AI visibility report
Galileo ranks #6 in LLM Observability Evals & Gateways AI search.
Outside the top three on 16 of the 25 prompts buyers actually ask.
Braintrust is cited on 12 of those losses.
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Track Galileo across these prompts daily.
Start free trial#6 among 11 vendors · still absent from 88% of tracked prompt responses
Top-3 citations across 125 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
Perplexity
Gemini Search
Google AI Mode
Bing Copilot
ChatGPTVisible, but narrative can improve. Galileo ranks #6 on presence but #9 on sentiment. The brand appears relatively often, but competitors may be getting more favorable language when they appear.
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 winning3
What LLM observability tools do ML engineering teams typically use to annotate and review production traces for quality feedback?
Avg # 1.0 · 1 platform
Which evaluation platforms for LLM outputs are easiest to plug into an existing CI pipeline for a five-engineer team?
Avg # 1.0 · 1 platform
Which LLM tracing platforms make it easiest to replay a failed multi-step agent run and pinpoint exactly where reasoning went wrong?
Avg # 1.5 · 2 platforms
Where Galileo is losing5
Which LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements?
Competitors on 4 platforms
Track this promptWhich LLM observability platforms can a small team get running against a production RAG pipeline in under a day?
Competitors on 4 platforms
Track this promptLooking for an eval platform that supports automated safety and toxicity scoring on LLM outputs at scale — what are my options?
Competitors on 4 platforms
Track this promptWhich LLM observability platforms integrate natively with the most popular agent frameworks so traces appear automatically with no manual instrumentation?
Competitors on 4 platforms
Track this promptI'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes?
Competitors on 4 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
...x / Arize AI | ML observability / OpenTelemetry teams | Strong | Deep trace inspection, evals, clustering/drift | | Galileo | Teams wanting packaged quality evaluation | Strong | Built-in quality evaluators and failure analysis | | Datado...
What LLM observability tools do ML engineering teams typically use to annotate and review production traces for quality feedback?
Runtime enforcement: Only a few (e.g., Failproof AI, Galileo) can actively stop bad agent actions in real time; most platforms only observe and log.befailproof.aibefailproof.ai.
What platforms support end-to-end tracing of multi-agent pipelines including tool calls, retrieval steps, and sub-agent spawning?
The strongest options are Future AGI, Galileo Luna-2, Arize Phoenix, Langfuse, and Braintrust, each with different trade-offs in infrastructure, latency, and observability. 🔑 Key Considerations --------------------- * Async vs. Inline: Async...
Which LLM eval platforms support async evaluation at scale without blocking the inference path or adding latency for end users?
Most cited sources7
237 Best LLM Observability Tools for Debugging and Tracing | Galileo
galileo.ai·Blog Post
- 10
7 Best LLM Eval Platforms Compared | Galileo
galileo.ai·Blog Post
- 9
8 Best AI and LLM Observability Tools in 2026 | Galileo
galileo.ai·Blog Post
- 6
10 Best Low-Latency LLM Evaluation Tools in 2026 | Galileo
galileo.ai·Blog Post
- 5
Domain-Specific LLM Evaluation: Why Generic Rubrics ...
