AI visibility report
Portkey ranks #10 in LLM Observability Evals & Gateways AI search.
Outside the top three on 18 of the 25 prompts buyers actually ask.
Braintrust is cited on 7 of those losses.
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Start free trial#10 among 11 vendors · still absent from 97.3% of tracked prompt responses
Top-3 citations across 75 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
ChatGPT
Gemini Search
PerplexityNarrower footprint, stronger tone. Portkey ranks #10 on presence but #5 on sentiment. That means the brand is framed well when it appears, but still needs broader prompt-response coverage.
Where Portkey is losing
Prompts where competitors are visible and Portkey is not.
These prompt-level losses are the first prompts to track and repair.
Where Portkey is winning1
Which AI gateways let me route between OpenAI, Anthropic, and open-source models with a single API call?
Avg # 5.0 · 1 platform
Where Portkey is losing5
Which LLM observability tools work with OpenTelemetry so I don't have to add yet another vendor SDK?
Competitors on 3 platforms
Track this promptWhich LLM eval platforms support running automated evaluations on production traces with custom metrics?
Competitors on 3 platforms
Track this promptWhat are the best tools for detecting hallucinations and faithfulness issues in RAG pipelines?
Competitors on 3 platforms
Track this promptWhich AI observability platforms can be self-hosted with one command using Docker Compose?
Competitors on 2 platforms
Track this promptWhat AI eval platforms support on-premise or VPC deployment for regulated industries?
Competitors on 2 platforms
Track this prompt
Track Portkey daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
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.
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 Facts
- Founded
- 2023
- HQ
- San Francisco, CA, USA
- Founders
- Rohit Agarwal, Ayush Garg
- Employees
- 11-50
- Funding
- ~$18M
- ARR
- ~$5M (as of June 2024)
- Customers
- 24,000+ organizations
- Status
- Acquisition by Palo Alto Networks (NASDAQ: PANW) pending, an
Target users
Key Capabilities10
- 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
Key Use Cases8
- 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
Portkey customer outcomes
25+ GenAI use cases managed at 30M policies/month
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.
Thousands of dollars saved via semantic caching
Deployed Portkey in GitHub CI/CD workflows to cache LLM test runs, eliminating repeated token spend on unchanged tests while maintaining production performance quality.
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.
10,000+ daily queries managed
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.
Recent Trend
How AI describes Portkey3
...| --- | --- | --- | --- | --- | | OpenRouter | Hosted | ✅ | ✅ | ✅ | ✅ | | Portkey AI Gateway | Both | ✅ | ✅ | ✅ | ✅ | | [LiteLLM](https://litellm.ai?utm_source...
Which AI gateways let me route between OpenAI, Anthropic, and open-source models with a single API call?
...| --- | | LiteLLM | Yes | Virtual keys, provider key abstraction | Strong (team/user/project budgets) | Excellent | Yes | | Portkey | Yes | Centralized key vault and governance | Strong | Excellent | Yes | | Helicone | Yes (including cost-based limits) |...
Which AI proxies handle rate limiting, key rotation, and cost tracking across teams centrally?
\[1\] | | Portkey Gateway | Open-source gateway + optional hosted components | Enterprise governance | Strong routing, caching, guardrails, observability, policy enforcement.
Which LLM gateways are open-source and self-hostable for teams that don't want a SaaS dependency?
Most cited sources2
Alternatives in LLM Observability Evals & Gateways6
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.
Reviews
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
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.
Pricing
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.
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.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | Gemini Search | ChatGPT | Perplexity |
|---|---|---|---|
Evaluation0/5 cited (0%) | |||
Which LLM platforms have the best workflows for human annotation and labeling of model outputs? | |||
What tools provide model-graded evaluation with calibrated reference-free scoring for chatbots? | |||
Which LLM eval platforms support running automated evaluations on production traces with custom metrics? | |||
What are the best tools for detecting hallucinations and faithfulness issues in RAG pipelines? | |||
Which evaluation platforms let me convert development-time evals into production guardrails automatically? | |||
Gateways & Routing2/5 cited (40%) | |||
What gateways have the lowest latency overhead when routing high-volume LLM traffic? | |||
Which LLM gateways are open-source and self-hostable for teams that don't want a SaaS dependency? | |||
Which AI gateways let me route between OpenAI, Anthropic, and open-source models with a single API call? | |||
What LLM gateway platforms support automatic fallbacks, retries, and load balancing across providers? | |||
Which AI proxies handle rate limiting, key rotation, and cost tracking across teams centrally? | |||
Production Readiness0/5 cited (0%) | |||
What AI eval platforms support on-premise or VPC deployment for regulated industries? | |||
What LLM monitoring platforms integrate with PagerDuty, Slack, or Datadog for alerting workflows? | |||
Which observability tools include real-time alerting on quality drops, not just latency? | |||
Which AI guardrail platforms provide pre-execution intervention to block unsafe agent actions before they run? | |||
Which LLM observability platforms scale to billions of traces per month at enterprise volumes? | |||
Setup & First Run0/5 cited (0%) | |||
Which AI observability platforms can be self-hosted with one command using Docker Compose? | |||
Which LLM observability tools work with OpenTelemetry so I don't have to add yet another vendor SDK? | |||
I want to add eval tracking to my agent — which platforms have the simplest Python decorator-style integration? | |||
What's the easiest way to log every LLM call my app makes for debugging without changing my application architecture? | |||
What's the fastest way to start tracing my LLM application calls without rewriting my code? | |||
Tracing & Debugging0/5 cited (0%) | |||
Which LLM observability tools show token usage, latency, and cost per step in an agent pipeline? | |||
What platforms support replaying production traces in development for reproducible debugging? | |||
Which observability platforms offer the best agent execution tracing for multi-step LLM workflows? | |||
What tools let me drill into a single user session to debug exactly what my agent did at each step? | |||
Which AI observability tools surface unknown failure patterns I wouldn't have written tests for? | |||
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Braintrust | 26.7% | 26.4% | 2.7% | 0.0% | 26.7% | #8.5 | +0.39 |
| 2 | Confident AI | 13.3% | 8.0% | 0.0% | 4.0% | 13.3% | #5.0 | +0.37 |
| 3 | LangChain | 13.3% | 6.9% | 5.3% | 0.0% | 13.3% | #9.3 | +0.44 |
| 4 | Langfuse | 13.3% | 18.4% | 6.7% | 2.7% | 13.3% | #12.1 | +0.51 |
| 5 | Galileo | 12.0% | 10.9% | 0.0% | 12.0% | 12.0% | #5.5 | +0.52 |
| 6 | Arize AI | 12.0% | 13.8% | 0.0% | 0.0% | 12.0% | #12.9 | +0.45 |
| 7 | BerriAI (LiteLLM) | 5.3% | 2.3% | 4.0% | 0.0% | 2.7% | #9.0 | +0.40 |
| 8 | Helicone | 5.3% | 10.3% | 1.3% | 5.3% | 5.3% | #18.2 | +0.32 |
| 9 | Traceloop | 4.0% | 1.7% | 0.0% | 4.0% | 4.0% | #3.7 | +0.20 |
| 10 | Portkey | 2.7% | 1.1% | 0.0% | 0.0% | 2.7% | #11.0 | +0.42 |
| 11 | Patronus AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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