Build, scale, and govern AI applications with Portkey — the production stack that brings Gateway, Observability, Guardrails, Governance, and Prompt Management together.
What is an AI Gateway?
An AI gateway is a centralized orchestration platform that helps teams manage, secure, and scale large language models (LLMs) in production environments. It removes the complexity of working directly with individual providers, offering a unified interface to connect applications with one or more LLMs.
Core functions of an AI gateway
Request orchestration
Analyzes incoming queries, normalizes inputs, and routes them to the most suitable model based on latency, cost, and use-case parameters.
Centralized management
Provides a single platform to handle API keys, authentication, model updates, and environment configurations.
Performance optimization
Uses caching, batching, and parallel processing to improve latency and reduce compute and token costs.
Security and compliance
Enforces strong data-protection controls, PII masking, and content moderation to meet organizational and regulatory requirements.
Monitoring and observability
Tracks usage, latency, token counts, and costs while logging every request for debugging and auditability.
Why GenAI Builders Need a Gateway Layer
Moving GenAI from prototypes to production quickly exposes gaps that traditional infrastructure can’t fill.
Teams suddenly juggle multiple providers, scattered keys, and growing compliance risks — all while trying to keep latency, cost, and quality under control.
The production challenges
Fragmented model ecosystem:
Different APIs, credentials, and usage patterns across providers make scaling brittle.
Limited visibility
Without centralized logs and metrics, teams can’t trace errors, measure latency, or monitor token spend.
Key sprawl and security risk
Keys are shared across users or environments, creating compliance and access issues.
Inconsistent reliability
Provider downtimes, quota limits, and slow responses disrupt workflows and customer experience.
Governance blind spots:
No unified way to enforce content policies, moderate outputs, or track who’s calling which model.
Top AI Gateway Solutions in 2026
Platform | Focus Area | Key Features | Ideal For |
|---|---|---|---|
Portkey | End-to-end production control plane | Unified API for 1,600+ LLMs, observability, guardrails, governance, prompt management, MCP integration | GenAI builders and enterprises scaling to production |
Vercel AI Gateway | Frontend integration & caching | Route AI requests from frontend apps, built-in caching, model proxy via Edge Functions | Web developers integrating AI features in client apps |
TrueFoundry Gateway | MLOps workflow management | Model deployment, serving, and monitoring; integrates with internal ML pipelines | ML and platform engineering teams |
Kong AI Gateway | API gateway with AI routing extensions | API lifecycle management, rate limiting, policy enforcement via plugins | Enterprises already using Kong for APIs |
Solo.io Gloo Gateway | Cloud-native API gateway | Envoy-based routing, service mesh integrations, AI traffic policies | Cloud-native teams and service mesh users |
F5 AI Gateway | Enterprise-grade traffic control | Load balancing, security filtering, integration with F5 BIG-IP | Large enterprises with existing F5 infrastructure |
IBM AI Gateway | AI governance and enterprise compliance | Integrates with watsonx, supports enterprise IAM and auditing | Regulated industries (finance, healthcare) |
Azure AI Gateway | Managed Microsoft ecosystem solution | Routing, telemetry, Azure OpenAI and model catalog integration | Enterprises fully on Azure stack |
Cequence AI gateway | API security platform, extending the security layer to AI and LLM endpoints | API defense, traffic inspection, and threat prevention | Security teams and enterprises that need strong API protection and threat prevention. |
Custom Gateway Solutions | In-house or OSS-built layers | Fully customizable; requires ongoing maintenance and scaling effort | Advanced engineering teams needing full control |
In-Depth Analysis of the Top 10 AI Gateway Solutions
Explain that while the comparison table offers a quick overview, the following detailed analyses dive deeper into each solution — covering their core strengths, weaknesses, pricing, customer base, and market reputation — to help teams choose the right gateway for their GenAI production stack.
