# Portkey AI visibility in LLM Observability Evals & Gateways

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

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

Latest AI responses: 2026-10-29
Prompts: 25
Runs: 5


## Platforms

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

Rank: 6
Total brands: 11
Measured responses: 125
Presence percent: 4.8
Share of voice percent: 3.3846153846153846
Average position: 3.727272727272727
Docs presence percent: 1.6
Blog presence percent: 0
Brand mention percent: 19.2


## Profile

Overview: Portkey is a San Francisco-based AI infrastructure company founded in January 2023 by Rohit Agarwal and Ayush Garg. It provides a unified production stack for generative AI teams, combining an AI Gateway, LLM observability, guardrails, prompt management, and enterprise governance in one platform. Portkey routes traffic to 1,600+ large language models across 60+ providers via a single OpenAI-compatible API, processing over 500 billion tokens daily for 24,000+ organizations. Its open-source gateway has accumulated 10.8k+ GitHub stars. Recognized as a 2025 Gartner Cool Vendor in LLM Observability and top-rated on G2, Portkey raised a $15M Series A in February 2026. In April 2026, Palo Alto Networks announced its intent to acquire Portkey to serve as the AI Gateway for the Prisma AIRS security platform.
Product summary: Portkey is a full-stack LLMOps platform serving as a unified control plane for production AI. It offers an AI Gateway (routing, fallbacks, load balancing, and semantic caching across 1,600+ LLMs), real-time observability (logs, traces, cost tracking, and 40+ metrics), guardrails (PII redaction, content filtering, and prompt injection prevention), a Prompt Engineering Studio (versioning, deployment, and playground), an MCP Gateway, and enterprise governance (RBAC, SSO, audit logs, and budget controls). Deployable as managed SaaS, hybrid, or fully self-hosted with a 3-line code integration claim and 0.999% uptime SLA.


### Key capabilities

- Unified AI Gateway routing to 1,600+ LLMs across 60+ providers via a single OpenAI-compatible API
- Real-time LLM observability: logs, traces, 40+ metrics, cost attribution, and custom metadata
- Automatic fallbacks, load balancing, retries, and semantic caching for production reliability
- Guardrails engine: PII redaction, content filtering, prompt injection prevention, and LLM-as-judge evaluations
- Prompt Engineering Studio: versioning, testing, variable management, playground, and API deployment
- Enterprise AI governance: RBAC, SSO (Okta/SAML), granular budget and rate limits, and org-wide audit logs
- MCP Gateway for centralized authentication and observability of Model Context Protocol servers
- Virtual key management and multi-tenant workspace isolation
- OpenTelemetry-compliant tracing with open-source MIT-licensed gateway (10.8k+ GitHub stars)
- Open-source LLM pricing database powering cost attribution across 200+ enterprises



### Target users

- AI/ML engineers building and maintaining production LLM applications
- Platform and DevOps teams managing AI infrastructure at scale
- Enterprise AI leaders and CTOs governing multi-team GenAI programs
- Startups and scale-ups productionizing generative AI features
- Security and compliance teams in regulated industries (healthcare, finance, pharma)
- AI agent developers deploying autonomous multi-step agentic workflows



### Key use cases

- Production LLM application reliability and uptime management across multiple providers
- Enterprise AI cost attribution and budget governance across teams and projects
- Multi-provider LLM routing, fallback, and load balancing for high-availability AI
- Agentic AI governance and observability for autonomous multi-step agent workflows
- Prompt versioning, testing, and deployment across LLM providers
- Security compliance and PII protection for regulated industry AI deployments
- Centralized AI access management and key governance for large engineering organizations
- MCP server authentication and observability for enterprise AI tool ecosystems

