# Galileo AI visibility in LLM Observability Evals & Gateways

Canonical: https://devtune.ai/verticals/llm-observability-evals-gateways/galileo

[Website](https://galileo.ai/)

Updated: 2026-10-22T05:35:19.352871+00:00
Prompts: 25
Runs: 5


## Platforms

- chatgpt-search
- perplexity
- google-ai
- google-ai-mode
- bing-copilot-search

Rank: 8
Total brands: 11
Measured responses: 125
Presence percent: 4
Share of voice percent: 1.935483870967742
Average position: 5.666666666666667
Docs presence percent: 0
Blog presence percent: 4
Brand mention percent: 7.199999999999999


## Profile

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.
Product summary: 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 capabilities

- 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



### Target users

- Enterprise AI/ML engineers building production GenAI applications
- AI platform and reliability teams at Fortune 500 companies
- Data scientists needing turnkey evaluation metrics without custom judge engineering
- Product managers overseeing GenAI application quality and safety
- Developers building and deploying multi-step AI agents
- AI governance and compliance teams requiring real-time guardrails



### Key use cases

- 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

Integrations ecosystem: Galileo integrates with all major LLM providers and a wide set of agentic frameworks. Documented integrations include LangChain and LangGraph, CrewAI, OpenAI SDK and OpenAI Agents SDK, Google ADK, Vercel AI SDK, Pydantic AI, Mastra, Strands Agents, Microsoft Agent Framework, and A2A protocol. Infrastructure integrations include OpenTelemetry and OpenInference (for distributed tracing), NVIDIA NeMo Guardrails and NVIDIA NIM, AWS Marketplace availability, and an MCP Server. SDKs are available in Python and TypeScript. Deployment options span SaaS, Virtual Private Cloud (VPC), and on-premises.
Pricing summary: 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.
Review summary: 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.
Competitive positioning: 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.
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.


### Source urls

- https://galileo.ai/
- https://galileo.ai/pricing
- https://galileo.ai/about
- https://galileo.ai/case-studies
- https://docs.galileo.ai/what-is-galileo
- https://github.com/rungalileo
- https://www.prnewswire.com/news-releases/galileo-raises-45m-series-b-funding-to-bring-evaluation-intelligence-to-generative-ai-teams-everywhere-302276383.html
- https://galileo.ai/blog/announcing-our-series-b
- https://www.prnewswire.com/news-releases/galileo-announces-free-agent-reliability-platform-302508172.html
- https://checkthat.ai/brands/galileo-ai
- https://appsecsanta.com/galileo-ai
- https://langfuse.com/faq/all/best-galileo-ai-alternatives
- https://aws.amazon.com/marketplace/pp/prodview-ecxzfdcsn6jje

Reviewed at: 2026-05-06T17:53:55.208+00:00


### Customer outcomes

| Customer | Summary | Metric |
| --- | --- | --- |
| Satisfi Labs | 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. | Accuracy improved from ~70% toward 100% |
| Clearwater Analytics | 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. | Mean time to detect reduced from ~3 days to minutes |



### Reviews breakdown

| Platform | Score | Score max | Review count | Url |
| --- | --- | --- | --- | --- |
| Capterra | 4.9 | 5 | 26 | Not available |
| G2 | 4.4 | 5 | 17 | https://www.g2.com/products/galileo-galileo/reviews |



### Review themes



#### 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




### Company facts

Founded year: 2021
Hq: San Francisco, CA


#### Founders

- Vikram Chatterji
- Atindriyo Sanyal
- Yash Sheth

Employees range: 101-250
Total funding: $68M
Valuation: Not available
Arr: Not available
Customer count: Not available
Status: Private


Readiness: Not available


## Ranking

| Display name | Pair count | Total pairs | Presence percent | Avg position |
| --- | --- | --- | --- | --- |
| Langfuse | 41 | 125 | 32.800000000000004 | 5.239130434782608 |
| Arize AI | 39 | 125 | 31.2 | 6.219047619047619 |
| LangChain | 38 | 125 | 30.4 | 4.506024096385542 |
| Braintrust | 28 | 125 | 22.400000000000002 | 6.763636363636364 |
| LiteLLM | 19 | 125 | 15.2 | 6.035714285714286 |
| Portkey | 15 | 125 | 12 | 6.542857142857143 |
| Confident AI | 6 | 125 | 4.8 | 3.3333333333333335 |
| Galileo | 5 | 125 | 4 | 5.666666666666667 |
| Helicone | 4 | 125 | 3.2 | 9.166666666666666 |
| Traceloop | 3 | 125 | 2.4 | 6.8 |
| Patronus AI | 1 | 125 | 0.8 | 6.25 |



## Platform breakdown

| Platform | Prompt count | Presence rate |
| --- | --- | --- |
| chatgpt-search | 0 | 0 |
| perplexity | 0 | 0 |
| google-ai | 0 | 0 |
| google-ai-mode | 2 | 8 |
| bing-copilot-search | 3 | 12 |