Portkey
Portkey is an AI Gateway and production control plane built specifically for GenAI workloads. It provides a single interface to connect, observe, and govern requests across 1,600+ LLMs. Portkey extends the gateway with observability, guardrails, governance, and prompt management, enabling teams to deploy AI safely and at scale.
Strengths
Unified API layer
Primarily designed for production teams, so lightweight prototypes may find it more advanced than needed.
Enterprise governance features (e.g., policy-as-code, regional data residency) are part of higher-tier plans.
Deep observability
Detailed logs, latency metrics, token and cost analytics by app, team, or model.
Guardrails and moderation
Request and response filters, jailbreak detection, PII redaction, and policy-based enforcement.
Governance and access control
Workspaces, roles, data residency, audit trails, and SSO/SCIM integration.
Prompt management and versioning
Reusable templates, variable substitution, and environment promotion.
Multi-model routing and reliability controls
Latency- or cost-based routing, fallbacks, canary deployments, and circuit breakers.
MCP integration
Register tools and servers with built-in observability and governance for agent use cases.
Developer experience
Modern SDKs (Python, JS, TS), streaming support, evaluation hooks, and test harnesses.
Limitations
Primarily designed for production teams, so lightweight prototypes may find it more advanced than needed.
Enterprise governance features (e.g., policy-as-code, regional data residency) are part of higher-tier plans.
Pricing Structure
Portkey offers a usage-based pricing model with a free tier for initial development and testing. Enterprise plans include advanced governance, audit logging, and dedicated regional deployments.
Ideal For / Typical Users
GenAI builders, enterprise AI teams, and platform engineers running multi-provider LLM workloads who need observability, compliance, and reliability in production.
4.8/5 on G2 — praised for its developer-friendly SDKs, observability depth, and governance flexibility.
Vercel AI Gateway
Vercel AI Gateway is an extension of Vercel’s developer platform designed to help frontend and edge applications call LLMs efficiently. It abstracts model access behind a single endpoint integrated with Vercel’s Edge Functions, letting developers stream responses directly to users with minimal setup. The focus is on latency, caching, and ease of use rather than deep governance or observability.
Strengths
Seamless frontend integration:
Tightly coupled with Vercel Edge Functions and the ai SDK for instant model calls from web apps.
Developer-first experience:
Zero-config setup within existing Vercel deployments.
Streaming support:
Optimized for fast token delivery to improve UX in chat or completion UIs.
Usage analytics:
Basic request-level metrics surfaced through Vercel’s dashboard.
Limitations
Limited controls for backend orchestration, governance, or cross-team visibility.
Strongest support for OpenAI; limited cross-provider routing and observability.
Lacks advanced features like guardrails, policy enforcement, or cost allocation by workspace.
Not ideal for enterprise or compliance-heavy environments.
Pricing Structure
Included within Vercel’s platform plans. The gateway has a free tier with request limits and usage-based pricing beyond that, tied to overall Vercel bandwidth and function execution.
Ideal For / Typical Users
Frontend developers and product teams deploying AI-powered web applications that need fast, cached inference with minimal infrastructure overhead.
Widely praised for simplicity and speed; some users note limited visibility into backend performance and costs.
TrueFoundry AI Gateway
TrueFoundry is an MLOps platform that helps teams deploy, monitor, and manage machine learning models and LLM-based applications.
Its AI Gateway component is part of a broader MLOps suite, focused on infrastructure automation, model deployment, and workflow management rather than pure multi-provider orchestration.
The gateway is positioned for teams that already have ML pipelines and need a controlled way to serve LLMs within those workflows.
Strengths
End-to-end MLOps integration:
Tightly connected with model deployment, experiment tracking, and model registry features.
Strong internal infrastructure controls:
Autoscaling, rollout strategies, and Kubernetes-native deployments for teams that prefer infrastructure ownership.
Custom model hosting:
Supports deploying fine-tuned or proprietary LLMs alongside hosted providers.
Monitoring and alerts:
Metrics for model performance, resource usage, and API health within the same interface.