Integrations ecosystem: Portkey supports 1,600+ LLMs across 60+ providers including OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, Mistral, Cohere, and others via a unified OpenAI-compatible API. It integrates natively with agent frameworks including LangChain, CrewAI, AutoGen, and others. SDKs are available for Python and Node.js. Strategic partnerships include F5 (enterprise AI application delivery) and Aporia (guardrails). The platform is listed on AWS Marketplace and supports private deployment on AWS, GCP, and Azure. An MCP Gateway centralizes authentication and observability for Model Context Protocol servers. Infrastructure-as-code is supported via a Terraform provider and official Helm charts for Kubernetes.
Pricing summary: Portkey offers four tiers. Developer (Free): 10,000 recorded logs/month, 3-day log retention, community support. Production: $49/month, 100,000 recorded logs/month with $9 per additional 100k requests (up to 3M), 30-day retention, guardrails, RBAC, semantic caching, and production support. Enterprise: Custom pricing, 10M+ recorded logs/month, custom retention, SSO, granular budgets and rate limits, private cloud/VPC deployment, SOC2 Type 2, GDPR, and HIPAA compliance, custom BAAs, and dedicated onboarding. A self-hosted open-source tier (MIT license) is also available with no managed log limits. Pricing is log-volume-based rather than request-based.
Review summary: Users on G2 and Gartner Peer Insights consistently praise Portkey's ease of integration, intuitive observability dashboard, responsive customer support, and effectiveness at reducing LLM costs through caching and intelligent routing. Enterprises cite the ability to unify multi-provider logging and cost attribution as a major workflow improvement. Criticisms center on a learning curve for teams new to LLMOps, documentation gaps for advanced configurations, incomplete price tracking for some models in air-gapped setups, and advanced analytics features still maturing. Portkey holds a 4.6/5 rating on G2 and was recognized as a 2025 Gartner Cool Vendor in LLM Observability.
Competitive positioning: Portkey positions itself as a full-stack 'unified control plane for production AI,' differentiating from point-solution observability tools by bundling an AI Gateway, real-time observability, guardrails, prompt management, and enterprise governance into a single platform. Its open-source gateway (10.8k+ GitHub stars) and claimed 3-line integration lower friction versus self-hosted alternatives like LiteLLM. Against standalone eval-focused tools such as Braintrust or Galileo, Portkey emphasizes production reliability and cost governance over evaluation depth. The April 2026 announced acquisition by Palo Alto Networks positions Portkey's gateway as the foundational AI security layer within the Prisma AIRS platform.
Limitations: Log retention is capped at 30 days on the Production plan; custom retention requires Enterprise contracts estimated at $5,000–$10,000+/month. The Production plan limits recorded logs to 100k/month with $9 overage charges per 100k additional requests. Price tracking does not work universally across all models, and air-gapped setups require manual pricing updates. Advanced analytics and visualization features are still maturing relative to established enterprise observability platforms. The platform can feel complex and overwhelming for teams new to LLMOps. Documentation quality receives mixed feedback in user reviews, particularly for advanced configurations.


### Source urls

- https://portkey.ai/
- https://portkey.ai/pricing
- https://portkey.ai/blog/series-a-funding/
- https://www.paloaltonetworks.com/company/press/2026/palo-alto-networks-to-acquire-portkey-to-secure-the-rise-of-ai-agents
- https://ventureburn.com/2023/08/portkey-ai-raises-3m-to-accelerate-generative-ai-apps/
- https://github.com/Portkey-AI/gateway
- https://tracxn.com/d/companies/portkey/__ZBFkMQ22qjERQNfNQH39gbt9Y3bf72VJNqiydQkp6qU
- https://www.g2.com/products/portkey/reviews
- https://portkey.ai/blog/portkey-in-2025-gartner-cool-vendors-in-llm-observability/
- https://aws.amazon.com/marketplace/pp/prodview-o2leb4xcrkdqa

Research approved: 2026-05-06T17:53:55.767+00:00


### Customer outcomes

| Customer | Summary | Metric |
| --- | --- | --- |
| Qoala | Used Portkey to manage 25+ GenAI use cases processing 30 million insurance policies per month, gaining visibility into prompt management, per-use-case cost tracking, and API key governance. | 25+ GenAI use cases managed at 30M policies/month |
| Ario | Deployed Portkey in GitHub CI/CD workflows to cache LLM test runs, eliminating repeated token spend on unchanged tests while maintaining production performance quality. | Thousands of dollars saved via semantic caching |
| Snorkel AI | Replaced a fragmented patchwork of S3 logs, DB queries, and manual visualization with Portkey's unified UI, enabling engineers to identify, debug, and rebuild AI agents with confidence. | Not available |
| Springworks | Used Portkey's analytics and semantic caching to manage cost, latency, and rate limiting for their AI-first employee help desk (Albus) handling over 10,000 daily queries across OpenAI. | 10,000+ daily queries managed |



