positive public review volume. On Capterra, it holds approximately 4.9/5 from 26 verified reviews; on G2, approximately 4.4/5 from 17 reviews. Users consistently praise the precision of evaluation metrics, the intuitive onboarding for core features, and the value of real-time observability for catching production AI failures. Criticism centers on a steep learning curve for advanced capabilities, challenges in discovering platform features without vendor assistance, and limited flexibility when integrating arbitrary pre-trained models.
Pricing
Galileo offers three tiers.
- Free
$0/month, includes 5,000 traces/month, unlimited users, and unlimited custom evals.
- Pro
$100/month (billed annually, saving 33%), includes 50,000 traces/month, standard RBAC, advanced analytics, and dedicated Slack support; pricing scales with trace volume.
- Enterprise
custom pricing, includes unlimited traces, custom rate limits, SaaS/VPC/on-premises deployment, enterprise RBAC and SSO, dedicated CSM, real-time guardrails, 24/7 multi-channel support, and forward-deployed engineering support.
Limitations
- Users report a steep learning curve for advanced features despite an intuitive interface for basic use cases.
- Feature discoverability can be challenging, requiring vendor contact to uncover full platform capabilities.
- Compatibility with a broad range of pre-trained models appears limited, reducing flexibility for teams wanting to plug in arbitrary base models.
- Review volume across public platforms is sparse relative to the company's enterprise positioning (~43 total reviews across Capterra and G2 as of early 2026).
- As a proprietary commercial platform, it lacks the data-portability and vendor-lock-in flexibility of open-source alternatives like Langfuse or Arize Phoenix.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | Gemini Search | ChatGPT | Perplexity |
|---|---|---|---|
Evaluation2/5 cited (40%) | |||
Which LLM platforms have the best workflows for human annotation and labeling of model outputs? | |||
What tools provide model-graded evaluation with calibrated reference-free scoring for chatbots? | |||
Which LLM eval platforms support running automated evaluations on production traces with custom metrics? | |||
What are the best tools for detecting hallucinations and faithfulness issues in RAG pipelines? | |||
Which evaluation platforms let me convert development-time evals into production guardrails automatically? | |||
Gateways & Routing0/5 cited (0%) | |||
What gateways have the lowest latency overhead when routing high-volume LLM traffic? | |||
Which LLM gateways are open-source and self-hostable for teams that don't want a SaaS dependency? | |||
Which AI gateways let me route between OpenAI, Anthropic, and open-source models with a single API call? | |||
What LLM gateway platforms support automatic fallbacks, retries, and load balancing across providers? | |||
Which AI proxies handle rate limiting, key rotation, and cost tracking across teams centrally? | |||
Production Readiness1/5 cited (20%) | |||
What AI eval platforms support on-premise or VPC deployment for regulated industries? | |||
What LLM monitoring platforms integrate with PagerDuty, Slack, or Datadog for alerting workflows? | |||
Which observability tools include real-time alerting on quality drops, not just latency? | |||
Which AI guardrail platforms provide pre-execution intervention to block unsafe agent actions before they run? | |||
Which LLM observability platforms scale to billions of traces per month at enterprise volumes? | |||
Setup & First Run0/5 cited (0%) | |||
Which AI observability platforms can be self-hosted with one command using Docker Compose? | |||
Which LLM observability tools work with OpenTelemetry so I don't have to add yet another vendor SDK? | |||
I want to add eval tracking to my agent — which platforms have the simplest Python decorator-style integration? | |||
What's the easiest way to log every LLM call my app makes for debugging without changing my application architecture? | |||
What's the fastest way to start tracing my LLM application calls without rewriting my code? | |||
Tracing & Debugging3/5 cited (60%) | |||
Which LLM observability tools show token usage, latency, and cost per step in an agent pipeline? | |||
What platforms support replaying production traces in development for reproducible debugging? | |||
Which observability platforms offer the best agent execution tracing for multi-step LLM workflows? | |||
What tools let me drill into a single user session to debug exactly what my agent did at each step? | |||
Which AI observability tools surface unknown failure patterns I wouldn't have written tests for? | |||
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Braintrust | 26.7% | 26.4% | 2.7% | 0.0% | 26.7% | #8.5 | +0.39 |
| 2 | Confident AI | 13.3% | 8.0% | 0.0% | 4.0% | 13.3% | #5.0 | +0.37 |
| 3 | LangChain | 13.3% | 6.9% | 5.3% | 0.0% | 13.3% | #9.3 | +0.44 |
| 4 | Langfuse | 13.3% | 18.4% | 6.7% | 2.7% | 13.3% | #12.1 | +0.51 |
| 5 | Galileo | 12.0% | 10.9% | 0.0% | 12.0% | 12.0% | #5.5 | +0.52 |
| 6 | Arize AI | 12.0% | 13.8% | 0.0% | 0.0% | 12.0% | #12.9 | +0.45 |
| 7 | BerriAI (LiteLLM) | 5.3% | 2.3% | 4.0% | 0.0% | 2.7% | #9.0 | +0.40 |
| 8 | Helicone | 5.3% | 10.3% | 1.3% | 5.3% | 5.3% | #18.2 | +0.32 |
| 9 | Traceloop | 4.0% | 1.7% | 0.0% | 4.0% | 4.0% | #3.7 | +0.20 |
| 10 | Portkey | 2.7% | 1.1% | 0.0% | 0.0% | 2.7% | #11.0 | +0.42 |
| 11 | Patronus AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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