Developer workflows:
CI/CD pipelines, environment promotions, and reproducible deployment templates.
Limitations
Primarily built for ML/MLOps teams, not general application developers.
Less focus on cross-provider LLM routing compared to modern AI gateways.
Lacks granular guardrails, policy governance, and cost controls expected in enterprise AI gateways.
Requires infrastructure management (Kubernetes, cloud resources), which may add overhead for teams wanting a hosted solution.
Observability is more ML-focused rather than request-level LLM observability.
Pricing Structure
No broad public free tier; pricing is generally provided on request.
Ideal For / Typical Users
ML platform teams, data science groups, and enterprises building custom model pipelines who want model hosting and serving integrated with their DevOps/MLOps workflows.
Appreciated for its deployment automation and developer workflows; some users note a steeper learning curve around infrastructure setup.
Kong AI Gateway
Kong is a widely adopted API gateway and service connectivity platform used by engineering teams to manage API traffic at scale. Its AI Gateway capabilities are delivered through plugins and extensions built on top of Kong Gateway and Kong Mesh.
Strengths
Robust API management foundation:
Industry-standard rate limiting, authentication, transformations, and traffic policies.
Plugin ecosystem:
AI-related plugins support routing to LLM providers, applying policies, and transforming requests.
Security controls:
Integrates with WAFs, OAuth providers, RBAC, and audit frameworks.
Environment flexibility:
Deployable as OSS, enterprise self-hosted, or cloud-managed via Kong Konnect.
Limitations
AI capabilities are extensions, not core design — lacks deep observability and cost intelligence for LLM workloads
Limited multi-provider LLM orchestration, routing logic, or model-aware optimizations (latency-based routing, canaries, cost-aware fallback).
No built-in LLM guardrails, jailbreak detection, evaluations, or policy enforcement beyond generic API rules.
Requires configuration and DevOps involvement; not optimized for rapid GenAI iteration.
Governance and monitoring are API-centric, not request/token-centric like modern AI gateways.
Pricing Structure
AI gateway is a part of their API gateway offering.
Ideal For / Typical Users
Enterprises already using Kong as their API gateway or service mesh who want to extend existing infrastructure to support basic LLM routing, without adding a new platform.
Praised for reliability and API governance; not typically evaluated for AI-specific workloads.
Solo.io Gloo Gateway
Gloo Gateway is a cloud-native API gateway built on Envoy to secure, observe, and control AI applications. While not AI-native, Solo has introduced AI traffic policies and LLM-aware routing extensions built on top of Gloo’s existing API and mesh infrastructure.
Strengths
Service-mesh native:
Deep integration with Istio, Envoy, and Kubernetes, making it a natural fit for cloud-native platforms.
Policy-driven routing:
Supports transformations, throttling, authentication, and custom routing logic applicable to LLM endpoints.
High performance at scale:
Optimized for large microservices architectures with low-latency routing.
Security features:
mTLS, OPA/OPA-Gatekeeper policies, JWT validation, and audit controls.
Limitations
AI support is not core — AI routing is layered on top of a general-purpose API gateway.
No model-level observability, token analytics, or LLM-specific cost tracking.
Lacks guardrails, redaction, moderation, or jailbreak detection capabilities expected in an AI gateway.
Requires Kubernetes, Helm, Istio, and other infra planning — heavy for AI teams wanting a simple managed gateway.
Limited multi-provider orchestration or automated failover between LLM providers.
Pricing Structure
Pricing is typically custom-quoted for mid-size and enterprise customers.
Ideal For / Typical Users
Platform engineering teams and enterprises running Kubernetes, Istio, or Envoy-based service meshes that want to add light AI routing on top of existing traffic infrastructure.
Respected for microservices and API performance; reviewers do not focus on AI workloads specifically.