### Reviews breakdown





### Review themes



#### Praised

- Easy 3-line integration with minimal code changes to existing stack
- Intuitive observability dashboard and analytics
- Effective LLM cost reduction via caching and intelligent routing
- Unified multi-provider API and automatic fallback management
- Responsive and knowledgeable customer support
- Fast time-to-value with immediate production monitoring
- Reliable fallback and retry logic for production uptime
- Strong RBAC and governance features for enterprise teams



#### Criticized

- Steep learning curve for teams new to LLMOps
- Documentation gaps in advanced and air-gapped configurations
- Price tracking incomplete or requiring manual updates for some models
- Advanced analytics and visualization still maturing vs. older enterprise tools
- Log retention limits on lower tiers can be restrictive for compliance use cases
- Feature breadth can feel overwhelming for newcomers




### Company facts

Founded year: 2023
Hq: San Francisco, CA, USA


#### Founders

- Rohit Agarwal
- Ayush Garg

Employees range: 11-50
Total funding: ~$18M
Valuation: Not available
Arr: ~$5M (as of June 2024)
Customer count: 24,000+ organizations
Status: Acquisition by Palo Alto Networks (NASDAQ: PANW) pending, an


Readiness: Not available


## Ranking

| Display name | Pair count | Total pairs | Presence percent | Avg position |
| --- | --- | --- | --- | --- |
| Arize AI | 39 | 125 | 31.2 | 6.011904761904762 |
| Langfuse | 35 | 125 | 28.000000000000004 | 4.298701298701299 |
| Braintrust | 30 | 125 | 24 | 4.615384615384615 |
| LangChain | 20 | 125 | 16 | 3.909090909090909 |
| LiteLLM | 19 | 125 | 15.2 | 4.75 |
| Portkey | 6 | 125 | 4.8 | 3.727272727272727 |
| Confident AI | 5 | 125 | 4 | 4.166666666666667 |
| Galileo | 5 | 125 | 4 | 6.5 |
| Helicone | 3 | 125 | 2.4 | 8.2 |
| Traceloop | 2 | 125 | 1.6 | 1 |
| Patronus AI | 1 | 125 | 0.8 | 5 |



## Platform breakdown

| Platform | Prompt count | Presence rate |
| --- | --- | --- |
| google-ai | 0 | 0 |
| google-ai-mode | 2 | 8 |
| chatgpt-search | 1 | 4 |
| perplexity | 3 | 12 |
| bing-copilot-search | 0 | 0 |



## Strengths





## Gaps

| Prompt text | Competitor presence count |
| --- | --- |
| Which LLM observability platforms integrate natively with the most popular agent frameworks so traces appear automatically with no manual instrumentation? | 5 |
| What platforms support end-to-end tracing of multi-agent pipelines including tool calls, retrieval steps, and sub-agent spawning? | 5 |
| Which LLM tracing platforms make it easiest to replay a failed multi-step agent run and pinpoint exactly where reasoning went wrong? | 4 |
| What LLM gateway tools integrate best with secret managers and internal auth systems for enterprise teams rolling out to multiple product teams? | 4 |
| Which LLM eval platforms have the best prompt playground experience for iterating on system prompts against a saved test dataset? | 4 |



## Topic scores

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



## Prompt results

- 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

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



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Langfuse | 6 |



##### Google-ai-mode

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



##### Chatgpt-search

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



##### Perplexity

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



##### 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

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



#### Platform rows



##### Google-ai

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



##### Google-ai-mode

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



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| LiteLLM | 3 |
| Arize AI | 6 |
| Braintrust | 11 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Langfuse | 2 |
| Arize AI | 4 |



##### Bing-copilot-search




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


#### Brand position by platform

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



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Galileo | 1 |
| Braintrust | 2 |



##### Google-ai-mode

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



##### Chatgpt-search





##### Perplexity

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



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Braintrust | 4 |


- 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

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



#### Platform rows



##### Google-ai

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



##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Braintrust | 4 |
| Arize AI | 8 |
| Galileo | 10 |



##### Perplexity

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



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Arize AI | 4 |
| Galileo | 6 |


- 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

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



#### Platform rows



##### Google-ai





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| LangChain | 3 |
| Arize AI | 15 |