## Strengths

| Prompt text | Platform count | Avg position |
| --- | --- | --- |
| Looking for an eval platform that supports automated safety and toxicity scoring on LLM outputs at scale — what are my options? | 1 | 1 |



## Gaps

| Prompt text | Competitor presence count |
| --- | --- |
| 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. | 5 |
| What platforms support end-to-end tracing of multi-agent pipelines including tool calls, retrieval steps, and sub-agent spawning? | 5 |
| Which LLM eval platforms support async evaluation at scale without blocking the inference path or adding latency for end users? | 5 |
| I'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes? | 4 |
| Which LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements? | 4 |



## Topic scores

| Topic name | Prompt count | Cited prompt count |
| --- | --- | --- |
| Capability | 5 | 2 |
| Developer Experience | 5 | 0 |
| Integrations & Ecosystem | 5 | 0 |
| Performance & Reliability | 5 | 1 |
| Setup & First Run | 5 | 1 |



## Prompt results

- Prompt text: I'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Braintrust | 2 |
| Langfuse | 10 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Braintrust | 1 |
| Langfuse | 3 |
| LangChain | 6 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Langfuse | 3 |
| Braintrust | 5 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Braintrust | 3 |



##### Bing-copilot-search




- Prompt text: 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.


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Langfuse | 2 |
| Arize AI | 6 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Braintrust | 3 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Langfuse | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Braintrust | 4 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Confident AI | 1 |


- Prompt text: What are the best OpenTelemetry-compatible tracing backends for LLM apps that work out of the box without custom span parsing?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 2 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 2 |



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 2 |
| Traceloop | 4 |



##### Bing-copilot-search




- Prompt text: Which LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 2 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Confident AI | 3 |
| Helicone | 5 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Langfuse | 2 |
| Portkey | 3 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Arize AI | 2 |
| LangChain | 4 |
| Portkey | 6 |
| Langfuse | 8 |



##### Bing-copilot-search




- Prompt text: Which LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 3 |
| Arize AI | 4 |
| Langfuse | 6 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| Langfuse | 2 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 8 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| LangChain | 2 |
| Traceloop | 7 |
| Langfuse | 9 |



##### Bing-copilot-search




- Prompt text: What platforms support end-to-end tracing of multi-agent pipelines including tool calls, retrieval steps, and sub-agent spawning?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Langfuse | 2 |
| Arize AI | 4 |
| Braintrust | 6 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Langfuse | 3 |
| Arize AI | 5 |



##### Google-ai

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Langfuse | 4 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Langfuse | 4 |
| Arize AI | 5 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Arize AI | 3 |


- Prompt text: What LLM tracing platforms handle high-throughput production workloads — millions of traces per day — without degrading query performance?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| LangChain | 3 |
| Arize AI | 5 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 5 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Langfuse | 1 |



##### Bing-copilot-search




- Prompt text: Which LLM eval platforms support async evaluation at scale without blocking the inference path or adding latency for end users?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: 6
Bing-copilot-search: 5



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| LangChain | 2 |
| Langfuse | 3 |
| Braintrust | 4 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Braintrust | 1 |
| LangChain | 2 |
| Arize AI | 4 |



##### Google-ai

| Display name | Position |
| --- | --- |
| LangChain | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Braintrust | 3 |
| Arize AI | 4 |
| Galileo | 6 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Arize AI | 3 |
| Galileo | 5 |


- Prompt text: What LLM observability tools do ML engineering teams typically use to annotate and review production traces for quality feedback?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Arize AI | 1 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Arize AI | 4 |



##### Google-ai

| Display name | Position |
| --- | --- |
| LangChain | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LangChain | 2 |
| Langfuse | 3 |



##### Bing-copilot-search




- Prompt text: Which LLM observability platforms stay reliable under traffic spikes from batch eval jobs running thousands of LLM calls simultaneously?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 3 |
| LangChain | 10 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Helicone | 5 |
| Braintrust | 6 |



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 2 |
| LangChain | 4 |



##### Bing-copilot-search




- Prompt text: Which LLM evaluation platforms support custom rubric-based scoring for domain-specific correctness beyond generic faithfulness metrics?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: 3
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Patronus AI | 3 |
| Braintrust | 6 |
| LangChain | 7 |
| Arize AI | 10 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 3 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Confident AI | 2 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Galileo | 3 |
| LangChain | 8 |
| Arize AI | 9 |



##### Bing-copilot-search




- Prompt text: Which LLM gateways handle multi-provider fallback and automatic retries while preserving full trace context across the switch?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Portkey | 1 |
| LiteLLM | 14 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 2 |



##### Google-ai

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Portkey | 3 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Portkey | 1 |
| LiteLLM | 4 |