F5 AI Gateway
F5’s AI Gateway builds on F5’s long-standing expertise in application delivery, traffic management, and enterprise security. Its value lies in bringing AI request flows under the same operational umbrella as existing enterprise apps and APIs.
Strengths
Enterprise-grade traffic control:
Load balancing, health checks, and availability controls across AI endpoints.
Security-first approach:
Integrates with F5 WAF, bot protection, TLS termination, and API security layers.
High performance under load:
Built for large-scale, low-latency environments with demanding throughput needs.
Hybrid and on-prem support:
Suitable for enterprises with datacenter or private cloud requirements.
Policy consistency:
AI calls can reuse existing security, authentication, and rate-limiting policies across apps.
Limitations
AI capabilities are extensions of traffic routing; lacks model-aware routing logic (latency/cost-based selection, fallbacks).
No built-in LLM observability, token analytics, cost tracking, or evaluation capabilities.
No guardrails such as redaction, jailbreak detection, or content moderation beyond generic API filtering.
Requires infrastructure ownership — BIG-IP appliances, NGINX deployments, or enterprise controllers.
Not ideal for rapid GenAI iteration or multi-provider experimentation.
Pricing Structure
AI-related extensions typically require premium modules or add-ons. Pricing is not public and is usually enterprise-negotiated.
Ideal For / Typical Users
Large enterprises with existing F5 or NGINX infrastructure that want to bring AI traffic into the same security, routing, and availability framework without introducing a new platform.
Strong marks for reliability and security; reviewers generally do not evaluate AI-specific functionality.
IBM AI Gateway
IBM’s AI Gateway sits within the broader watsonx ecosystem, designed to give enterprises a governed, auditable, and policy-driven interface for accessing foundation models.
Strengths
Strong governance foundations:
Integrates with watsonx.governance for explainability, lineage, approvals, and risk tracking.
Enterprise IAM integration:
Works with LDAP, Active Directory, IAM roles, SSO, and audit systems.
Security and compliance posture:
Built for industries requiring strict oversight (finance, healthcare, public sector).
Granular access controls:
Role-based permissions, workspace separation, project-level isolation, and audit trails.
Model hosting options:
Supports IBM’s own foundation models, open-source models, and integrations with selected third-party providers.
Limitations
Limited multi-provider orchestration; strongest support is within the IBM model catalog.
Observability is governance- and risk-focused, not designed for token-level debugging or latency analytics.
Lacks built-in performance-focused routing, caching, or cost-aware decisioning.
Few native LLM guardrails such as jailbreak detection, dynamic redaction, or tool governance.
Heavier onboarding and configuration — requires alignment with the larger IBM ecosystem.
Pricing Structure
Typically sold via annual enterprise contracts, with pricing varying based on model usage, governance requirements, and deployment size.
Ideal For / Typical Users
Enterprises prioritizing compliance, auditability, and controlled AI deployment, especially those already using IBM’s governance tools or operating in regulated industries.
Recognized for governance strength; reviewers note slower iteration speed and ecosystem lock-in compared to agile developer-first platforms.
Azure AI Gateway
Azure AI Gateway is part of Microsoft’s Azure AI platform, providing a unified access layer to Azure OpenAI models, the Azure Model Catalog, and enterprise-managed deployments.
Strengths
Deep Azure ecosystem integration:
Works seamlessly with Azure OpenAI, Azure AI Foundry, Azure AD, Key Vault, and Azure Monitor.
Centralized access layer:
Unified routing to foundation models hosted within Azure’s model catalog.
Enterprise-grade security:
Native support for Azure AD, managed identities, RBAC, network isolation, and keyless authentication.
Telemetry and diagnostics:
Integrates with Azure Monitor and Application Insights for request metrics, latency traces, and logging.
Governance aligned with Microsoft tooling:
Compliance, audit logs, and workspace-level access management built into the ecosystem.
Limitations
Primarily optimized for Azure OpenAI and Azure-hosted models — limited support for multi-cloud or multi-provider orchestration.