##### Perplexity

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



##### 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

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



#### Platform rows



##### Google-ai





##### Google-ai-mode

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



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 3 |



##### Perplexity

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



##### 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

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



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 2 |



##### Chatgpt-search





##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 6 |



##### Bing-copilot-search

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


- 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

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



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 2 |



##### Google-ai-mode





##### Chatgpt-search





##### Perplexity

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



##### Bing-copilot-search




- 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

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



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 2 |



##### Google-ai-mode

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



##### Chatgpt-search

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



##### Perplexity

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



##### Bing-copilot-search




- 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

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



#### Platform rows



##### Google-ai

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



##### Google-ai-mode





##### Chatgpt-search

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



##### Perplexity

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



##### 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

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



#### Platform rows



##### Google-ai





##### Google-ai-mode

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



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 4 |



##### Perplexity

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



##### 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

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



#### Platform rows



##### Google-ai

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



##### Google-ai-mode

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



##### Chatgpt-search

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



##### Perplexity

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



##### 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

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



#### Platform rows



##### Google-ai

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



##### Google-ai-mode

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



##### Chatgpt-search

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



##### Perplexity

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



##### 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

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



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Galileo | 4 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Galileo | 3 |



##### Chatgpt-search





##### Perplexity

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



##### 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

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



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Arize AI | 7 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Arize AI | 3 |
| Patronus AI | 4 |



##### Bing-copilot-search




- 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

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



#### Platform rows



##### Google-ai

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



##### Google-ai-mode

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



##### Chatgpt-search

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



##### Perplexity

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



##### Bing-copilot-search

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


- 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

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



#### Platform rows



##### Google-ai

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



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 4 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Braintrust | 1 |
| Langfuse | 2 |
| LangChain | 8 |
| Arize AI | 10 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Braintrust | 1 |
| Langfuse | 3 |
| LangChain | 4 |



##### Bing-copilot-search

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


- 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

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



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 2 |
| Langfuse | 3 |
| Arize AI | 5 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 2 |
| Arize AI | 4 |
| Langfuse | 6 |



##### Perplexity

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



##### 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

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



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Arize AI | 2 |
| Langfuse | 4 |



##### Google-ai-mode

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



##### Chatgpt-search

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



##### Perplexity

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



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Braintrust | 2 |
| Arize AI | 3 |


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


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: 2
Chatgpt-search: 4
Perplexity: 1
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

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



##### Google-ai-mode

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



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Portkey | 4 |
| Helicone | 11 |



##### Perplexity

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



##### 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

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



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Traceloop | 1 |



##### Chatgpt-search

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



##### Perplexity

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



##### 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

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



#### Platform rows



##### Google-ai

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



##### Google-ai-mode





##### Chatgpt-search

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



##### Perplexity

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

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



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Braintrust | 2 |
| Langfuse | 4 |
| Helicone | 5 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 9 |



##### Perplexity

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



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Confident AI | 4 |


- 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

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



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LiteLLM | 2 |
| Portkey | 4 |



##### Chatgpt-search





##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Helicone | 6 |



##### 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

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



#### Platform rows



##### Google-ai

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



##### Google-ai-mode

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



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Braintrust | 3 |
| LangChain | 5 |
| Arize AI | 6 |



##### Perplexity

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



##### Bing-copilot-search







## Top sources

| Url | Title | Domain | Logo url | Source vertical | Content type | Citation count | Last30d count |
| --- | --- | --- | --- | --- | --- | --- | --- |
| https://docs1.portkey.ai/docs/product/ai-gateway/fallbacks | Fallbacks | docs1.portkey.ai | Not available | commercial | documentation | 7 | 7 |
| https://docs.portkey.ai/docs/product/observability/traces | Tracing - Portkey (Prisma AIRS AI Gateway) Docs | docs.portkey.ai | Not available | commercial | documentation | 5 | 5 |
| https://portkey.ai/docs/product/ai-gateway | AI Gateway - Portkey Docs | portkey.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/a7fe751941f9ebd223ca6576a9d489a4af08ec9c.png | commercial | documentation | 3 | 3 |
| https://portkey.ai/docs/enterprise/security | Security - Portkey Docs | portkey.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/a7fe751941f9ebd223ca6576a9d489a4af08ec9c.png | commercial | documentation | 3 | 3 |
| https://portkey.ai/docs/guides/use-cases/enterprise-ready-unified-api | Unified LLM API with Automatic Failover & Error Handling | portkey.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/a7fe751941f9ebd223ca6576a9d489a4af08ec9c.png | commercial | documentation | 2 | 2 |
| https://portkey.ai/docs/product/ai-gateway/fallbacks | Fallbacks - Portkey Docs | portkey.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/a7fe751941f9ebd223ca6576a9d489a4af08ec9c.png | commercial | documentation | 2 | 2 |
| https://docs.portkey.ai/docs/guides/use-cases/track-costs-using-metadata | Tracking LLM Costs Per User with Portkey - Portkey Docs | docs.portkey.ai | Not available | commercial | documentation | 2 | 2 |
| https://github.com/Portkey-AI/docs-core/blob/main/product/observability/traces.mdx | docs-core/product/observability/traces.mdx at main · Portkey ... | github.com | Not available | commercial | documentation | 1 | 1 |