##### Bing-copilot-search




- Prompt text: What's the fastest LLM tracing platform for instrumenting a Python-based agent framework without rewriting existing code?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Arize AI | 3 |
| LangChain | 5 |
| Traceloop | 7 |
| Langfuse | 9 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Arize AI | 1 |



##### Google-ai





##### Google-ai-mode





##### Bing-copilot-search




- Prompt text: I'm evaluating LLM gateway solutions for a startup — which ones have the simplest self-hosted setup with a working UI on day one?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Portkey | 7 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 2 |



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Bing-copilot-search




- Prompt text: Which evaluation platforms for LLM outputs are easiest to plug into an existing CI pipeline for a five-engineer team?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LangChain | 4 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 5 |



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 5 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Confident AI | 2 |
| Arize AI | 5 |


- Prompt text: What are the most production-hardened LLM gateway options for an enterprise team needing 99.9% uptime with circuit-breaker support?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| Portkey | 4 |
| LiteLLM | 11 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Portkey | 1 |



##### Google-ai

| Display name | Position |
| --- | --- |
| LiteLLM | 5 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Portkey | 1 |
| LiteLLM | 2 |



##### Bing-copilot-search




- Prompt text: Which LLM observability platforms can a small team get running against a production RAG pipeline in under a day?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: 4



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 4 |
| LangChain | 8 |
| Braintrust | 9 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| LangChain | 3 |
| Arize AI | 5 |
| Braintrust | 8 |



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Langfuse | 2 |
| LangChain | 3 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Confident AI | 1 |
| Galileo | 4 |
| Braintrust | 8 |


- Prompt text: Which LLM tracing platforms make it easiest to replay a failed multi-step agent run and pinpoint exactly where reasoning went wrong?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Langfuse | 4 |
| Braintrust | 6 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 3 |



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LangChain | 4 |
| Langfuse | 5 |
| Arize AI | 7 |



##### Bing-copilot-search




- Prompt text: Which LLM gateways add the least latency overhead when routing between LLM providers — safe to use in production for sub-500ms SLAs?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 7 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Helicone | 5 |



##### Google-ai





##### Google-ai-mode





##### Bing-copilot-search




- Prompt text: What LLM gateway tools integrate best with secret managers and internal auth systems for enterprise teams rolling out to multiple product teams?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Portkey | 7 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Portkey | 5 |



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Portkey | 3 |
| LiteLLM | 5 |



##### Bing-copilot-search




- Prompt text: Which LLM eval platforms have the best prompt playground experience for iterating on system prompts against a saved test dataset?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Braintrust | 1 |
| LangChain | 4 |
| Langfuse | 5 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Braintrust | 2 |
| Langfuse | 3 |



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 1 |
| Arize AI | 15 |



##### Bing-copilot-search




- Prompt text: Which LLM gateway tools give developers the best real-time cost and token usage visibility across multiple LLM providers during development?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Helicone | 1 |
| LiteLLM | 2 |
| Portkey | 10 |
| Langfuse | 12 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Portkey | 1 |
| LiteLLM | 3 |



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LiteLLM | 3 |
| Portkey | 6 |



##### Bing-copilot-search




- Prompt text: Looking for an eval platform that supports automated safety and toxicity scoring on LLM outputs at scale — what are my options?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: 1



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Arize AI | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Arize AI | 1 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Confident AI | 1 |
| Braintrust | 8 |



##### Google-ai-mode





##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Galileo | 1 |


- Prompt text: Which LLM observability platforms integrate natively with the most popular agent frameworks so traces appear automatically with no manual instrumentation?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 3 |
| Arize AI | 4 |
| LangChain | 6 |
| Braintrust | 9 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Langfuse | 5 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Langfuse | 2 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Arize AI | 3 |



##### Bing-copilot-search




- Prompt text: Which LLM tracing platforms export trace data to a data warehouse so analysts can run custom eval queries alongside product metrics?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available
Google-ai-mode: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| Braintrust | 6 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| LangChain | 3 |
| Braintrust | 6 |
| Langfuse | 9 |