No latency- or cost-based multi-model routing outside the Azure ecosystem.
Limited prompt management, versioning, or environment workflows compared to AI-native gateways.
Not ideal for teams wanting flexibility across Anthropic, Mistral, Google Gemini, Hugging Face, and other providers.
Pricing Structure
The gateway itself does not have a standalone billing model; costs derive from Azure service consumption.
Ideal For / Typical Users
Teams fully invested in the Microsoft Azure ecosystem that want a governed interface to Azure OpenAI and the Azure model catalog, with enterprise-grade identity and compliance controls.
(Azure AI platform overall) — praised for enterprise readiness; some reviews cite ecosystem lock-in and slower iteration compared to cloud-agnostic developer platforms.
Cequence AI Gateway
Cequence is best known for its API security platform, offering protection against bots, fraud, and malicious API traffic. The Cequence AI Gateway extends this security layer to AI and LLM endpoints, focusing on API defense, traffic inspection, and threat prevention rather than orchestration or model-level intelligence.
Strengths
Advanced API threat detection:
Uses behavioral analytics, anomaly detection, and bot defense to protect AI endpoints from abuse.
Security-first policies:
Rate limiting, schema validation, traffic filtering, and risk scoring applied directly to AI calls.
End-to-end API security suite:
Integrates WAF capabilities, bot management, API discovery, and runtime protection.
Enterprise deployment flexibility:
Supports on-prem, cloud, and hybrid models; integrates with existing API gateways.
Visibility into malicious usage:
Detailed logs of suspicious patterns, overuse, or attacks targeting LLM APIs.
Limitations
Not designed as an LLM orchestration layer — lacks model-aware routing, fallbacks, or cost-based decisioning.
No built-in LLM observability, such as token tracking, latency metrics, or provider-level performance analytics.
Does not provide prompt management, multi-provider SDKs, or unified model access.
No guardrails such as PII redaction, jailbreak detection, or content moderation tuned for LLMs.
Focused on protecting AI endpoints, not accelerating or optimizing their usage.
Pricing Structure
Pricing is usually provided through a custom annual contract.
Ideal For / Typical Users
Security teams and enterprises that need strong API protection and threat prevention for AI endpoints, especially those already using Cequence for broader API security operations.
Recognized for strong API security, bot mitigation, and threat detection; limited mentions of AI-specific workflows.
Custom Gateway Solutions
Some engineering teams choose to build their own AI gateway or proxy layer using open-source components, internal microservices, or cloud primitives.
These “custom gateways” often start as simple proxies for OpenAI or Anthropic calls and gradually evolve into internal platforms handling routing, logging, and key management.
While they offer full control, they require significant ongoing engineering, security, and maintenance investment to keep up with the rapidly expanding LLM ecosystem.
Strengths
Full customization:
Every component can be tailored to internal needs.
Complete control over data flow:
Easy to enforce organization-specific data policies or network isolation.
Deep integration with internal stacks:
Fits seamlessly into proprietary systems, legacy infrastructure, or internal developer platforms.
Potentially lower cost at a very small scale:
If only supporting a single provider or simple routing logic.
Limitations
High engineering burden: implementing and maintaining routing, retries, fallbacks, logging, key rotation, credentials, dashboards, and guardrails is complex.
Difficult to keep up with fast-moving LLM features—function calling, new provider APIs, model updates, safety settings, embeddings, multimodal inputs, etc.
No built-in observability: requires building token tracking, latency metrics, per-user analytics, cost dashboards, and log pipelines.
Security and governance debt: RBAC, workspace separation, policy enforcement, audit logs, and compliance controls require significant effort.
Reliability challenges: implementing circuit breakers, provider failover, shadow testing, canary routing, and load balancing is nontrivial.
Higher opportunity cost — engineers spend time maintaining infrastructure rather than building AI products.