## Response excerpts

| Prompt text | Platform | Excerpt |
| --- | --- | --- |
| Which LLM gateways handle multi-provider fallback and automatic retries while preserving full trace context across the switch? | perplexity | Portkey is the clearest match for all three requirements: it supports automatic retries and provider fallbacks, and logs the attempts in a shared trace so you can inspect the failed and successful calls across the switch. |
| I'm evaluating LLM gateway solutions for a startup — which ones have the simplest self-hosted setup with a working UI on day one? | perplexity | Portkey: Its docs describe running the gateway locally with one command, but the cited setup doesn’t establish that this gives you a comparable self-hosted admin UI out of the box. |
| Which LLM gateways add the least latency overhead when routing between LLM providers — safe to use in production for sub-500ms SLAs? | perplexity | [1] \| \| Portkey \| 2.29 ms p99 in the same comparison \| Also very low in that test. |



## Competitor excerpts

| Platform | Competitor name | Excerpt |
| --- | --- | --- |
| google-ai | LangChain | ...zation rather than manual wrapper code for every tool or agent step), a few platforms stand out due to deep integrations with major agent frameworks (like LangChain/LangGraph, CrewAI, LlamaIndex, LlamaIndex Workflows, AutoGen, and the OpenAI Agents SDK). |
| google-ai-mode | Arize AI | Learn More: Review the Arize Auto-Instrumentation Docs on Arize AI. |
| google-ai-mode | LangChain | LangSmith (Best for LangChain & LangGraph) * * Supported Frameworks: LangChain, LangGraph, and native integrations with major LLM SDKs. |
| google-ai-mode | Langfuse | Langfuse (Best Open-Source / Modular Platform) * * Supported Frameworks: LlamaIndex, LangChain, LiteLLM, and OpenTelemetry integrations. |
| chatgpt-search | Langfuse | ...tracing guide](https://mlflow.org/docs/latest/genai/tracing/app-instrumentation/automatic/?utm_source=chatgpt.com) ## 2. Langfuse — broad integrations and open-source flexibility Langfuse lists native framework integrations for LangChain, LangGraph, C... |
| perplexity | LangChain | Arize Phoenix / Arize AX: Broad OpenInference coverage includes LangChain, LangGraph, LlamaIndex, AutoGen/AG2, and CrewAI, among many others. |
| google-ai-mode | Langfuse | AI Agent Observability, Tracing & Evaluation with LangfuseAI Observability Tools: 8 Platforms for Tracing AI Agents in 2026 The le... |
| chatgpt-search | Langfuse | Website: LangSmith ### 2. Langfuse Best open-source all-rounder Captures hierarchical traces with distinct observation types for agents, tools, model generations, chains, and retrieval. |
| bing-copilot-search | Braintrust | Leading options include MLflow, Braintrust, LangSmith, Langfuse, Arize AX/Phoenix, Helicone, ONES.com, and New Relic.[Best LLM Tracing Tools for Multi-Agent Systems in 2026 \| MLflow](https://mlflow.org/articles/best-llm-tracing-tools-for-multi-agent-s... |
| google-ai | Braintrust | Braintrust * Why it shines for replay: Braintrust focuses heavily on closing the loop between production failures and evaluation testing. |
| google-ai-mode | Langfuse | LLM Playground - LangfusePlayground - LangSmith - LangChainIterate in playgrounds - Braintrust * * * 1\. |
| google-ai-mode | LangChain | LLM Playground - LangfusePlayground - LangSmith - LangChainIterate in playgrounds - Braintrust * * * 1\. |



## Trend

Visibility delta: -7.2
Avg position delta: -2.6666666666666665
Citation count delta: -22