galileo.ai·Blog Post
- 4
6 Best LLM Monitoring Solutions for Enterprise in 2026 - Galileo AI
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 | Google AI Mode | Perplexity | ChatGPT | Bing Copilot |
|---|---|---|---|---|---|
Capability2/5 cited (40%) | |||||
Which LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements? | A competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which LLM evaluation platforms support custom rubric-based scoring for domain-specific correctness beyond generic faithfulness metrics? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Looking for an eval platform that supports automated safety and toxicity scoring on LLM outputs at scale — what are my options? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
What platforms support end-to-end tracing of multi-agent pipelines including tool calls, retrieval steps, and sub-agent spawning? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Which LLM gateways handle multi-provider fallback and automatic retries while preserving full trace context across the switch? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Developer Experience4/5 cited (80%) | |||||
What LLM observability tools do ML engineering teams typically use to annotate and review production traces for quality feedback? | A competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited | Neither your brand nor a competitor was cited |
I'm looking for an LLM observability platform with a great team collaboration workflow — where engineers and PMs can both review trace quality without SQL knowledge. | A competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited | A competitor was cited |
Which LLM tracing platforms make it easiest to replay a failed multi-step agent run and pinpoint exactly where reasoning went wrong? | A competitor was cited | Your brand was cited | Your brand and a competitor were cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which LLM eval platforms have the best prompt playground experience for iterating on system prompts against a saved test dataset? | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which LLM gateway tools give developers the best real-time cost and token usage visibility across multiple LLM providers during development? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Integrations & Ecosystem2/5 cited (40%) | |||||
Which LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which LLM observability platforms integrate natively with the most popular agent frameworks so traces appear automatically with no manual instrumentation? | A competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited | A competitor was cited |
I'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Which LLM tracing platforms export trace data to a data warehouse so analysts can run custom eval queries alongside product metrics? | A competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited | A competitor was cited |
What LLM gateway tools integrate best with secret managers and internal auth systems for enterprise teams rolling out to multiple product teams? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Performance & Reliability3/5 cited (60%) | |||||
Which LLM observability platforms stay reliable under traffic spikes from batch eval jobs running thousands of LLM calls simultaneously? | A competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited | A competitor was cited |
What are the most production-hardened LLM gateway options for an enterprise team needing 99.9% uptime with circuit-breaker support? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What LLM tracing platforms handle high-throughput production workloads — millions of traces per day — without degrading query performance? | A competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which LLM eval platforms support async evaluation at scale without blocking the inference path or adding latency for end users? | A competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited | Your brand was cited |
Which LLM gateways add the least latency overhead when routing between LLM providers — safe to use in production for sub-500ms SLAs? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Setup & First Run2/5 cited (40%) | |||||
Which LLM observability platforms can a small team get running against a production RAG pipeline in under a day? | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
What are the best OpenTelemetry-compatible tracing backends for LLM apps that work out of the box without custom span parsing? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
What's the fastest LLM tracing platform for instrumenting a Python-based agent framework without rewriting existing code? | Your brand and a competitor were cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
I'm evaluating LLM gateway solutions for a startup — which ones have the simplest self-hosted setup with a working UI on day one? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which evaluation platforms for LLM outputs are easiest to plug into an existing CI pipeline for a five-engineer team? | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited | A competitor was cited |
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Braintrust | 32.0% | 21.8% | 7.2% | 0.0% | 44.0% | #3.3 | +0.51 |
| 2 | LangChain | 26.4% | 18.8% | 8.8% | 0.0% | 48.0% | #3.3 | +0.42 |
| 3 | Langfuse | 21.6% | 14.3% | 8.0% | 4.0% | 55.2% | #3.1 | +0.59 |
| 4 | Confident AI | 18.4% | 14.7% | 0.0% | 0.0% | 15.2% | #4.9 | +0.43 |
| 5 | Arize AI | 16.0% | 10.9% | 0.0% | 4.8% | 15.2% | #4.4 | +0.51 |
| 6 | Galileo | 12.0% | 6.8% | 0.0% | 12.0% | 10.4% | #2.8 | +0.40 |
| 7 | LiteLLM | 5.6% | 4.1% | 3.2% | 0.0% | 0.0% | #3.4 | +0.52 |
| 8 | Traceloop | 4.0% | 3.8% | 0.0% | 2.4% | 6.4% | #3.6 | +0.25 |
| 9 | Portkey | 4.0% | 3.4% | 0.8% | 0.0% | 21.6% | #5.3 | +0.45 |
| 10 | Helicone | 2.4% | 1.5% | 1.6% | 0.8% | 25.6% | #5.8 | +0.83 |
| 11 | Patronus AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.8% | — | — |
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