Pricing Structure
No fixed pricing — the cost is measured in engineering hours, cloud resources, and operational overhead. Over time, most teams report that maintaining custom gateways costs more than adopting a purpose-built platform, especially once governance, observability, and multi-provider support become requirements.
Ideal For / Typical Users
Highly specialized engineering teams with unique compliance or architectural constraints that cannot be met by commercial platforms, and who have the bandwidth to maintain internal infrastructure.
Key Capabilities of AI Gateways
Unified Endpoint
Connect to any LLM via a single API.
Multi-Model Routing
Dynamically route requests to OpenAI, Anthropic, Mistral, etc.
Caching & Rate Limiting
Optimize latency and cost.
Key Management
Securely store and rotate provider keys.
Observability:
Track token usage, latency, and performance.
Governance & Guardrails:
Enforce content policies and access control.
Why Portkey is different
Gloo Gateway is a cloud-native API gateway built on Envoy to secure, observe, and control AI applications. While not AI-native, Solo has introduced AI traffic policies and LLM-aware routing extensions built on top of Gloo’s existing API and mesh infrastructure.
Governance at scale
Built for enterprise control from day one
Workspaces and role-based access
Budgets, rate limits, and quotas
Data residency controls
SSO, SCIM, audit logs
Policy-as-code for consistent enforcement
HIPAA
COMPLIANT
GDPR
MCP-native capabilities
Portkey is the first AI gateway designed for MCP at scale. It provides:
MCP server registry
Tool and capability discovery
Governance over tool execution
Observability for tool calls and context loads
Unified routing for both model calls and tool invocations
Comprehensive visibility into every request
Token and cost analytics
Latency traces
Transformed logs for debugging
Workspace, team, and model-level insights
Error clustering and performance trends
Unified SDKs and APIs
A single interface for 1,600+ LLMs and embeddings across OpenAI, Anthropic, Mistral, Gemini, Cohere, Bedrock, Azure, and local deployments.
Guardrails and safety
PII redaction
Jailbreak detection
Toxicity and safety filters
Request and response policy checks
Moderation pipelines for agentic workflows
Prompt and context management
Template versioning, variable substitution, environment promotion, and approval flows to maintain clean, reproducible prompt pipelines.
Portkey unifies everything teams need to build, scale, and govern GenAI systems — with the reliability and control that production demands
Reliability automation
Sophisticated failover and
routing built into the gateway:
MCP server registry
Fallbacks and retries
Canary and A/B routing
Latency and cost-based selection
Provider health checks
Circuit breakers and dynamic throttling
Architecture overview
Portkey sits at the center of the GenAI stack as the control plane that every model call and tool interaction passes through. When an application, agent, or backend service sends a request, it first reaches Portkey’s gateway layer. This is where authentication, access rules, key management, routing logic, caching, rate limits, guardrails, and reliability controls are applied. Instead of each application implementing its own logic, Portkey standardizes this behavior across the entire organization.
From the gateway, the request is routed to the appropriate model provider or internal model. Portkey supports the full ecosystem — OpenAI, Anthropic, Mistral, Google Gemini, Cohere, Hugging Face, Bedrock, Azure OpenAI, Ollama, and custom enterprise-hosted LLMs. Provider differences, credentials, model versions, and health checks are all abstracted behind a single consistent interface.
Portkey also routes MCP tool calls. MCP servers are registered and discovered through Portkey, allowing agents to call external tools with the same governance, access controls, and observability as model requests.
Tool executions, context loads, errors, and policies are all enforced within the same control plane. Every interaction—whether a model call or a tool invocation—is captured by Portkey’s observability layer. Logs, transformed traces, latency breakdowns, token and cost analytics, error patterns, and replay data flow into a unified telemetry stream. This gives teams a real-time understanding of system behavior, performance, and spend without instrumenting anything manually.
All of this rolls up into Portkey’s governance layer, where organizations manage workspaces, roles, budgets, rate limits, data residency, audit trails, and policy-as-code. This is where compliance and operational controls are defined and consistently enforced across teams and applications.