##### Bing-copilot-search







## Top sources

| Url | Title | Domain | Logo url | Source vertical | Content type | Citation count | Last30d count |
| --- | --- | --- | --- | --- | --- | --- | --- |
| https://galileo.ai/blog/best-low-latency-llm-evaluation-tools | 10 Best Low-Latency LLM Evaluation Tools in 2026 \| Galileo | galileo.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/0d0bd0d9bc3825a684e7501bb7f6610429121235.png | commercial | blog_post | 7 | 7 |
| https://galileo.ai/blog/best-rag-observability-tools | 5 Best RAG Observability Tools Compared in 2026 \| Galileo | galileo.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/0d0bd0d9bc3825a684e7501bb7f6610429121235.png | commercial | blog_post | 6 | 6 |
| https://galileo.ai/blog/best-llm-eval-platforms-compared | 7 Best LLM Eval Platforms Compared \| Galileo | galileo.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/0d0bd0d9bc3825a684e7501bb7f6610429121235.png | commercial | blog_post | 5 | 5 |
| https://galileo.ai/blog/llm-evaluation-step-by-step-guide | How to Evaluate Large Language Models - Galileo AI | galileo.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/0d0bd0d9bc3825a684e7501bb7f6610429121235.png | commercial | blog_post | 1 | 1 |
| https://galileo.ai/blog/domain-specific-llm-evaluation-expert-annotations | Domain | galileo.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/0d0bd0d9bc3825a684e7501bb7f6610429121235.png | commercial | blog_post | 1 | 1 |



## Response excerpts

| Prompt text | Platform | Excerpt |
| --- | --- | --- |
| Which LLM eval platforms support async evaluation at scale without blocking the inference path or adding latency for end users? | bing-copilot-search | Direct Answer: Platforms like Future AGI, Galileo Luna-2, Arize Phoenix, and Langfuse support asynchronous evaluation at scale, allowing teams to run large evaluation workloads without blocking the inference path or adding latency for end users. |
| Which LLM observability platforms can a small team get running against a production RAG pipeline in under a day? | bing-copilot-search | Best LLM Observability Tools in 2026 - 6 Compared \| Awesome Agents5 Best RAG Observability Tools Compared in 2026 \| Galileo Langfuse\| <1 day (SDK \+ Docker)\| Open-source, self-host\| Self-host (MIT license)\| Lightweight, MIT-licensed, built-in evals. |
| Looking for an eval platform that supports automated safety and toxicity scoring on LLM outputs at scale — what are my options? | bing-copilot-search | ...s at scale: ## 🔑 Key Evaluation Platforms Platform\| Focus Areas\| Safety/Toxicity Features\| Notes ---\|---\|---\|--- Galileo\| Production-grade LLM evals\| Automated toxicity filtering, hallucination detection, custom metrics, continuous monitori... |



## Competitor excerpts

| Platform | Competitor name | Excerpt |
| --- | --- | --- |
| chatgpt-search | LangChain | langchain.com/langsmith/evaluation) Watch out: particularly compelling for LangChain/LangGraph teams; assess fit with your existing stack. |
| chatgpt-search | Langfuse | Explore LangSmith ## 2. Langfuse Best open-source option Particularly good for cross-functional teams: annotation queues let domain experts score traces, add comments, and supply corrected outputs. |
| perplexity | LangChain | Its annotation queues let reviewers inspect runs, apply shared criteria, and track review progress; LangChain specifically describes PMs, engineers, and subject-matter experts as potential annotators. |
| perplexity | Braintrust | ...e turned into dataset examples for future evaluations.[1][2] For an even more explicit review-and-assignment workflow, Braintrust is worth comparing: reviewers can score production traces, add comments and tags, assign items to teammates, and mark r... |
| google-ai | Langfuse | Langfuse (Open Source / Cloud) Langfuse is widely regarded for striking an exceptional balance between developer experience and product/business team usability. |
| google-ai-mode | LangChain | langchain.com/langsmith/annotation-queues-sdk)Use annotation queues - Docs by LangChain[Annotate traces and runs inline - Docs by LangChain](https://docs.langchain.com/langsmith/annotate-traces-in... |
| bing-copilot-search | Confident AI | Best fit for your need: Confident AI is currently the strongest LLM observability platform for cross‑functional collaboration, because it allows engineers, PMs, and domain experts to directly annotate traces, cluster errors, and validate metrics — all... |
| chatgpt-search | LangChain | ## Platforms worth evaluating ## 1. LangSmith Best for LangChain / LangGraph Provides hierarchical traces for model calls, retrieved context, tool behavior, and agent execution. |
| chatgpt-search | Langfuse | Website and docs ## 2. Langfuse Best open-source option Supports nested traces with explicit observation types for agents, tools, retrievers, chains, and model generations. |
| perplexity | Langfuse | Langfuse — represents a run as a nested trace tree with model calls, tool executions, and retrieval steps; its guidance specifically covers nesting tool calls under the agent step that orchestrates them. |
| google-ai | LangChain | LangChain ### 1\. Top Platforms for Multi-Agent Tracing * LangSmith * Best For: Deep ecosystem integration (especially with LangGraph and LangChain), testing, and end-to-end debugging. |
| google-ai-mode | LangChain | langchain.com/langsmith). LangSmith vs Arize: AI agent observability, evals, and deployment compared9 LLM Observability Tools for Production AI Agents 2. Langfuse * Best for: Open-source, highly customizable tracing with robust self-hosting options. |



## Trend

Visibility delta: -2
Avg position delta: 0.833333333333333
Citation count delta: -5