Together, these layers form a single architecture: applications send requests, Portkey governs and routes them, providers execute them, and all telemetry flows back into a governed control plane. It replaces the fragmented mix of proxies, scripts, dashboards, and ad-hoc control mechanisms with one unified platform built for production-grade GenAI.
Use Cases
Portkey is designed to support a wide range of GenAI applications, from early prototypes evolving into production systems to large-scale enterprise deployments.
Teams building AI copilots, agents, and workflows use Portkey as the backbone that keeps every model call and tool invocation reliable, governed, and traceable as projects grow from prototype to production.
As applications span multiple models, providers, and tools, Portkey ensures consistent behavior across all workflows without requiring developers to rewrite internal logic or maintain provider-specific complexity.
Enterprise AI teams use Portkey to centralize access to models and tools, replacing scattered provider keys, credentials, and policies with a single control plane that enforces budgets, quotas, permissions, and compliance rules.
Organizations with many teams and departments rely on Portkey to standardize AI access, making onboarding easier and ensuring that usage, safety, and governance requirements are applied uniformly.
Product and platform engineering teams use Portkey to move from experimentation to stable production, with clear visibility into latency, costs, token usage, and model behavior—without building internal dashboards or handling vendor fragmentation.
Companies using both hosted and internally deployed models use Portkey to unify provider and self-hosted LLM access behind the same gateway, making routing, governance, and observability consistent across all inference sources.
Integrations
Portkey connects to the full GenAI ecosystem through a unified control plane. Every integration works through the same consistent gateway. This gives teams one place to manage routing, governance, cost controls, and observability across their entire AI stack.
Portkey supports integrations with all major LLM providers, including OpenAI, Anthropic, Mistral, Google Gemini, Cohere, Hugging Face, AWS Bedrock, Azure OpenAI, and many more. These connections cover text, vision, embeddings, streaming, and function calling, and extend to open-source and locally hosted models.
Beyond models, Portkey integrates directly with the major cloud AI platforms. Teams running on AWS, Azure, or Google Cloud can route requests to managed model endpoints, regional deployments, private VPC environments, or enterprise-hosted LLMs—all behind the same Portkey endpoint.
Integrations with systems like Palo Alto Networks Prisma AIRS, Patronus, and other content-safety and compliance engines allow organizations to enforce redaction, filtering, jailbreak detection, and safety policies directly at the gateway level. These controls apply consistently across every model, provider, app, and tool.
Frameworks such as LangChain, LangGraph, CrewAI, OpenAI Agents SDK, etc. route all of their model calls and tool interactions through Portkey, ensuring agents inherit the same routing, guardrails, governance, retries, and cost controls as core applications.
Portkey integrates with vector stores and retrieval infrastructure, including platforms like Pinecone, Weaviate, Chroma, LanceDB, etc. This allows teams to unify their retrieval pipelines with the same policy and governance layer used for LLM calls, simplifying both RAG and hybrid search flows.
Tools such as Claude Code, Cursor, LibreChat, and OpenWebUI can send inference requests through Portkey, giving organizations full visibility into token usage, latency, cost, and user activity, even when these apps run on local machines.
For teams needing deep visibility, Portkey integrates with monitoring and tracing systems like Arize Phoenix, FutureAGI, Pydantic Logfire and more. These systems ingest Portkey’s standardized telemetry, allowing organizations to correlate model performance with application behavior.
Finally, Portkey connects with all major MCP clients, including Claude Desktop, Claude Code, Cursor, VS Code extensions, and any MCP-capable IDE or agent runtime.
Across all of these categories, Portkey acts as the unifying operational layer. It replaces a fragmented integration landscape with a single, governed, observable, and reliable control plane for the entire GenAI ecosystem.
Get started
Portkey gives teams a single control plane to build, scale, and govern GenAI applications in production with multi-provider support, built-in safety and governance, and end-to-end visibility from day one.
